This document presents a study that uses gene expression programming (GEP) to develop a model for predicting sediment transport in sewers under no-deposition conditions. The study first reviews existing sediment transport equations developed using dimensional analysis and semi-experimental methods. It then describes using GEP to present six different models considering effective parameters on sediment transport. The best model is selected by comparing their performance on validation data not used in model development. The results show the GEP model achieves a root mean squared error of 0.12 and mean average percentage error of 2.56 on the training data, and 0.14 and 2.82 respectively on the validation data. This performance is compared to existing sediment transport equations.
Moisture penetration causes many direct and indirect distresses in flexible asphalt pavement. Due to damage in asphalt concrete and binder by moisture are the prime concern of failure for flexible pavement worldwide. The causes and prediction are investigated in this study. The asphalt binder was modified with carbon nanotubes (CNT) with very small percentages. The modified binder was simulated with moisture damage with AASHTO T-283 methods. In this study, polymer and carbon nanotubes (CNT) have been added to liquid asphalt binder to examine whether the resulting modified binder has improved moisture damage resistance. Using laboratory tested data, an artificial intelligence modeling technique has been utilized to determine the moisture damage behavior of the modified binder. Multi-Layer Perceptron (MLP) provides the best prediction for wet and dry samples AFM readings with R2 values respectively 0.6407 and 0.8371.
Today, retrofitting of the old structures is important. For this purpose, determination of capacities for these buildings, which mostly are non-ductile, is a very useful tool. In this context, non-ductile RC joint in concrete structures, as one of the most important elements in these buildings are considered, and the shear capacity, especially for retrofitting goals can be very beneficial. In this paper, three famous soft computing methods including artificial neural networks (ANN), adaptive neuro-fuzzy inference system (ANFIS) and also group method of data handling (GMDH) were used to estimating the shear capacity for this type of RC joints. A set of experimental data which were a failure in joint are collected, and first, the effective parameters were identified. Based on these parameters, predictive models are presented in detail and compare with each other. The results showed that the considered soft computing techniques are very good capabilities to determine the shear capacity.
Efficiency of vertical drains using finite element method may 2017Dr Mazin Alhamrany
Incorporating one-dimensional bar elements with two-dimensional quadrilateral axisymmetrical elements to tackle problems of consolidation of clay with vertical drains.
Structural evaluation of low volume road pavements using pavement dynamic con...eSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Structural evaluation of low volume road pavements using pavement dynamic con...eSAT Journals
Abstract
Static and dynamic cone penetration tests are widely used in Foundation engineering for measuring the penetration resistance of the
ground and for relating it to the degree of compaction and safe bearing capacity of soils. In Highway Engineering, Pavement
Dynamic Cone Penetrometer (DCP) is used for rapid in-situ strength evaluation of subgrade and other unbound pavement layers. In
the present studies, an attempt has been made to identify the strength and thickness of different pavement layers of newly constructed
low volume roads in the State of Karnataka, India using dynamic cone penetration studies and was compared with actual
measurements at the site. The evaluation of pavement test stretches was made for a period of two years, and changes in penetration
resistance of different pavement layers were measured. A Software was used to analyze the DCP data and to correlate with field
observation. The results have favoured the possibility of using dynamic cone penetrometer as a quality control and pavement
monitoring tool for low volume roads, eliminating the need for a Benkelman beam or a falling weight deflectometer.
Keywords: Pavement Dynamic Cone Penetrometer, CBR, Subgrade, Rural roads
HVOF Sprayed WC-Cocr Coating on Mild Steel: Microstructure and Wear Evaluationiosrjce
IOSR Journal of Applied Physics (IOSR-JAP) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of physics and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in applied physics. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Modelling study of jet metal interaction in ld processeSAT Journals
Abstract Water model experiments have been carried out in a 1/30th scaled down model of the 100 ton LD converter in order to investigate the effect of changing the lance height and the gas flow rate on the penetration depth of liquid with different exit diameters. It is found the penetration depth increases with decreasing nozzle diameter, decreasing the lance height and with increase the gas flow rate. Gas jets impinging onto a gas–liquid interface of a liquid pool are also studied using computational fluid dynamics modeling, which aims to obtain a better understanding of the behavior of the gas jets. The gas and liquid flows are modeled using the volume of fluid technique. The governing equations in the axisymmetric cylindrical coordinates are solved by the CFD simulation using FLUENT. The computed results are compared with experimental result and it isfound a good match with all the data. Keywords: LD process, Water Modeling, Penetration Depth, Volume of Fluid, CFD.
Moisture penetration causes many direct and indirect distresses in flexible asphalt pavement. Due to damage in asphalt concrete and binder by moisture are the prime concern of failure for flexible pavement worldwide. The causes and prediction are investigated in this study. The asphalt binder was modified with carbon nanotubes (CNT) with very small percentages. The modified binder was simulated with moisture damage with AASHTO T-283 methods. In this study, polymer and carbon nanotubes (CNT) have been added to liquid asphalt binder to examine whether the resulting modified binder has improved moisture damage resistance. Using laboratory tested data, an artificial intelligence modeling technique has been utilized to determine the moisture damage behavior of the modified binder. Multi-Layer Perceptron (MLP) provides the best prediction for wet and dry samples AFM readings with R2 values respectively 0.6407 and 0.8371.
Today, retrofitting of the old structures is important. For this purpose, determination of capacities for these buildings, which mostly are non-ductile, is a very useful tool. In this context, non-ductile RC joint in concrete structures, as one of the most important elements in these buildings are considered, and the shear capacity, especially for retrofitting goals can be very beneficial. In this paper, three famous soft computing methods including artificial neural networks (ANN), adaptive neuro-fuzzy inference system (ANFIS) and also group method of data handling (GMDH) were used to estimating the shear capacity for this type of RC joints. A set of experimental data which were a failure in joint are collected, and first, the effective parameters were identified. Based on these parameters, predictive models are presented in detail and compare with each other. The results showed that the considered soft computing techniques are very good capabilities to determine the shear capacity.
Efficiency of vertical drains using finite element method may 2017Dr Mazin Alhamrany
Incorporating one-dimensional bar elements with two-dimensional quadrilateral axisymmetrical elements to tackle problems of consolidation of clay with vertical drains.
Structural evaluation of low volume road pavements using pavement dynamic con...eSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Structural evaluation of low volume road pavements using pavement dynamic con...eSAT Journals
Abstract
Static and dynamic cone penetration tests are widely used in Foundation engineering for measuring the penetration resistance of the
ground and for relating it to the degree of compaction and safe bearing capacity of soils. In Highway Engineering, Pavement
Dynamic Cone Penetrometer (DCP) is used for rapid in-situ strength evaluation of subgrade and other unbound pavement layers. In
the present studies, an attempt has been made to identify the strength and thickness of different pavement layers of newly constructed
low volume roads in the State of Karnataka, India using dynamic cone penetration studies and was compared with actual
measurements at the site. The evaluation of pavement test stretches was made for a period of two years, and changes in penetration
resistance of different pavement layers were measured. A Software was used to analyze the DCP data and to correlate with field
observation. The results have favoured the possibility of using dynamic cone penetrometer as a quality control and pavement
monitoring tool for low volume roads, eliminating the need for a Benkelman beam or a falling weight deflectometer.
Keywords: Pavement Dynamic Cone Penetrometer, CBR, Subgrade, Rural roads
HVOF Sprayed WC-Cocr Coating on Mild Steel: Microstructure and Wear Evaluationiosrjce
IOSR Journal of Applied Physics (IOSR-JAP) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of physics and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in applied physics. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Modelling study of jet metal interaction in ld processeSAT Journals
Abstract Water model experiments have been carried out in a 1/30th scaled down model of the 100 ton LD converter in order to investigate the effect of changing the lance height and the gas flow rate on the penetration depth of liquid with different exit diameters. It is found the penetration depth increases with decreasing nozzle diameter, decreasing the lance height and with increase the gas flow rate. Gas jets impinging onto a gas–liquid interface of a liquid pool are also studied using computational fluid dynamics modeling, which aims to obtain a better understanding of the behavior of the gas jets. The gas and liquid flows are modeled using the volume of fluid technique. The governing equations in the axisymmetric cylindrical coordinates are solved by the CFD simulation using FLUENT. The computed results are compared with experimental result and it isfound a good match with all the data. Keywords: LD process, Water Modeling, Penetration Depth, Volume of Fluid, CFD.
Predicting Resilient Modulus of Clayey Subgrade Soils by Means of Cone Penetr...Pouyan Fakharian
Resilient modulus (Mr) of subgrade soils is considered as one of the most important factors for designing flexible pavements using empirical methods as well as mechanistic-empirical methods. The resilient modulus is commonly measured by a dynamic triaxial loading test, which is complex and expensive. In this research, back-propagation artificial neural network method has been employed to model the resilient modulus of clayey subgrade soils based on the results of the cone penetration test. The prediction of the resilient modulus of clayey subgrade soil can be possible through the developed neural network based on the parameters of the cone tip resistance (qc), sleeve friction (fs), moisture content (w), and dry density (γd). The results of the present study show that the coefficients of determination (R2) for training and testing sets are 0.9837 and 0.9757, respectively. According to the sensitivity analysis results, the moisture content is the least important parameter to predict the resilient modulus of clayey subgrade soils, while the importance of other parameters is almost the same. In this study, the effect of different parameters on the resilient modulus of clayey subgrade soil was evaluated using parametric analysis and it was found that with increasing the cone tip resistance (qc), the sleeve friction (fs) and the dry density (γd) and also with decreasing the moisture content (w) of soils, the resilient modulus of clayey subgrade soils increases.
Improving the Properties of Self-compacted Concrete with Using Combined Silic...Pouyan Fakharian
The viscosity is the main property of self- compacted concrete (SCC) and using of pozzolan material such as metakaolin (MK) and Silica fume (SF) can help to achieve that goal. The effect of simultaneous substitution of MK and SF instead of cement on the rheological and mechanical properties of self-compacted concrete was experimentally investigated in this paper. Seventeen mix designs were cast with a substitution weight percentage (5, 10, 15, 20 %) in water to adhesive material ratio equal 0.32. All mixes were examined by compressive, tensile strengths and water absorption tests with an appropriate fluidity, without having signs of segregation or instability. The test results were indicated that the SCC mixes containing MK and SF had higher compressive and tensile strengths in comparison with no-pozzolan concrete. The comparison of linear multiple regression techniques (LMRT) and nonlinear multiple regression technique outputs with experimental results showed an appropriate similarity.
A new proposed approach for moment capacity estimation of ferrocement members...Pouyan Fakharian
Ferrocement composites are widely used as a novel method for many different structural purposes recently. The uniform distribution and the high surface area-to-volume ratio of the reinforcement of such composites would improve the crack-arresting mechanism. Given these properties, ferrocement is an ideal option as a replacement for some traditional structures methods. In members with axially loaded reinforced concrete ferrocement composite, it would be the best alternative to use ferrocement members. Lack of sufficient research in this approach is the cause of not well defining this field for RC structures. This study has aimed to evaluate the moment capacity of ferrocement members using the GMDH method. Mechanical and geometrical parameters including the width of specimens, total depth specimens, compressive strength of ferrocement, ultimate strength of wire mesh and volume fraction of wire mesh are considered as inputs to predict the moment capacity of ferrocement members. For evaluating this model, mean absolute error (MAE), root mean absolute error (RMAE), normalized root mean square error (NRMSE) and mean absolute percentage error (MAPE) were carried out. The results conducted that the GMDH model is significantly better than some previous models and comparable to some other methods. Moreover, a new formulation for moment capacity of ferrocement members based on GMDH approach is presented. Finally, Sensitivity analysis is operated to understand the influence of each input parameters on moment capacity of ferrocement members.
