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.
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 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.
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.
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.
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.
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.
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 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.
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.
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.
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.
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.
International Journal of Engineering Research and Applications (IJERA) is a team of researchers not publication services or private publications running the journals for monetary benefits, we are association of scientists and academia who focus only on supporting authors who want to publish their work. The articles published in our journal can be accessed online, all the articles will be archived for real time access.
Our journal system primarily aims to bring out the research talent and the works done by sciaentists, academia, engineers, practitioners, scholars, post graduate students of engineering and science. This journal aims to cover the scientific research in a broader sense and not publishing a niche area of research facilitating researchers from various verticals to publish their papers. It is also aimed to provide a platform for the researchers to publish in a shorter of time, enabling them to continue further All articles published are freely available to scientific researchers in the Government agencies,educators and the general public. We are taking serious efforts to promote our journal across the globe in various ways, we are sure that our journal will act as a scientific platform for all researchers to publish their works online.
Number of Iteration Analysis for Complex FSS Shape Using GA for Efficient ESGjournalBEEI
ESG stand for Energy-Saving Glass is a special shielded glass with a metallic oxide layer to abuse undesirable of infrared and ultraviolet radiation into construction assemblies like our home. Firstly, different number of the iteration is the main thing to study a performance of the frequency selective surface shape using genetic algorithm (GA) for efficient energy saving glass (ESG). Three different values for the number of iterations were taken that is 1500, 2000 1nd 5000. Before that, the response of this complex FSS shape on incident electromagnetic wave with different symmetry shape are investigating. Three of them are no symmetrical shape, ¼ symmetrical shape, and 1/8 symmetrical shape. The 1500 number simulation considered about 89.000 per second, compared with 2000 iteration and 5000 iterations had consumed 105.09 per second and 196.00 per second, respectively. For