Shear Strength Prediction of Reinforced Concrete Shear Wall Using ANN, GMDH-N...Pouyan Fakharian
To provide lateral resistance in structures as well as buildings, there are some types of structural systems such as shear walls. The utilization of lateral loads occurs on a plate on the wall's vertical dimension. Conventionally, these sorts of loads are transferred to the wall collectors. There is a significant resistance between concrete shear walls and lateral seismic loading. To guarantee the building's seismic security, the shear strength of the walls has to be prognosticated by using models. This paper aims to predict shear strength by using Artificial Neural Network (ANN), Neural Network-Based Group Method of Data Handling (GMDH-NN), and Gene Expression Programming (GEP). The concrete's compressive strength, the yield strength of transverse reinforcement, the yield strength of vertical reinforcement, the axial load, the aspect ratio of the dimensions, the wall length, the thickness of the reinforced concrete shear wall, the transverse reinforcement ratio, and the vertical reinforcement ratio are the input parameters for the neural network model. And the shear strength of the reinforced concrete shear wall is considered as the target parameter of the ANN model. The results validate the capability of the models predicted by ANN, GMDH-NN, and GEP, which are suitable for use as a tool for predicting the shear strength of concrete shear walls with high accuracy.
Applications of FEM in Geotechnical Engineering / State-of-the-ArtDr Mazin Alhamrany
This presentation supposed to be given during the 1st Iraqi International Conference on Geotechnical Engineering (ICGE) - Baghdad - 17-19 February 2020. I am uploading this document on LinkedIn as a contribution providing geotechnical engineers an insight for the earlier, recent and "potential" future applications of FEM in the field of Geotechnical Engineering. With my best wishes to the Iraqi Geotechnical Society.
The impact of the diameter to height ratio on the compressibility parameters ...eSAT Journals
Abstract Compressibility parameters of fine-grained soils are mainly influenced by soil mineralogy, moisture content and soil diameter to height ratio (D/H). The British and American standards suggested that to obtain accurate engineering properties; it is necessary to use D/H ratio of 4 and 2.5 respectively to eliminate friction between the soil and the structure. In the current study, various D/H ratios were adopted ranging from 0.5 to 11. The D/H ratios effect on some compressibility parameters such as coefficient of consolidation (cv), compression index (cc) and coefficient of volume compressibility (mv) were analysed. Additionally, the impact of the D/H ratio on the acquire cv values were also presented where three methods were used namely: Casagrande, Taylor and Inflection method. The scaling effects based on cv ratio [cv (√t) / cv (log t)] from Oedometer tests using different D/H ratios are also presented. The results showed that Taylor’s method is the most appropriate way to achieve an accurate cv and an increase in pressure leads to a reduction in cc and a gradual decrease in mv. The validation of the experimental results on a finite element software package PLAXIS was completed. Keywords: Compressibility, D/H ratio, Fine-grained soil, Friction
International Journal of Engineering Research and DevelopmentIJERD Editor
Electrical, Electronics and Computer Engineering,
Information Engineering and Technology,
Mechanical, Industrial and Manufacturing Engineering,
Automation and Mechatronics Engineering,
Material and Chemical Engineering,
Civil and Architecture Engineering,
Biotechnology and Bio Engineering,
Environmental Engineering,
Petroleum and Mining Engineering,
Marine and Agriculture engineering,
Aerospace Engineering.
Asme2009 82287 - Porous Media - Forced Convection FlowHIIO
In this study the flow field and heat transfer properties of a
steady, two-dimensional flow field in a porous domain between
two parallel plates is investigated numerically by using a
discretized numeric code. Analysis has been carried for
Reynolds number based on particle sizes ranging from 60 to
1000. Numerical results are compared with different numerical
methods used for predicting this kind of flow. Results are
obtained for different regime, various p Re numbers and the
effect of Particles size is also investigated. Solutions indicate
that by increasing the
p Re , the flow in the porous media
remains laminar where the flow has turbulence characteristics
for p Re <50. Moreover, by increasing p Re , the value of
average Nusselt number increases. Also, reducing the particle
size affects the Nusselt number and it increases while the
porosity remains the same.
An Investigation of the Interlayer Adhesion Strength in Deeper Layers of the ...AM Publications
A road pavement structure is typically composed of different layers arranged one on top of the other, all supported by a natural or improved subgrade. The main purpose of this configuration is to provide the most effective structure with adequate potential to spread traffic loading from the surface to the subgrade with minimum damage. In spite of material properties and construction techniques, researchers have shown that the overall pavement performance is significantly influenced by the interlayer adhesion condition throughout the pavement structure ([8], [11], [21], [22]). Lack of intimate contact between layers results in them acting as individuals rather than as a thick bonded unit. This therefore induces overloading of layers which leads to premature deterioration of the entire structure due to traffic induced distresses. This work, therefore investigated the interlayer adhesion characteristics between the granular base and the lightly cemented subbase of a typical South African pavement structure. The influence of bonding condition on the overall pavement performance was also studied. A series of interlayer direct shear tests was run on 300 x 300 mm samples made of two layers: a 100mm G2 Granular Base (GB) compacted on top of the 100mm Cement Treated Subbase (CTSB) composed of a G5 material stabilised with 1.8% of cement. Effects of the CTSB scarification, normal pressure and moisture conditions were analysed whereby the results of the interlayer strength tests were compared with those of intra-layer strength tests. The comparative analysis showed that scarifying the CTSB before laying the GB enhances intimate contact between two layers and stimulates the unison interaction which, according to structural modelling results, improves the overall pavement performance.
Open channel flow velocity profiles for different reynolds numbers and roughn...eSAT Journals
Abstract A series of laboratory tests were carried out to understand the extent of effect of roughness and Reynolds number on mean velocity in both outer and inner scaling. To this end, four different types of bed surface conditions (impermeable smooth bed, impermeable rough bed, permeable sand bed and impermeable distributed roughness) and two different Reynolds number (Reh = 47,500 and 31,000) were adopted in the study. Sand particles of median diameter of 2.46 mm were used to create the roughness. The results show that the mean velocities collapsed well for different Reynolds number and for all different bed surfaces. The maximum velocity for all flow conditions were observed below some distances from the free surface. The location of maximum velocity is seen to be dependent on both of roughness and Reynolds number. The smooth bed test data agrees well with the standard log law and collapses well in viscous sub layer and overlap region. The extent of collapses is found to be dependent on Reynolds number. Friction coefficient is noted to be dependent on both the Reynolds number and roughness. Key Words: Open channel flow, Reynolds number, Roughness, mean velocity, friction coefficient, log law
STRESSES BELOW EXISTING STRUCTURES DURING TUNNEL EXCAVATION USING TUNNEL BORI...ijiert bestjournal
The Finite element (FE) analysis include the response of structures t o horizontal & vertical dynamic forces and consider all site characteristics,such as soils and geologic conditions. The induced stresses under the foundation of adjacent buildings during newly constructed underground tunnel through TBM,were investigated in this study. Results of this study were examined to find out whether the amount of variations in forces and stresses are in the allowable ranges or not. In this paper,soil parameters used for the study are based on the existing Delhi Metro tunnel site. Using these soils parameters,tunnel excavation through TBM has been modelled in PLAXIS Tunnel - 3D and the adjacent structures have also been included in the model.
C'est avec plaisir que nous partageons la présentation de M. Masood Meidani de l'Université McGill, lauréat du concours Branko Ladanyi pour ses travaux sur l'effort axial dans les conduites enfouies.
Il remporte une bourse qui lui permettra d’assister à la 71e Conférence Canadienne de Géotechnique qui se tiendra à Edmonton du 23 au 26 septembre 2018 (http://www.geoedmonton2018.ca).
Ce prix a été nommé en l'honneur de M. Branko Ladanyi, Professeur émérite à l'École Polytechnique de Montréal. Durant sa longue et fructueuse carrière, le Professeur Ladanyi a enseigné la géotechnique et mené des travaux de recherche originaux sur une variété de sujets, incluant le comportement des sables, des argiles et des roches, et le dimensionnement des fondations superficielles et profondes. Il est à l’origine de nombreuses contributions scientifiques et techniques marquantes qui sont présentées à travers plus de 200 publications. Il s'est avéré un pionnier dans le domaine de la géotechnique des sols gelés et de l'ingénierie en régions froides. Il est l'auteur, avec O.B. Andersland, du « best-seller » intitulé « An Introduction to Frozen Ground Engineering » (Chapman & Hall, 1994; Second Edition, ASCE Press et John Wiley & Sons, 2003). Ces travaux ont valu au Professeur Ladanyi une grande renommée internationale et de nombreux prix prestigieux. Il est membre de l'Académie canadienne du génie et de l'Académie des sciences de la Société royale du Canada.
Predicting Resilient Modulus of Clayey Subgrade Soils by Means of Cone Penetr...Pouyan Fakharian
Resilient modulus (Mr) of subgrade soils is considered as one of the most important factors for designing flexible pavements using empirical methods as well as mechanistic-empirical methods. The resilient modulus is commonly measured by a dynamic triaxial loading test, which is complex and expensive. In this research, back-propagation artificial neural network method has been employed to model the resilient modulus of clayey subgrade soils based on the results of the cone penetration test. The prediction of the resilient modulus of clayey subgrade soil can be possible through the developed neural network based on the parameters of the cone tip resistance (qc), sleeve friction (fs), moisture content (w), and dry density (γd). The results of the present study show that the coefficients of determination (R2) for training and testing sets are 0.9837 and 0.9757, respectively. According to the sensitivity analysis results, the moisture content is the least important parameter to predict the resilient modulus of clayey subgrade soils, while the importance of other parameters is almost the same. In this study, the effect of different parameters on the resilient modulus of clayey subgrade soil was evaluated using parametric analysis and it was found that with increasing the cone tip resistance (qc), the sleeve friction (fs) and the dry density (γd) and also with decreasing the moisture content (w) of soils, the resilient modulus of clayey subgrade soils increases.
Improving the Properties of Self-compacted Concrete with Using Combined Silic...Pouyan Fakharian
The viscosity is the main property of self- compacted concrete (SCC) and using of pozzolan material such as metakaolin (MK) and Silica fume (SF) can help to achieve that goal. The effect of simultaneous substitution of MK and SF instead of cement on the rheological and mechanical properties of self-compacted concrete was experimentally investigated in this paper. Seventeen mix designs were cast with a substitution weight percentage (5, 10, 15, 20 %) in water to adhesive material ratio equal 0.32. All mixes were examined by compressive, tensile strengths and water absorption tests with an appropriate fluidity, without having signs of segregation or instability. The test results were indicated that the SCC mixes containing MK and SF had higher compressive and tensile strengths in comparison with no-pozzolan concrete. The comparison of linear multiple regression techniques (LMRT) and nonlinear multiple regression technique outputs with experimental results showed an appropriate similarity.
A new proposed approach for moment capacity estimation of ferrocement members...Pouyan Fakharian
Ferrocement composites are widely used as a novel method for many different structural purposes recently. The uniform distribution and the high surface area-to-volume ratio of the reinforcement of such composites would improve the crack-arresting mechanism. Given these properties, ferrocement is an ideal option as a replacement for some traditional structures methods. In members with axially loaded reinforced concrete ferrocement composite, it would be the best alternative to use ferrocement members. Lack of sufficient research in this approach is the cause of not well defining this field for RC structures. This study has aimed to evaluate the moment capacity of ferrocement members using the GMDH method. Mechanical and geometrical parameters including the width of specimens, total depth specimens, compressive strength of ferrocement, ultimate strength of wire mesh and volume fraction of wire mesh are considered as inputs to predict the moment capacity of ferrocement members. For evaluating this model, mean absolute error (MAE), root mean absolute error (RMAE), normalized root mean square error (NRMSE) and mean absolute percentage error (MAPE) were carried out. The results conducted that the GMDH model is significantly better than some previous models and comparable to some other methods. Moreover, a new formulation for moment capacity of ferrocement members based on GMDH approach is presented. Finally, Sensitivity analysis is operated to understand the influence of each input parameters on moment capacity of ferrocement members.
Shear Strength Prediction of Reinforced Concrete Shear Wall Using ANN, GMDH-N...Pouyan Fakharian
To provide lateral resistance in structures as well as buildings, there are some types of structural systems such as shear walls. The utilization of lateral loads occurs on a plate on the wall's vertical dimension. Conventionally, these sorts of loads are transferred to the wall collectors. There is a significant resistance between concrete shear walls and lateral seismic loading. To guarantee the building's seismic security, the shear strength of the walls has to be prognosticated by using models. This paper aims to predict shear strength by using Artificial Neural Network (ANN), Neural Network-Based Group Method of Data Handling (GMDH-NN), and Gene Expression Programming (GEP). The concrete's compressive strength, the yield strength of transverse reinforcement, the yield strength of vertical reinforcement, the axial load, the aspect ratio of the dimensions, the wall length, the thickness of the reinforced concrete shear wall, the transverse reinforcement ratio, and the vertical reinforcement ratio are the input parameters for the neural network model. And the shear strength of the reinforced concrete shear wall is considered as the target parameter of the ANN model. The results validate the capability of the models predicted by ANN, GMDH-NN, and GEP, which are suitable for use as a tool for predicting the shear strength of concrete shear walls with high accuracy.
Applications of FEM in Geotechnical Engineering / State-of-the-ArtDr Mazin Alhamrany
This presentation supposed to be given during the 1st Iraqi International Conference on Geotechnical Engineering (ICGE) - Baghdad - 17-19 February 2020. I am uploading this document on LinkedIn as a contribution providing geotechnical engineers an insight for the earlier, recent and "potential" future applications of FEM in the field of Geotechnical Engineering. With my best wishes to the Iraqi Geotechnical Society.
The impact of the diameter to height ratio on the compressibility parameters ...eSAT Journals
Abstract Compressibility parameters of fine-grained soils are mainly influenced by soil mineralogy, moisture content and soil diameter to height ratio (D/H). The British and American standards suggested that to obtain accurate engineering properties; it is necessary to use D/H ratio of 4 and 2.5 respectively to eliminate friction between the soil and the structure. In the current study, various D/H ratios were adopted ranging from 0.5 to 11. The D/H ratios effect on some compressibility parameters such as coefficient of consolidation (cv), compression index (cc) and coefficient of volume compressibility (mv) were analysed. Additionally, the impact of the D/H ratio on the acquire cv values were also presented where three methods were used namely: Casagrande, Taylor and Inflection method. The scaling effects based on cv ratio [cv (√t) / cv (log t)] from Oedometer tests using different D/H ratios are also presented. The results showed that Taylor’s method is the most appropriate way to achieve an accurate cv and an increase in pressure leads to a reduction in cc and a gradual decrease in mv. The validation of the experimental results on a finite element software package PLAXIS was completed. Keywords: Compressibility, D/H ratio, Fine-grained soil, Friction
International Journal of Engineering Research and DevelopmentIJERD Editor
Electrical, Electronics and Computer Engineering,
Information Engineering and Technology,
Mechanical, Industrial and Manufacturing Engineering,
Automation and Mechatronics Engineering,
Material and Chemical Engineering,
Civil and Architecture Engineering,
Biotechnology and Bio Engineering,
Environmental Engineering,
Petroleum and Mining Engineering,
Marine and Agriculture engineering,
Aerospace Engineering.
Asme2009 82287 - Porous Media - Forced Convection FlowHIIO
In this study the flow field and heat transfer properties of a
steady, two-dimensional flow field in a porous domain between
two parallel plates is investigated numerically by using a
discretized numeric code. Analysis has been carried for
Reynolds number based on particle sizes ranging from 60 to
1000. Numerical results are compared with different numerical
methods used for predicting this kind of flow. Results are
obtained for different regime, various p Re numbers and the
effect of Particles size is also investigated. Solutions indicate
that by increasing the
p Re , the flow in the porous media
remains laminar where the flow has turbulence characteristics
for p Re <50. Moreover, by increasing p Re , the value of
average Nusselt number increases. Also, reducing the particle
size affects the Nusselt number and it increases while the
porosity remains the same.
An Investigation of the Interlayer Adhesion Strength in Deeper Layers of the ...AM Publications
A road pavement structure is typically composed of different layers arranged one on top of the other, all supported by a natural or improved subgrade. The main purpose of this configuration is to provide the most effective structure with adequate potential to spread traffic loading from the surface to the subgrade with minimum damage. In spite of material properties and construction techniques, researchers have shown that the overall pavement performance is significantly influenced by the interlayer adhesion condition throughout the pavement structure ([8], [11], [21], [22]). Lack of intimate contact between layers results in them acting as individuals rather than as a thick bonded unit. This therefore induces overloading of layers which leads to premature deterioration of the entire structure due to traffic induced distresses. This work, therefore investigated the interlayer adhesion characteristics between the granular base and the lightly cemented subbase of a typical South African pavement structure. The influence of bonding condition on the overall pavement performance was also studied. A series of interlayer direct shear tests was run on 300 x 300 mm samples made of two layers: a 100mm G2 Granular Base (GB) compacted on top of the 100mm Cement Treated Subbase (CTSB) composed of a G5 material stabilised with 1.8% of cement. Effects of the CTSB scarification, normal pressure and moisture conditions were analysed whereby the results of the interlayer strength tests were compared with those of intra-layer strength tests. The comparative analysis showed that scarifying the CTSB before laying the GB enhances intimate contact between two layers and stimulates the unison interaction which, according to structural modelling results, improves the overall pavement performance.
Open channel flow velocity profiles for different reynolds numbers and roughn...eSAT Journals
Abstract A series of laboratory tests were carried out to understand the extent of effect of roughness and Reynolds number on mean velocity in both outer and inner scaling. To this end, four different types of bed surface conditions (impermeable smooth bed, impermeable rough bed, permeable sand bed and impermeable distributed roughness) and two different Reynolds number (Reh = 47,500 and 31,000) were adopted in the study. Sand particles of median diameter of 2.46 mm were used to create the roughness. The results show that the mean velocities collapsed well for different Reynolds number and for all different bed surfaces. The maximum velocity for all flow conditions were observed below some distances from the free surface. The location of maximum velocity is seen to be dependent on both of roughness and Reynolds number. The smooth bed test data agrees well with the standard log law and collapses well in viscous sub layer and overlap region. The extent of collapses is found to be dependent on Reynolds number. Friction coefficient is noted to be dependent on both the Reynolds number and roughness. Key Words: Open channel flow, Reynolds number, Roughness, mean velocity, friction coefficient, log law
STRESSES BELOW EXISTING STRUCTURES DURING TUNNEL EXCAVATION USING TUNNEL BORI...ijiert bestjournal
The Finite element (FE) analysis include the response of structures t o horizontal & vertical dynamic forces and consider all site characteristics,such as soils and geologic conditions. The induced stresses under the foundation of adjacent buildings during newly constructed underground tunnel through TBM,were investigated in this study. Results of this study were examined to find out whether the amount of variations in forces and stresses are in the allowable ranges or not. In this paper,soil parameters used for the study are based on the existing Delhi Metro tunnel site. Using these soils parameters,tunnel excavation through TBM has been modelled in PLAXIS Tunnel - 3D and the adjacent structures have also been included in the model.
C'est avec plaisir que nous partageons la présentation de M. Masood Meidani de l'Université McGill, lauréat du concours Branko Ladanyi pour ses travaux sur l'effort axial dans les conduites enfouies.
Il remporte une bourse qui lui permettra d’assister à la 71e Conférence Canadienne de Géotechnique qui se tiendra à Edmonton du 23 au 26 septembre 2018 (http://www.geoedmonton2018.ca).
Ce prix a été nommé en l'honneur de M. Branko Ladanyi, Professeur émérite à l'École Polytechnique de Montréal. Durant sa longue et fructueuse carrière, le Professeur Ladanyi a enseigné la géotechnique et mené des travaux de recherche originaux sur une variété de sujets, incluant le comportement des sables, des argiles et des roches, et le dimensionnement des fondations superficielles et profondes. Il est à l’origine de nombreuses contributions scientifiques et techniques marquantes qui sont présentées à travers plus de 200 publications. Il s'est avéré un pionnier dans le domaine de la géotechnique des sols gelés et de l'ingénierie en régions froides. Il est l'auteur, avec O.B. Andersland, du « best-seller » intitulé « An Introduction to Frozen Ground Engineering » (Chapman & Hall, 1994; Second Edition, ASCE Press et John Wiley & Sons, 2003). Ces travaux ont valu au Professeur Ladanyi une grande renommée internationale et de nombreux prix prestigieux. Il est membre de l'Académie canadienne du génie et de l'Académie des sciences de la Société royale du Canada.
Effect of Height and Surface Roughness of a Broad Crested Weir on the Dischar...RafidAlboresha
Weir is usually incorporated as control or regulation devices in hydraulic systems,
with flow measurement as their secondary. It is normally intended for use in the field and thus
to regulate broad discharges. Broad-Crested weir is among the oldest common weir types. In this
paper, the effect of height and surface roughness for different Board Crested weirs models were
studied on discharge coefficient (Cd) in a horizontal open channel. In the crest of the weir,
certain materials may be combined with concrete (e.g., boulders) or may be used as cladding to
minimize the effect of water overflow (e.g. stone). The weir surface should not be considered
smooth in this case, and the discharge coefficient (Cd) must be re-estimated. For these purposes, laboratory flume was used to study the effect of height and surface roughness on the discharge coefficients with four of the different weir models dimensions of the concrete blocks. In this study, the flow conditions were considered to be free water flow and the viscosity effect was neglected. In all cases, the weir height effect was directly proportional to the discharge coefficient while the surface roughness effect was found to be inversely proportional to the coefficient Cd of the case study.
The Effect of Geometry Parameters and Flow Characteristics on Erosion and Sed...Dr. Amarjeet Singh
One of the most critical problems in the river
engineering field is scouring, sedimentation and morphology
of a river bed. In this paper, a finite volume method
FORTRAN code is provided and used. The code is able to
model the sedimentation. The flow and sediment were
modeled at the interception of the two channels. It is applied
an experimental model to evaluate the results. Regarding the
numerical model, the effects of geometry parameters such as
proportion of secondary channel to main channel width and
intersection angle and also hydraulic conditionals like
secondary to main channel discharge ratio and inlet flow
Froude number were studied on bed topographical and flow
pattern. The numerical results show that the maximum
height of bed increased to 32 percent as the discharge ratio
reaches to 51 percent, on average. It is observed that the
maximum height of sedimentation decreases by declining in
main channel to secondary channel Froude number ratio. On
the assessment of the channel width, velocity and final bed
height variations have changed by given trend, in all the
ratios. Also, increasing in the intersection angle accompanied
by decreasing in flow velocity variations along the channel.
The pattern of velocity and topographical bed variations are
also constant in any studied angles.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Field and Theoretical Analysis of Accelerated Consolidation Using Vertical Dr...inventionjournals
Mumbai is the region consisting of soft compressible marine clay deposits. There are several construction problems on such soils and thus ground improvement is need to be carried out. Vertical drains is generally preferred technique as accelerated settlement is achieved during the construction phase itself if planned accordingly. The concept of vertical drains is based on the theory of three dimensional consolidation as described by Terzaghi (1943). Based on this concept, a consolidation programme is developed and an attempt is made to determine the field to laboratory coefficient of vertical consolidation ratio by Taylor’s Square Root of Time Method and Casagrande’s Logarithm of Time Fitting Method for this region. Based on this, the rate of consolidation and time required for consolidation in the field can be determined knowing the consolidation parameters. Equations are developed by using output of the programme and it is explained.
Design and simulation of microfluidic passive mixer with geometric variationeSAT Journals
Abstract
Microfluidic designs are advantageous and are extensively used in number of fields related to biomedical and biochemical
engineering. The objective of this paper is to perform numerical simulations to optimize the design of microfluidic mixers in order
to achieve optimum mixing. In the present study, fluid mixing in different type of micro channels has been investigated. Numerical
simulations are performed in order to understand the effect of channel geometry parameters on mixing performance. A two
dimensional “T shaped” passive microfluidic mixer is restructured by employing the rectangular shaped obstacles in the channel
to improve the mixing performance. The impact of proper placement of obstacles in the channel is demonstrated by applying the
leakage concept. It has been observed that, the channel design with non-leaky obstacles (i.e. without leaky barriers) has presented
better mixing performance in contrast to channel design with leaky obstacles (i.e. leaky barriers) and channel design without
obstacles. The mixing occurs by virtue of secondary flow and generation of vortices due to curling of fluids in the channel on
account of the presence of obstacles. This passive mixer has achieved complete mixing of fluids in few seconds or some
milliseconds, which is certainly acceptable to utilize in biological applications such as cell dynamics, drug screening,
toxicological screening and others.
Keywords: Microfluidic Mixing, Passive Mixer, Microchannel, Numerical Simulation
A Revisit To Forchheimer Equation Applied In Porous Media FlowIJRES Journal
A brief reference to various non-linear forms of relation between hydraulic gradient and velocity of
flow through porous media is presented, followed by the justification of the use of Forchheimer equation. In
order to study the nature of coefficients of this equation, an experimental programme was carried out under
steady state conditions, using a specially designed permeameter. Eight sizes of coarse material and three sizes
of glass spheres are used as media with water as the fluid medium. Equations for linear and non-linear
parameters of Forchheimer equation are proposed in terms of easily measurable media properties. These
equations are presented in the form of graphs as quick reckoners.
International Journal of Computational Engineering Research(IJCER)ijceronline
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology
Effect Of Water On Slope Stability And Investigation Of ΝΝw Drainage Techniqu...IJMER
International Journal of Modern Engineering Research (IJMER) is Peer reviewed, online Journal. It serves as an international archival forum of scholarly research related to engineering and science education.
Simulation of Sediment Transport in the Canal Using the Hec-Ras (Hydrologic E...inventionjournals
The underground canal of Southwest Kano Irrigation scheme was designed to ensure that water
is conveyed with minimal erosion and sedimentation but over time it has been silted up to the extent that its
conveyance capacity has significantly dropped. This study is based on simulation of sediment transport within
the underground canal in Southwest Kano Irrigation Scheme boundaries using Hydrologic Engineering Centre
– River Analysis System (HEC-RAS) model. Ackers-White sediment transport equation, engraved in the model,
was used to analyse sediment transport characteristics. The conceptual and physical parameters required in the
HEC-RAS model were determined through calibration and direct measurement respectively. The model was
calibrated based on the current operational conditions of the canal followed by simulation using the model to
determine the sediment discharge and deposition rates at different levels of flow in the canal. The Ackers-White
sediment transport equation predicted the sediment sizes which were deposited at specific sections of the canal
at different flow rates. Higher flow rates resulted in minimal deposition. As a sediment management strategy,
these sediment sizes could be screened off at the canal intake, to ensure that sediment passing through would be
transported out to the canal outlet without deposition.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Similar to No-Deposition Sediment Transport in Sewers Using Gene Expression Programming (20)
A nonlinear model for the vibration suppression of a smart composite elastic plate using graphical representation involving fuzzy control is presented. The plate follows the von Kármán and Kirchhoff plate bending theories and the oscillations are caused by external transversal loading forces, which are applied directly on it. Two different control forces, one continuous and one located at discrete points, are considered. The mechanical model is spatially discretized by using the time spectral Galerkin and collocation methods. The aim is to suppress vibrations through a simulation process within a modern graphical computing environment. Here we use MATLAB/SIMULINK, while other similar packages can be used as well. The nonlinear controller is designed, based on an application of a Mamdani-type fuzzy inference system. A computational algorithm, proposed and tested here is not only effective but robust as well. Furthermore, all elements of the study can be replaced or extended, due to the flexibility of the used SIMULINK environment.
Funding agencies such as the U.S. National Science Foundation (NSF), U.S. National Institutes of Health (NIH), and the Transportation Research Board (TRB) of The National Academies make their online grant databases publicly available which document a variety of information on grants that have been funded over the past few decades. In this paper, based on a quantitative analysis of the TRB’s Research In Progress (RIP) online database, we explore the feasibility of automatically estimating the appropriate funding level, given the textual description of a transportation research project. We use statistical Text Mining (TM) and Machine Learning (ML) technologies to build this model using the 14,000 or more records of the TRB’s RIP research grants big data. Several Natural Language Processing (NLP) based text representation models such as the Latent Dirichlet Allocation (LDA), Latent Semantic Indexing (LSI) and the Doc2Vec Machine Learning (ML) approach are used to vectorize the project descriptions and generate semantic vectors. Each of these representations is then used to train supervised regression models such as Random Forest (RF) regression. Out of the three latent feature generation models, we found LDA gives the least Mean Absolute Error (MAE) using 300 feature dimensions and RF regression model. However, based on the correlation coefficients, it was found that it is not very feasible to accurately predict the funding level directly from the unstructured project abstract, given the large variations in source agencies, subject areas, and funding levels. By using separate prediction models for different types of funding agencies, funding levels were better correlated with the project abstract.
Construction Management (CM) has to deal with a variety of uncertainties related to Time, Cost, Quality, and Safety, to name a few. Such uncertainties make the entire construction process highly unpredictable. It, therefore, falls under the purview of artificial neural networks (ANNs) in which the given hazy information can be effectively interpreted in order to arrive at meaningful conclusions. This paper reviews the application of ANNs in construction activities related to the prediction of costs, risk, and safety, tender bids, as well as labor and equipment productivity. The review suggests that the ANN’s had been highly beneficial in correctly interpreting inadequate input information. It was seen that most of the investigators used the feed forward back propagation type of the network; however, if a single ANN architecture was found to be insufficient, then hybrid modeling in association with other machine learning tools such as genetic programming and support vector machines were much useful. It was however clear that the authenticity of data and experience of the modeler are important in obtaining good results.
Assessment of wear rate is an inseparable section of the saw ability of dimension stone, and an essential task to optimization in the diamond wire saw performance. This research aims to provide an accurate, practical and applicable model for predicting the wear rate of diamond bead based on rock properties using applications and performances of intelligent systems. In order to reach this purpose, 38 cutting test results with 38 different rocks were used from andesites, limestones and real marbles quarries located in eleven areas in Turkey. Prediction of wear rate is determined by optimization techniques like Multilayer Perceptron (MLP) and hybrid Genetic algorithm –Artificial neural network (GA-ANN) models that were utilized to build two estimation models by MATLAB software. In this study, 80% of the total samples were used randomly for the training dataset, and the remaining 20% was considered as testing data for GA-ANN model. Further, accuracy and performance capacity of models established were investigated using root mean square error (RMSE), the coefficient of determination (R2) and standard deviation (STD). Finally, a comparison was made among performances of these soft computing techniques for predicting and the results obtained indicated hybrid GA-ANN model with a coefficient of determination (R2) of training = 0.95 and testing = 0.991 can get more accurate predicting results in comparison with MLP models.
Recently the design of RC building to mitigate seismic loads has received great attention. Since Saudi Arabia has low to moderate seismicity, most of the buildings were designed only for gravity load. The objective of this paper is to analysis design RC building located in the most active seismic zone region in Saudi Arabia to mitigate seismic loads. A multi-story reinforced concrete building, in Haql city, was seismically analyzed and designed using the Equivalent Lateral Force Procedure with the aid of SAP200 software. The chosen buildings which were Ordinary Moment Resisting Frame (OMR), was analyzed and designed by using SBC 301 (2007) Saudi Building Code [1], SAP2000 (structural analysis software) [2] and ISACOL "Information Systems Application on Reinforced Concrete Columns" [3]. The results showed that the current design of RC buildings located in the most active seismic zone region in Saudi Arabia, Haql city was found unsafe, inadequate and unsatisfied to mitigate seismic loads.
The purpose of this paper is to perform a structural optimization of a flat thermoplastic plate (tile). This task is developed computationally through the interface between an optimization algorithm and the finite element method with the goal of minimizing the equivalent stress with specified target stress of 2 MPa when applied with a load intensity of 1000N. A 300 x 300 x 20 mm thermoplastic plate was selected for the optimization, which was performed with a tool in MATLAB R2012b known as genetic algorithm accompanied with static analysis in ANSYS 15. The results produced the optimum equivalent stress (δopt) of 2.136 MPa with the optimum dimensions of 305 x 302 x 20 mm. Also, the dimensions of the plate with the optimum value of the equivalent stress were discovered to be within the lower and upper bound dimensions of the plate. The thermoplastic plate object of the optimization was a square plate of 300 x 300mm, and 20 mm thick with isotropic properties and a particular load and boundary conditions were applied on the entire plate.
This paper presents a model for calculation of torsion capacity of the reinforced concrete beams using the artificial neural network. Considering the complex reaction of reinforced concrete beams under torsion moments, torsion strength of these beams is depended on different parameters; therefore using the artificial neural network is a proper method for estimating the torsion capacity of the beams. In the presented model the beam's dimensions, concrete compressive strength and longitudinal and traverse bars properties are the input data, and torsion capacity of the reinforced concrete beam is the output of the model. Also considering the neural network results, a sensitivity analysis is performed on the network layers weight, and the effect of different parameters is evaluated on the torsion strength of the reinforced concrete beams. According to the sensitivity analysis, properties of traverse steel have the most effect on torsion capacity of the beams.
The philosophy of fuzzy logic was formed by introducing the membership degree of a linguistic value or variable instead of divalent membership of 0 or 1. Membership degree is obtained by mapping the variable on the graphical shape of fuzzy numbers. Because of simplicity and convenience, triangular membership numbers (TFN) are widely used in different kinds of fuzzy analysis problems. This paper suggests a simple method using statistical data and frequency chart for constructing non-isosceles TFN when we are using direct rating for evaluating a variable in a predefined scale. In this method, the relevancy between assessment uncertainties and statistical parameters such as mean value and the standard deviation is established in a way that presents an exclusive form of triangle number for each set of data. The proposed method with regard to the graphical shape of the frequency chart distributes the standard deviation around the mean value and forms the TFN with the membership degree of 1 for mean value. In the last section of the paper modification of the proposed method is presented through a practical case study.
Waste concrete is one of the most usable and economic kind of concrete which is used in many civil projects all around the world, and its importance is undeniable. Also, the explanation of constructional process and destruction of them cause the extensive growth of irreversible waste to the industry cycle, which can be as one of the main damaging factors to the economy. In this investigation, with using of constructional waste included concrete waste, brick, ceramic and tile and stone new aggregate was made. Also it was used with different weight ratios of cement in the mix design. The results of laboratory studies showed that the using of the ratio of sand to cement 1 and waste aggregate with 20% weight ratio (W20), replacing of normal aggregate, increased the 28 days compressive strength to the maximum stage 45.23 MPa. In the next stage, in order to develop the experimental results backpropagation neural network was used. This network with about 91% regression, 0.24 error, and 1.41 seconds, is a proper method for estimating results.
More from Journal of Soft Computing in Civil Engineering (9)
Student information management system project report ii.pdfKamal Acharya
Our project explains about the student management. This project mainly explains the various actions related to student details. This project shows some ease in adding, editing and deleting the student details. It also provides a less time consuming process for viewing, adding, editing and deleting the marks of the students.
Saudi Arabia stands as a titan in the global energy landscape, renowned for its abundant oil and gas resources. It's the largest exporter of petroleum and holds some of the world's most significant reserves. Let's delve into the top 10 oil and gas projects shaping Saudi Arabia's energy future in 2024.
Hierarchical Digital Twin of a Naval Power SystemKerry Sado
A hierarchical digital twin of a Naval DC power system has been developed and experimentally verified. Similar to other state-of-the-art digital twins, this technology creates a digital replica of the physical system executed in real-time or faster, which can modify hardware controls. However, its advantage stems from distributing computational efforts by utilizing a hierarchical structure composed of lower-level digital twin blocks and a higher-level system digital twin. Each digital twin block is associated with a physical subsystem of the hardware and communicates with a singular system digital twin, which creates a system-level response. By extracting information from each level of the hierarchy, power system controls of the hardware were reconfigured autonomously. This hierarchical digital twin development offers several advantages over other digital twins, particularly in the field of naval power systems. The hierarchical structure allows for greater computational efficiency and scalability while the ability to autonomously reconfigure hardware controls offers increased flexibility and responsiveness. The hierarchical decomposition and models utilized were well aligned with the physical twin, as indicated by the maximum deviations between the developed digital twin hierarchy and the hardware.
We have compiled the most important slides from each speaker's presentation. This year’s compilation, available for free, captures the key insights and contributions shared during the DfMAy 2024 conference.
Industrial Training at Shahjalal Fertilizer Company Limited (SFCL)MdTanvirMahtab2
This presentation is about the working procedure of Shahjalal Fertilizer Company Limited (SFCL). A Govt. owned Company of Bangladesh Chemical Industries Corporation under Ministry of Industries.
6th International Conference on Machine Learning & Applications (CMLA 2024)ClaraZara1
6th International Conference on Machine Learning & Applications (CMLA 2024) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of on Machine Learning & Applications.
2. 30 I. Ebtehaj, H. Bonakdari/ Journal of Soft Computing in Civil Engineering 1-1 (2017) 29-53
1. Introduction
Transmitting flow through sewage channel is often accompanied by solid materials. Sediment
deposition takes place because of solid materials wide range entry into the sewer as well as the
intermittent and variable nature of flow regimes within the sewer. Therefore, the management of
sediment transport in sewers is considered one of the most important items in sewer designing
and operation. During wet weather flow, the flow rate is enough to suspend the solid sediments.
Solid materials deposition in sewers takes place especially in low flow rate cases such as the
beginning of the layout period, low consumption hours or warm seasons of the year. Permanent
deposition on the pipe bed causes cross-sectional variation and bed roughness and therefore
velocity, and shear stress distribution change and sewer hydraulic resistance consequently
influences sediment transport capacity and finally causes operation maintenance cost increments.
In order to convey minimum entry flow into the sewer, the slope ought to be as much to be able
to prevent sediment deposition, or for a fixed channel slope, the minimum transmitting flow rate
shall be as much to be able to transport solid materials. In addition, to design the velocity that is
somehow capable of transmitting no-deposition solid materials, pipe diameter shall be selected in
a way that transmitting maximum flow rate becomes possible.
Therefore, methods are needed to manage deposit transmission in a way that the transmitting
flow would be capable of cleansing deposited sediments. Also, hand design process needs to be
economical and optimized [1]. The traditional method of designing sewage channels to prevent
sediment deposition in the flow uses minimum velocity or minimum shear stress. In this method,
sewer designing was done by presenting a fixed velocity or minimum shear stress at a
determined flow depth or specified period. For example, ASCE [2] proposes the constant
velocity for full and semi-full flow equal to 0.6 m/s for sanitary sewer and 0.9 m/s for storm
sewer. British Standard [3] proposes 0.75 m/s for full flow and storm sewer and 1 m/s for
combined sewer. European Standard [4] considers the constant velocity for pipes with diameters
less than 300 mm equal to 0.7 m/s. While this criterion has not presented any suggestion for
larger diameter pipes, flow conditions are not denoted in this standard. Also, for constant shear
stress criterion, ASCE [2] has proposed shear stress within the range of 1.3 to 12.6 N/m2,
and
Lysne [5] has proposed shear stress between 2 to 4 N/m2
. Therefore, we can conclude that
velocity or minimum shear stress values are not equal in different conditions and countries. This
is related to implemented experiments, size of sediments in a different region and other
parameters. So, in order to determine self-cleansing velocity, one has to achieve factors effective
on sediment transport such as sediment concentration and size, flow hydraulic depth or radius,
3. I. Ebtehaj, H. Bonakdari/ Journal of Soft Computing in Civil Engineering 1-1 (2017) 29-53 31
pipe roughness, and diameter, so that the designer can achieve minimum required velocity
according to regional conditions.
To survey sediment transmission in the no-deposition case within sewers, sediment transport has
been presented in two general ways: using dimensional analysis and semi-experimental relations.
In order to present sediment transport relations with the use of dimensional analysis,
dimensionless parameters are determined after implementing various experiments and studying
the effect of effective parameters on sediment transport, and finally, sediment transport relations
are presented. In order to present semi-experimental relations with the use of effective forces
exerted on a particle in an equilibrium state, relations are presented. Using dimensional analysis,
presented relations are given in three different states. The first approach evaluates densimetric
Froude Number (Fr) with the use of volumetric sediment concentration (CV), relative flow depth
(d/R) and overall sediment friction factor (λs) [6–9]. The second approach calculates Fr similar to
the first case, but the difference is that in this approach in addition to the presented parameter in
the first case, dimensionless particle number (Dgr) is also used [10–13]. The third approach of the
presented relations which uses dimensional analysis evaluates Fr by using volumetric sediment
concentration (CV) and flow proportional depth (d/R or d/y) [11,14,15]. Semi-experimental
relations are also presented in different ways and will be briefly presented. May [16] obtained his
model of bed load transport based on effective loads which are exerted on particles transmitted at
the limit of deposition. Using dimensional analysis, the author simplified the theoretical model in
order to present his model and fit it with experimental data. May et al. [17] modified the relation
of May [16] by using seven different sets of data. This relation is considered as the best sediment
transport relation at the limit of deposition, which is achieved semi-experimentally [18].
Correcting the relation by Ackers and White [19] and in order to consider flow cross section
form in pipes, Ackers [20] presented his relation. May [17] presented his relation in a semi-
experimental way to transport at the limit of deposition based on effective shear stress on the
sediments surface. To develop a new practical methodology for sewer, a comprehensive research
project conducted in the UK based on available experimental knowledge. The results of this
project were offered by Butler et al. [21]. The harvest of this study is presented as a self-
cleansing sewer design methodology based on a new definition of self-cleansing. The authors
considered an efficient self-cleansing sewer which has sediment transport capacity by
considering a minimum amount of deposited bed to balance between consolidated expenses of
construction, operation, and maintenance. Banasiak [22] investigated the behavior of non-
cohesive and partly cohesive deposited sediment in a partially full sewer pipes and its effect on
the hydraulic performance of sewer. They found the presence of cohesive- like beds is more
4. 32 I. Ebtehaj, H. Bonakdari/ Journal of Soft Computing in Civil Engineering 1-1 (2017) 29-53
desirable than granular ones in terms of the bed roughness. Because the attendance of fine
sediments as deposited sediment results in partly cohesive deposited solids so that decrease or in
such cases prevent bed forms development. Ota and Perrusquia [23] conducted several
experimental tests in two sewer pipes at the limit of deposition condition to the measurement of
sediment particle and sphere velocity. As regards, sediment transport depends on sediment repose
angle; the authors developed a new semi-theoretical equation based on a reclaimed non-
dimensional bed shear stress. Safari et al. [24] carried out a series of experiment tests on
trapezoidal channel cross-section. Using these samples and collected a wide range of
experimental data of U-shape, rectangular and circular channel cross sections from the literature,
the authors developed a self-cleansing model based on the definition of a shape factor to consider
the effect of channel cross section.
In recent years, using soft computing (SC) in different sciences has led to desirable results [25–
29]. To overcome the uncertainty and complexity accompanied with bed load sediment transport
estimation in sewers, Azamathulla et al. [13] presented multi-nonlinear regression-based model
and adaptive neuro-fuzzy inference systems (ANFIS). They found that the offered ANFIS model
could employ as a strength alternative tool in sediment transport prediction at the clean pipe.
Ebtehaj and Bonakdari [30] evaluated the performance of artificial neural network (ANN) in the
estimation of sediment transport using self-cleaning concept. They found the superior results of
ANN in compared with existing regression-based methods. Ebtehaj and Bonakdari [31]
employed two different algorithms; back-propagation (BP) and hybrid of back-propagation and
least-square (BP-LS); to train ANFIS in predicting of sediment transport in sewers. Moreover, to
the generation of fuzzy inference systems (FIS), sub-clustering (SC) and grid partitioning (GP)
were utilized. Based on these methods, they introduced four different methods of ANFIS
training. The results illustrated that a combination of GP and Hybrid results in the most precise
sediment transport prediction.
All computational methods have different advantages and disadvantages depending on the type
of problems, the decision on whether or not to use it. In ANN, the learning and computations are
easy, but the major drawbacks of this approach are as arriving at the local minimum, less
generalizing performance, over-fitting problem and slow convergence speed. Moreover, attaining
the optimal structure of a constructed ANN is not simple [32]. The main shortcoming of fuzzy
logic (FL) is in finding the shape of each variable, and suitable membership functions are
untangled by trial and error [33,34]. To overcome the disadvantage of ANN and fuzzy logic,
ANFIS has been introduced which are known as a most popular strong SC tool. ANFIS is an
adaptive fuzzy system which allows to the utilization of ANN topology with FL simultaneously.
5. I. Ebtehaj, H. Bonakdari/ Journal of Soft Computing in Civil Engineering 1-1 (2017) 29-53 33
It not only contains the features of both approaches but also removes some shortcomings of their
lonely-utilized case. Indeed, ANFIS consists of ANN advantages such as understanding
mathematical details is not obligate and acquaintance with the job data is enough, employed
different algorithm within learning course and solving nonlinear complex problems with strong
capacity [35]. Moreover, the advantages of ANFIS in comparison with ANN have attained highly
nonlinear mapping, better learning capacity, and involves fewer tunable parameters. However,
the most constraints in ANFIS are more complex than FIS, not exist for all types of FIS [32] and
there is no law for tuning the membership functions.
In addition to these drawbacks, the main problem in both of ANN and ANFIS is the existence of
a black-box and don’t provide a certain equation to apply in practical applications. Therefore, it
needs to a technique to overcome this shortcoming. One of the newest presented models in soft
computing topic is Gene Expression Programming (GEP). The main shortcomings of this
method are premature convergence due to the derivation of this method from genetic
programming and genetic algorithm, preservation of best individual based on roulette-wheel
selection method with elitism so that results in losing other better individuals [36] and CPU time-
consuming. Azamathulla and Ab. Ghani [37] predicted pipeline scoured depth with the use of
GEP and concluded that in comparison with existing models, the presented model provides better
results. Khan et al. [38] used GEP to predict bridge pier scour. The authors compared their
presented model with artificial neural network and regression relations and concluded that the
presented model leads to more satisfactory results when compared to existing models. Chang et
al. [39] compared three different methods available in soft computing, adaptive neuro-fuzzy
inference system, feed-forward neural network, and GEP, to survey bed load in the rivers.
Azamathulla and Ahmad [40] used GEP model to predict transverse mixing coefficient in open
channels flow. Using laboratory results mostly, the authors presented a relation to estimate
transverse mixing coefficient which presented the results with more precession compared with
the existing relations. With the use of Gene-Expression Programming (GEP) in this study,
sediment transport in sewerage channels has been studied. The presented model is applicable to
the no-deposition case.
To increase the accuracy of the presented model in this study – in comparison with the existing
models [41] which only used the four basic mathematical operations multiplication, subtraction,
division, and addition – various functions which can be seen in Table 1 were used. Firstly,
considering the effective parameter on sediment transport, six different models have been
presented. Comparing the presented models with data sets which were not used in presenting
models, the best model has been selected. To assess the accuracy of the models presented
6. 34 I. Ebtehaj, H. Bonakdari/ Journal of Soft Computing in Civil Engineering 1-1 (2017) 29-53
through GEP algorithm versus the existing equations, the experimental results of Ota and Nalluri
[14] which had no role in the training of the GEP were used.
2. Non-deposition sediment transport equations
May et al. [17], with the use of seven different data sets [11,12,42–46] studied the existing
sediment transport relations. Laboratory data was used to evaluate these relations. Results of
studying the relations showed that each relation presents good results only for data sets which
have been used for relation presenting, thus in order to present a relation for sediment transport
studying at the limit of deposition, they presented following relation:
4
t
1.5
2
0.6
2
2
V
)
V
V
(1
)
1)D
g(s
V
(
)
D
d
)(
A
D
(
10
3.03
C
(1)
0.47
0.5
t
]
d
y
[
1)d]
0.125[g(s
V
(2)
where CV is volumetric sediment concentration, D pipe diameter, A Cross-sectional area of the
flow, d median diameter of particle size, g gravitational acceleration, s specific gravity of
sediment (=ρs/ρ), V flow velocity, Vt the required velocity for incipient motion of sediment (Eq.
2) and y flow depth.
In order to sediment transport at the limit of deposition Ackers et al. [18] considered the above
relation as the best existing relation for designing usage and Vongvisessomjai et al. [15] too used
Eq. 1 for verification of his relation. Considering volumetric sediment consideration (CV) and
relative flow depth (d/R), Ebtehaj et al. [47] presented the Fr in the form of following relations:
0.54
0.21
V
R
d
4.49C
1)d
g(s
V
Fr
(3)
Ab. Ghani and Azamathulla [41] used GEP to predict the bed load transport in sewers. The
authors presented their equation by considering the parameters of volumetric sediment
concentration (CV), the relative depth of flow (d/R), dimensionless particle number (Dgr) and
Overall sediment friction factor (λs=1.13Dgr
0.01
CV
0.02
λC
0.98
, λC clear water friction factor) as
follows:
7. I. Ebtehaj, H. Bonakdari/ Journal of Soft Computing in Civil Engineering 1-1 (2017) 29-53 35
d
R
D
8.43λ
λ
λ
0.014
D
1
5.91
C
)
d
R
(
0.41
1.425
1)
gd(s
V
gr
1.5
s
s
s
gr
V
(4)
3. Data collection
In this research, a combination of the lab test results by Vongvisessomjai et al. [15] and Ota and
Nalluri [14] was used. The model is proposed using experimental results presented by
Vongvisessomjai et al. [15], and the results of lab experiments are used to verify the feasibility of
the model proposed by Ota and Nalluri [14]. Vongvisessomjai et al. [15] conducted their tests on
pipes in two sizes of 100 and 150 mm in diameter and 16 m in length. They employed two
sections to measure the flow: one at a distance of 4. 5 m upstream, and the other at the distance
of 5.5 m downstream. These two points were 6 m apart. In each section the velocities were
measured at flow surface, middle depth and near bottom and their mean average were taken as
the average velocity. For the air/water phase of the flow, the Manning coefficient of roughness
(n) was equal to 0.0125. Vongvisessomjai et al. [15] tests were conducted in a non-deposited bed
state. More details are given in Vongvisessomjai et al. [15]. To validate the accuracy of results
presented in this article, Ota and Nalluri [14] data were used for a limit of deposition. For the
purpose of their tests at the limit of deposition, Ota and Nalluri [14] used six different
dimensions of d (ranging from 0.71 mm to 5.61 mm). They conducted 24 tests in total.
Moreover, to test the impact of granulation on sediment transport, they conducted 20 further
experiments using five different ranges of sediments with an average diameter of d = 2 mm.
More details are given in Ota and Nalluri [14]. Table 1 shows the range of the data used in their
tests.
Table 1
Range of data in Ota and Nalluri [14] and Vongvisessomjai et al. [15] studies.
y/D V (m/s) R (m) CV (ppm) d (mm)
Ota and Nalluri (1999) 0.39-0.84 0.515-0736 0.005-0.076 16-59 0.6-6.3
Vongvisessomjai et al. (2010) 0.2-0.4 0.24-0.63 0.012-0.032 4 to 90 0.2-0.43
4. Overview of gene expression programming
Gene expression programming (GEP) is an expansion of genetic programming (GP) [48]. GEP
belongs to the family of evolutionary algorithms and is closely related to genetic algorithms and genetic
8. 36 I. Ebtehaj, H. Bonakdari/ Journal of Soft Computing in Civil Engineering 1-1 (2017) 29-53
programming. From genetic algorithms it inherited the linear chromosomes of fixed length, and
from genetic programming, it inherited the expressive parse trees of varied sizes and shapes [49].
The GEP procedure is such that initially required functions for model creation and terminal set
are being selected. In the next step, in order to evaluate the aimed parameter (in this study Fr)
and comparing it with the real value, existing data sets are being recalled. Afterward, in order to
randomly present the initial population, chromosomes are being produced. In the next step, for
population mass production with the use of existing chromosomes, the program is run, and the
fitness of target function is surveyed. If we arrive at pause conditions, the program is stopped,
otherwise with the use of new chromosomes - which have been corrected via genetic operators -
as well as new population; again target function is being evaluated. This action continues until
program pause conditions are present.
The fitness of an individual program (i) for fitness model (j) has been presented by Ferreira [50]
in the following form:
0
,
1
,
)
( )
(
)
(
ij
ij f
else
f
then
p
ij
E
If (5)
Where p precision and E(ij) the error of program i for fitness case (j). For the absolute error, it is
being stated as in the following the form:
j
(ij)
T
p
E(ij)
(6)
Also, the fitness value (fi) for an individual program is stated in the following form:
)
T
p
|
(R
f j
(ij)
i
(7)
where R is selection range, p(ij) the predicted value by the individual program (i) for fitness case
(j) and Tj the target value for fitness case (j). After fitness function determination, the terminal set
(T) and function set (F) have to be determined in order to select chromosomes.
5. Methodology
In order to survey sediment transport in pipes, effective parameters on flow and sediment
particles movement have to be recognized. According to laboratory studies by researchers
[12,15,17], the most important surveyed and utilized parameters to present their relations,
9. I. Ebtehaj, H. Bonakdari/ Journal of Soft Computing in Civil Engineering 1-1 (2017) 29-53 37
include parameters like flow velocity (V), dimensionless particles number (Dgr), volumetric
sediment concentration (CV), median diameter of particles size (d), pipe diameter (D), flow depth
(y), hydraulic radius (R), cross-sectional area of the flow (A), overall sediment friction factor (λs)
and special gravity of sediment (s). Thus dimensionless parameters could be considered in the
form of movement, transport, sediment, transport mode, and flow resistance. Movement
parameters are respectively stated as densimetric Froude number (Fr) or (ψ) which uses shear
stress instead of velocity. Transport parameter contains volumetric sediment concentration (CV)
or the presented transport parameter (φ), dimensionless particle number (Dgr), proportional
average size of particles (d/D) and specific gravity of sediment (s). Transport form parameter
includes the ratio of hydraulic radius to the median diameter of particles size (R/d), the ratio of
squared pipe diameter to the flow cross-sectional area (D2
/A), relative flow depth (y/d) - instead
of which usually R/d is being used - and the flow resistance parameter that considers flow overall
frictional coefficient (λs). Based on these explanations, in order to study the effect of each and
every parameter in different dimensionless groups, dimensionless parameters can be presented in
order to predict Fr in the form of Table 2.
Table 2
Dimensionless sediment transport parameters in clean pipes.
Dimensionless groups
Parameter type
1)d
ρg(s
τ
ψ
1
,
1)
gd(s
V
Fr 0
Movement
3
V
V
1)d
g(s
VR
C
φ
,
C
Transport
s
d/D,
,
Dgr
Sediment
y/D
d/y,
/A,
D
d/R, 2
Transport mode
)/D
k
(k
,
λ s
0
s
Flow resistance
It is necessary to use different statistical indexes to verify the feasibility of the proposed model.
The statistical indexes used in this study include dimensionless coefficient criteria called R-
Squared (R2
), the relative criteria of Mean Average Percentage Error (MAPE) and absolute
criteria of Root Mean Squared Error (RMSE). The R-Squared (R2
) index is the ratio of the
combined dispersion of the estimated model and the observed value to the dispersion of the
estimated and observed models. The MAPE expresses the estimated value in relation to the
observed value. MAPE is a non-negative index which has no higher limit. The RMSE is a
10. 38 I. Ebtehaj, H. Bonakdari/ Journal of Soft Computing in Civil Engineering 1-1 (2017) 29-53
criterion of mean error that has no upper limit and has the lowest possible value of zero,
representing the best estimation by the model.
2
n
1
i
2
i
GEP
i
EXP
n
1
i
2
i
EXP
i
EXP
n
1
i
i
GEP
i
GEP
i
EXP
i
EXP
2
Fr
Fr
Fr
Fr
Fr
Fr
Fr
Fr
R
(8)
n
1
i
2
i
GEP
i
EXP
)
Fr
(Fr
n
1
RMSE
(9)
)
Fr
|
Fr
Fr
|
(
)
n
100
(
MAPE
n
1
i
i
EXP
i
GEP
i
EXP
(10)
The indexes mentioned above present the estimated amounts as the average of the forecasted
error and do not present any information on the forecasted error distribution of the suggested
models. It is obvious that a high correlation coefficient (80- 90%) is not always considered as an
indication of the high accuracy of a model; on the contrary, this index may lead to showing high
accuracy for mediocre models [51]. In addition, RMSE index indicates the model’s ability to
predict a value away from the mean [52]. Therefore, the presented model must be evaluated
using other indexes such as average absolute relative error (AARE) and threshold statistics [53–
56]. TSx index indicates forecasted error distribution by each model for x% of the anticipations.
This parameter is determined for various amounts of average absolute relative error. The amount
of the TS index for x% of the predictions is determined as explained below:
100
n
Y
TS x
x
(11)
n
1
i
i
EXP
i
GEP
i
EXP
Fr
Fr
Fr
n
1
AARE
(12)
Where Yx is the number of the forecasted amounts of all the data for each amount of AARE is less
than x%.
11. I. Ebtehaj, H. Bonakdari/ Journal of Soft Computing in Civil Engineering 1-1 (2017) 29-53 39
6. Derivation of densimetric Froude number based on GEP
This section concentrates on GEP method to calculate densimetric Froude number (Fr). The
training set must be selected from amongst all the existing data to present a model. To that end,
the data presented by Vongvisessomjai et al. [15] was selected as training set, and the data
presented by Ota and Nalluri [14] was selected as a testing set. The training environment of the
system has been defined after selecting the training set. After classifying the data, various
parameters must be defined to make a model. To ,create the generation the initial population of
the individuals, multi-genic chromosomes, are used which include four genes. We must now
determine the number of the initial population. Considering Ferreira’s [49] suggestion stating
that using the size population within the range of 30- 100 can lead to good results, the size of the
used population includes 50 chromosomes in this study which was selected through trial and
error. After selecting the population size, the individuals are evaluated, and their fitness function
is calculated using MSE as follows:
j
ij
i
i
i O
P
E
for
E
f
1
1000
(13)
Where Qij is the amount observed for fitness case, and Pij is the amount predicted by using i
individual chromosome for fitness case j. The best state is when the equation Eij= 0 is obtained.
This means that the amounts predicted using i individual chromosome for fitness case j is equal
to the amount observed for fitness case j (Pij= Eij). The set of terminals and the set of the
function must be determined for each gene in the chromosome after selecting fitness function.
The function sets used in this study include {×, -, ÷, ×, Gau2} while the set of terminals are as
follows:
s
2
gr
V
r λ
,
D
R
,
A
D
,
R
d
,
D
d
,
D
,
C
,
F
T (14)
Afterward, the number of genes and their head and tail length must be determined for each gene
in the chromosome. By using trial and error and the succeeding rate, four genes were selected in
the present study in each chromosome. The head length was selected to be 5 (h=5), and while the
maximum number of arguments per function is equal to 2 (nmax= 2), the length of the tail turns
out to be equal to 6 (t= 5 × (2-1) +1). The genetic operator rate must now be determined. Genetic
operators such as mutation, inversion, transportation (IS, RIS, gene transportation),
12. 40 I. Ebtehaj, H. Bonakdari/ Journal of Soft Computing in Civil Engineering 1-1 (2017) 29-53
recombination and crossover (one point, two points, and gene recombination) were used. The
rates of the mentioned parameters are presented in Table 3. We must finally determine the linking
function. Considering the fact that using four different sub-expressions in this study has led to
having four genes, the genes must be bound for us to reach the final result. Therefore, {+}
operator has been used as the linking function among the genes in this study. Simulating the
model begins after determining the essential parameters. Gau2{x, y} function presented in Table
2 returns exp (-(x+ y)2
) amount.
Table 3
Parameters of GEP model.
Parameter Setting
Population size 50
Number of generations 250000
Number of chromosomes 50
Number of genes 4
function set ×, -, ÷, ×, Gau2
Linking function Addition
Mutation rate 0.03
Inversion rate 0.15
IS transposition rate 0.1
RIS transposition rate 0.1
Gene transposition rate 0.15
One-point recombination rate 0.3
Two-point recombination rate 0.3
Gene recombination rate 0.15
7. Result and discussion
To study sediment transport, different parameters in no-deposition stage and to present a model
which could estimate the best results in comparison with actual values, dimensionless parameters
13. I. Ebtehaj, H. Bonakdari/ Journal of Soft Computing in Civil Engineering 1-1 (2017) 29-53 41
in Table 2 have been used. As considered in this table, dimensionless parameters that effect on
the sediment transport in no-deposition mode are categorized into five groups. In order to present
a model, the effect of four groups of transport, deposition, and transport form and flow resistance
on movement group was surveyed. Thus, six different models are listed in Table 4. In the
presented models, volumetric sediment concentration (CV) which is related to transport
dimensionless group and overall sediment frictional coefficient (λs) which is related to flow
resistance dimensionless group have been considered constant. For sediment group Dgr and d/D
parameters and transport form group, d/R ،D2
/A and y/D have been considered.
Table 4
Dependent parameters in predicting Fr considering the effect of dimensionless group parameters.
Model
Dependent Independent Train Test
parameter parameters R2
MAPE RMSE R2
MAPE RMSE
1 Fr CV, Dgr, d/R, λs 0.98 2.66 0.16 0.96 2.94 0.12
2 Fr CV, Dgr, D2
/A, λs 0.89 6.58 0.73 0.81 11.09 0.30
3 Fr CV, Dgr, y/D, λs 0.89 5.33 0.53 0.90 7.84 0.32
4 Fr CV, d/D, d/R, λs 0.99 2.56 0.12 0.99 2.82 0.14
5 Fr CV, d/D, D2
/A, λs 0.92 5.70 0.65 0.85 9.39 0.27
6 Fr CV, d/D, y/D, λs 0.97 2.94 0.20 0.96 3.05 0.19
Table 3 shows sextet presented models with the use of Table 1. In order to present models,
laboratory results by Vongvisessomjai et al. [15] have been utilized. After presenting different
models to evaluate the estimated results, via each model, the Fr has been surveyed with the use
of Ota and Nalluri [14] laboratory results. According to verification criteria presented in Table 3,
model 4 which uses volumetric sediment concentration (CV), relative flow depth (d/R),
proportional average size of particles (d/D) and overall frictional factor (λs) to estimate Fr
delivers the best result. The MAPE index in Fr evaluation with the use of this model is about
2.56% for the test, and 2.82% for train and RMSE is 0.12 for train and 0.14 for the test. It is
considered that the effect of data sets alternations on the precision of this model is about less than
14. 42 I. Ebtehaj, H. Bonakdari/ Journal of Soft Computing in Civil Engineering 1-1 (2017) 29-53
1%. Other presented models in this table, compared to the mode with train data, show better
results than test data, and this is an indication that using these models (models 1, 2, 3, 5 and 6)
would not be trustworthy. Therefore, it could be said that to present a model which could well
estimate Fr in a sewer at the limit of deposition state, effective parameters can be considered like
model 4 in Table 3. This means that using CV as transport parameter, d/R as transport form
parameter, d/Das sediment parameter, and λs as flow resistance parameter in Fr evaluation, leads
to good results. The presented equation through using the parameters of model 4 and expression
tree presented in Figure 1 can be presented as follows. The amounts of the parameters presented
in this figure have been shown in Table 5.
9.54
5.9
D
d
92.4
D
d
D
d
λ
C
exp
132.42
D
d
λ
C
67
D
d
C
D
d
R
d
4.23
λ
15.45
D
d
C
33.1
R
d
C
R
d
λ
D
d
R
d
89.66
D
d
r
F
2
s
V
s
V
V
s
V
V
s
(15)
We can rewrite the above-mentioned formula as follows:
0.62
D
d
92.4
D
d
D
d
λ
C
exp
C
67
D
d
132.42
D
d
λ
C
D
d
R
d
4.23
λ
15.45
D
d
C
33.1
R
d
C
R
d
λ
89.66
R
d
D
d
2
r
F
2
s
V
V
s
V
s
V
V
s
(16)
15. I. Ebtehaj, H. Bonakdari/ Journal of Soft Computing in Civil Engineering 1-1 (2017) 29-53 43
Fig. 1. Expression Tree (ET) for GEP formulation.
16. 44 I. Ebtehaj, H. Bonakdari/ Journal of Soft Computing in Civil Engineering 1-1 (2017) 29-53
Table 5
Values of the parameters used in ET (Figure 1).
d0 d1 d2 d3 G1C5 G2C2 G2C0 G2C5 G3C8 G3C6 G4C7 G4C0 G4C6
Cv d/R d/D λs 89.66 33.10 4.23 15.45 132.42 67.08 92.40 -5.90 9.54
Figure 2 shows the Fr results predicted by model 4 (Eq. 15) in both training and testing stage.
Due to the fact that the accuracy of GEP model presented in Table 4 in this research (Eq. 15) has
been studied quantitatively for both test (MAPE= 2.82 & RMSE= 0.14) and train (MAPE= 2.56
& RMSE= 0.12) states, in this figure, we will attend to studying the prediction results of the GEP
model. The figure indicates that the forecasted Fr which were obtained through using GEP
presented fairly good results in both train and test states while almost all estimated amounts have
a relative error of less than 10%. The data used for the purpose of test and train of equation 15
have different ranges of Fr in such manner that the Fr used in training the model was within the
range of 4 to 9 while the Fr range used in testing the model is 3 to 6. Therefore, it could be stated
that while studying the model accuracy in test state all the Fr are not within the range which was
used in training the model, thus, considering the qualitative results (Table 4) and quantitative
results (Figure 2), it proves the accuracy of the presented results obtained by this equation.
Fig. 2. Comparison of GEP result for both of train and test stages with actual values.
17. I. Ebtehaj, H. Bonakdari/ Journal of Soft Computing in Civil Engineering 1-1 (2017) 29-53 45
Figure 3 compares the prediction results of Fr by using the GEP model presented in this study
(Eq. 15) and the existing regression equation with the actual values. The figure shows that the
results of the presented predictions using GEP are almost accurate in a way that all forecasted
points have an error less than ten percent and taking into consideration Figure 4 which shows the
accumulative distribution of the error, we can see that the maximum relative error in estimating
Fr through using GEP is almost equal to 6 %. Also, this figure indicates that approximately 90%
of the anticipated amounts have a relative error of less than 5%. Now in case, we intend to study
the results of the presented model through statistical indexes, referring to Table 6 shows us that
the amounts of the presented statistical indexes for this model with an R2
= 0.99, MAPE= 2.82
and RMSE= 0.14 is minimum in an amount in comparison to other equations presented in this
table. The equation presented by Ab. Ghani and Azamathulla [41] is less accurate (R2
= 0.74,
MAPE= 13.18 and RMSE= 0.49) considering Table 6 and Figure 4. The figure indicates that in
the majority of the points the results are presented with an error more than 10 percent.
Figure 4 shows that only 25% of the amounts estimated by this model have a relative error of
less than 10%. Also, it indicates that some of the Fr forecasted by this equation have a relative
error of more than 30 percent which indicates the uncertainty of the equation presented by Ab
Ghani and Azamathulla [41]. Therefore, using this equation for the purpose of estimating Fr
cannot be that much confidence. Ebtehaj et al. [47] equation are fairly accurate because it
estimates the majority of Fr with a less-than-10-percent relative error, but it is less accurate in
comparison with the equation presented in this study. This is in a way that considering Figure 4,
which indicates the distribution of the estimation error by different models, we can see that
approximately 70 percent of the estimation results of this model have an error less than 5% while
for the model presented in this study the predicted amounts have an error of less than 5% for
almost 90% of the Fr. At times, May et al. [17] equation which has been obtained through semi-
experimental method and has been known as one of the best sediment transport equations in limit
of deposition [15] presents the estimated amounts with a more- than-15% relative error
according to Figure 5 while the equation presented in this study has a maximum relative error of
6%. Also, considering Figure 6, the amounts of statistical indexes presented by this equation
(R2
= 0.93, MAPE= 5.74 and RMSE= 0.24) indicates lesser estimation accuracy of this equation
in comparison with that of the presented equation.
18. 46 I. Ebtehaj, H. Bonakdari/ Journal of Soft Computing in Civil Engineering 1-1 (2017) 29-53
Fig. 3. Comparison of proposed equation and existing equations.
Fig. 4. Error distribution of for GEP and existing equations.
19. I. Ebtehaj, H. Bonakdari/ Journal of Soft Computing in Civil Engineering 1-1 (2017) 29-53 47
Table 6
Validation of proposed equation and existing equations with statistical indexes.
Equation R2
MAPE RMSE
Proposed Equation (Eq. 15) 0.99 2.82 0.14
Ab. Ghani and Azamathulla (Eq. 4) 0.74 13.18 0.49
Ebtehaj et al. (Eq. 3) 0.97 3.70 0.18
May et al. (Eq. 1) 0.93 5.74 0.24
Accordingly, in this study, the effects of GEP model output on the variations of dimensionless
particle number (Dgr in this study) were investigated. The discrepancy ratio (DR) (ratio of
predicted to actual values) was employed to measure the sensitivity of the GEP model to Dgr
parameter. A DR value of 1 shows a perfect agreement, while values smaller (or greater) than 1
indicate under (or over) prediction of discharge coefficient inside weir. The result of the GEP
model for variations of the discrepancy ratio (DR) values is plotted versus the dimensionless
particle number (Dgr) in Figure 5. The maximum, mean and minimum DR values for GEP model
were obtained 1.06, 1.005 and 0.93, respectively. As Figure 5 shows it, for almost all the Fr
estimated, the DR is close to 1. When GEP predicts the model using the over design way, the
dimensionless particle number will be equal to 1.7 (DR= 1.06), and when it uses the under-
design way to predict Fr, dimensionless particle number is equal to 2.15 (DR= 0.93).
Fig. 5. DR values versus Dgr for GEP model.
20. 48 I. Ebtehaj, H. Bonakdari/ Journal of Soft Computing in Civil Engineering 1-1 (2017) 29-53
Based on explanations given in Figures 3 and 5, and Table 6, the equation presented in this study
is more accurate than the existing regression equations. While it is more accurate in studying the
estimation accuracy through using statistical indexes, it is also more accurate in studying the
estimation error distribution in Figure 4 and can be utilized as a substituting method in Fr
estimation for sediment transport in no-deposition mode.
8. Conclusion
Transmitting flow from sewerage systems often contains suspended materials. Therefore,
transporting suspended materials and preventing their sedimentation are important matters inflow
transport through sewerage networks. Different methods have been presented for sediment
transport in sewage, but due to the lack of recognition of effective factors on sediment transport,
these methods show different results in different conditions. Hence, in recent years, soft
computations have been utilized in order to estimate densimetric Froude number (Fr) in these
systems. In this paper, with the use of the presented model by Gene-expression programming
(GEP), Fr has been estimated. In order to present the effective factor on Fr estimation, six
different models were presented. In these models, the effect of movement, transport, sediment,
transport mode and flow resistance parameters have been considered. After Fr estimation, the
precision of all sextet models has been studied. The results indicated that among the three
parameters provided by “Transport mode” group, the best and the worst accuracy were achieved
by using d/R and D2
/A (respectively) as improper use of the parameters of this group, up to two-
fold increase relative error. In addition to, Also, with the constant parameters in the groups
“transport”, “flow resistance” and “transport mode”, the parameter d/D in all input combinations,
leading to better results than when used Dgr as “sediment” parameter. Therefore, it was revealed
that the model which considers volumetric sediment concentration (CV), relative flow depth
(d/R), proportional average size of particles (d/D), overall friction factor (λs) for Fr estimation,
shows the best results. The presented model estimates Fr with an average error value of about
2.82%. The comparison of existing methods illustrated the high level of accuracy of Ebtehaj et
al. (Eq. 3) method in comparison with others. It should not be an inappropriate use of GEP
functions such as Eq. (4) results in weak performance of the model. The presented model with
existing values was also studied, and the results showed that in proportion with existing relations
the model well estimates the Fr. Incidentally making use of the proposed GEP-based technique
in the form of the most superlative formulations has a dominant role to experience in the
attaining astonishing and remarkable successes for real-world application. Another plus aspect of
21. I. Ebtehaj, H. Bonakdari/ Journal of Soft Computing in Civil Engineering 1-1 (2017) 29-53 49
this study is the use of extracted mathematical expressions as a realistically valuable technique
for practical engineering as an alternative for existing methods.
Notation
A Cross-sectional area of the flow
CV Volumetric sediment concentration
D Pipe diameter
d Median diameter of particle size
Dgr Dimensionless particle number
E(ij) Error of program i for fitness case j (Eq. 5)
Fr Densimetric Froude number
P Precision (Eq. 5)
P(ij) Value predicted by individual program i for fitness case j (Eq. 6)
R Hydraulic radius, Selection Range (Eq. 6)
s Specific gravity of sediment (=ρs/ρ)
V Velocity of flow
Vt Incipient flow velocity which follows from equation (2)
y Flow depth
λc Clear Water friction factor
λs Overall sediment friction factor
ψ Flow parameter
φ Transport parameter
References
[1] Butler D, Clark P. Sediment management in urban drainage catchments. Report No. 134,
Construction Industry Research and Information Association, London, UK: 1995.
[2] ASCE. Water pollution control federation: Design and construction of sanitary and storm
sewers. American Society of Civil Engineers Manuals and Reports on Engineering
Practices, No. 37, Reston, VA: 1970.
[3] BS8005-1. Sewerage Guide to New Sewerage Construction. British Standard Institution,
London, UK: 1987.
22. 50 I. Ebtehaj, H. Bonakdari/ Journal of Soft Computing in Civil Engineering 1-1 (2017) 29-53
[4] European Standard EN 752-4. Drain and sewer system outside building: Part 4. Hydraulic
design and environmental considerations. Brussels: CEN (European Committee for
Standardization): Part; 1997.
[5] Lysne DK. Hydraulic design of self-cleaning sewage tunnels. J Sanit Eng Div 1969;95:17–
36.
[6] Pedroli R. Bed load transportation in channels with fixed and smooth inverts. Scuola
Politecnica Federale, Zurigo, Switzerland, 1963.
[7] GRAF WH, ACAROGLU ER. Sediment transport in conveyance systems (Part 1)/A
physical model for sediment transport in conveyance systems. Hydrol Sci J 1968;13:20–39.
[8] Novak P, Nalluri C. Sediment transport in smooth fixed bed channels. J Hydraul Div
1975;101:1139–54.
[9] Nalluri C. Sediment transport in rigid boundary channels. Proceeding Euromech 192
Transp. Suspended Solids Open channels, vol. 192, Neubiberg, Germany: 1985, p. 101–4.
[10] Mayerle R. Sediment transport in rigid boundary channels. University of Newcastle Upon
Tyne, UK, 1988.
[11] Mayerle R, Nalluri C, Novak P. Sediment transport in rigid bed conveyances. J Hydraul
Res 1991;29:475–95. doi:10.1080/00221689109498969.
[12] Ghani A. Sediment transport in sewers. University of Newcastle Upon Tyne, UK, 1993.
[13] Azamathulla HM, Ab. Ghani A, Fei SY. ANFIS-based approach for predicting sediment
transport in clean sewer. Appl Soft Comput 2012;12:1227–30.
doi:10.1016/j.asoc.2011.12.003.
[14] Ota JJ, Nalluri C. Graded sediment transport at limit deposition in clean pipe channel. 28th
Congr. Int. Assoc. Hydro-Environmental Eng. Res. Graz, Austria, 1999.
[15] Vongvisessomjai N, Tingsanchali T, Babel MS. Non-deposition design criteria for sewers
with part-full flow. Urban Water J 2010;7:61–77. doi:10.1080/15730620903242824.
[16] May RWP. Sediment transport in sewers. Hydraulic Research Station, Wallingford,
England, Report IT 222; 1982.
[17] May RWP, Ackers JC, Butler D, John S. Development of design methodology for self-
cleansing sewers. Water Sci Technol 1996;33:195–205.
[18] Ackers JC, Butler D, May RWP. Design of sewers to control sediment problems. Report
No. CIRIA 141, Construction Industry Research and Information Association, London,
UK: Construction Industry Research and Information Association London; 1996.
[19] Ackers P, White WR. Sediment transport: new approach and analysis. J Hydraul Div
1973;99:2041–60.
[20] Ackers P. Sediment aspects of drainage and outfall design. Proc. Int. Symp. Environ.
Hydraul. Hong kong, 1991.
23. I. Ebtehaj, H. Bonakdari/ Journal of Soft Computing in Civil Engineering 1-1 (2017) 29-53 51
[21] Butler D, May R, Ackers J. Self-Cleansing Sewer Design Based on Sediment Transport
Principles. J Hydraul Eng 2003;129:276–82. doi:10.1061/(ASCE)0733-
9429(2003)129:4(276).
[22] Banasiak R. Hydraulic performance of sewer pipes with deposited sediments. Water Sci
Technol 2008;57:1743. doi:10.2166/wst.2008.287.
[23] Ota JJ, Perrusquía GS. Particle velocity and sediment transport at the limit of deposition in
sewers. Water Sci Technol 2013;67:959. doi:10.2166/wst.2013.646.
[24] Safari M-J-S, Aksoy H, Unal NE, Mohammadi M. Non-deposition self-cleansing design
criteria for drainage systems. J Hydro-Environment Res 2017;14:76–84.
doi:10.1016/j.jher.2016.11.002.
[25] Mondal SK, Jana S, Majumder M, Roy D. A comparative study for prediction of direct
runoff for a river basin using geomorphological approach and artificial neural networks.
Appl Water Sci 2012;2:1–13. doi:10.1007/s13201-011-0020-3.
[26] GAD MI, Khalaf S. Application of sharing genetic algorithm for optimization of
groundwater management problems in Wadi El-Farigh, Egypt. Appl Water Sci
2013;3:701–16. doi:10.1007/s13201-013-0114-1.
[27] Al-Abadi AM. Modeling of stage–discharge relationship for Gharraf River, southern Iraq
using backpropagation artificial neural networks, M5 decision trees, and Takagi–Sugeno
inference system technique: a comparative study. Appl Water Sci 2016;6:407–20.
doi:10.1007/s13201-014-0258-7.
[28] Ahmadianfar I, Adib A, Taghian M. Optimization of multi-reservoir operation with a new
hedging rule: application of fuzzy set theory and NSGA-II. Appl Water Sci 2017;7:3075–
86. doi:10.1007/s13201-016-0434-z.
[29] Azimi H, Bonakdari H, Ebtehaj I, Ashraf Talesh SH, Michelson DG, Jamali A.
Evolutionary Pareto optimization of an ANFIS network for modeling scour at pile groups
in clear water condition. Fuzzy Sets Syst 2017;319:50–69. doi:10.1016/j.fss.2016.10.010.
[30] Ebtehaj I, Bonakdari H. Evaluation of Sediment Transport in Sewer using Artificial Neural
Network. Eng Appl Comput Fluid Mech 2013;7:382–92.
doi:10.1080/19942060.2013.11015479.
[31] Ebtehaj I, Bonakdari H. Performance Evaluation of Adaptive Neural Fuzzy Inference
System for Sediment Transport in Sewers. Water Resour Manag 2014;28:4765–79.
doi:10.1007/s11269-014-0774-0.
[32] Rezaei H, Rahmati M, Modarress H. Application of ANFIS and MLR models for
prediction of methane adsorption on X and Y faujasite zeolites: effect of cations
substitution. Neural Comput Appl 2017;28:301–12. doi:10.1007/s00521-015-2057-y.
[33] Singh R, Vishal V, Singh TN. Soft computing method for assessment of compressional
wave velocity. Sci Iran 2012;19:1018–24. doi:10.1016/j.scient.2012.06.010.
[34] Singh R, Vishal V, Singh TN, Ranjith PG. A comparative study of generalized regression
neural network approach and adaptive neuro-fuzzy inference systems for prediction of
24. 52 I. Ebtehaj, H. Bonakdari/ Journal of Soft Computing in Civil Engineering 1-1 (2017) 29-53
unconfined compressive strength of rocks. Neural Comput Appl 2013;23:499–506.
doi:10.1007/s00521-012-0944-z.
[35] Isanta Navarro R. Study of a neural network-based system for stability augmentation of an
airplane. Univ Politècnica Catalunya 2013.
[36] Gan Z, Yang Z, Li G, Jiang M. Automatic Modeling of Complex Functions with Clonal
Selection-Based Gene Expression Programming. Third Int. Conf. Nat. Comput. (ICNC
2007), IEEE; 2007, p. 228–32. doi:10.1109/ICNC.2007.278.
[37] Azamathulla HM, Ghani AA. Genetic Programming to Predict River Pipeline Scour. J
Pipeline Syst Eng Pract 2010;1:127–32. doi:10.1061/(ASCE)PS.1949-1204.0000060.
[38] Khan M, Azamathulla HM, Tufail M, Ab Ghani A. Bridge pier scour prediction by gene
expression programming. Proc Inst Civ Eng - Water Manag 2012;165:481–93.
doi:10.1680/wama.11.00008.
[39] CHANG CK, AZAMATHULLA HM, ZAKARIA NA, GHANI AA. Appraisal of soft
computing techniques in prediction of total bed material load in tropical rivers. J Earth Syst
Sci 2012;121:125–33. doi:10.1007/s12040-012-0138-1.
[40] Azamathulla HM, Ahmad Z. Gene-expression programming for transverse mixing
coefficient. J Hydrol 2012;434–435:142–8. doi:10.1016/j.jhydrol.2012.02.018.
[41] Ab. Ghani A, Md. Azamathulla H. Gene-Expression Programming for Sediment Transport
in Sewer Pipe Systems. J Pipeline Syst Eng Pract 2011;2:102–6.
doi:10.1061/(ASCE)PS.1949-1204.0000076.
[42] Macke E. About sedimentation at low concentrations in partly filled pipes. (In Ger
Mitteilungen, LeichtweissÐInstitut FuÈ r Wasserbau Der Tech Univ t Braunschweig 1982.
[43] May RWP, Brown PM, Hare GR, Jones KD. Self-cleaning conditions for sewers carryign
sediment. Hydraulic Research Ltd (Wallingford), Report SR 221; 1989.
[44] May RWP. Sediment transport in pipes, sewers and deposited beds. Hydraulic Research
Ltd., Wallingford, England, Report SR 320; 1993.
[45] Nalluri C, Ab-Ghani A. Bed load transport with deposition in channels of circular cross
section. Proc. Sixth Int. Conf. Urban Storm Drain., Niagara Falls, Canada: 1993.
[46] Nalluri C, Ghani AA, El-Zaemey AKS. Sediment transport over deposited beds in sewers.
Water Sci Technol 1994;29:125–33.
[47] Ebtehaj I, Bonakdari H, Sharifi A. Design criteria for sediment transport in sewers based
on self-cleansing concept. J Zhejiang Univ Sci A 2014;15:914–24.
doi:10.1631/jzus.A1300135.
[48] John R. Koza. Genetic programming: on the programming of computers by means of
natural selection. MIT Press Cambridge, MA, USA; 1992.
[49] Ferreira C. Algorithm for solving gene expression programming: a new adaptive problems.
Complex Syst 2001;13:87–129.
[50] Ferreira C. Gene expression programming: mathematical modeling by an artificial
intelligence. vol. 21. 2nd ed. Springer; 2006.
25. I. Ebtehaj, H. Bonakdari/ Journal of Soft Computing in Civil Engineering 1-1 (2017) 29-53 53
[51] Legates DR, McCabe GJ. Evaluating the use of “goodness-of-fit” Measures in hydrologic
and hydroclimatic model validation. Water Resour Res 1999;35:233–41.
doi:10.1029/1998WR900018.
[52] Hsu K, Gupta HV, Sorooshian S. Artificial Neural Network Modeling of the Rainfall-
Runoff Process. Water Resour Res 1995;31:2517–30. doi:10.1029/95WR01955.
[53] Jain A, Kumar Varshney A, Chandra Joshi U. Short-Term Water Demand Forecast
Modelling at IIT Kanpur Using Artificial Neural Networks. Water Resour Manag
2001;15:299–321. doi:10.1023/A:1014415503476.
[54] Jain A, Ormsbee LE. Short-term water demand forecast modeling techniques-
CONVENTIONAL METHODS VERSUS AI. J Am Water Works Assoc 2002;94:64–72.
doi:10.1002/j.1551-8833.2002.tb09507.x.
[55] Rajurkar MP, Kothyari UC, Chaube UC. Modeling of the daily rainfall-runoff relationship
with artificial neural network. J Hydrol 2004;285:96–113.
doi:10.1016/j.jhydrol.2003.08.011.
[56] Maghrebi MF, Givehchi M. Using non-dimensional velocity curves for estimation of
longitudinal dispersion coefficient. Proc. seventh Int. Symp. river Eng., 2007, p. 16–8.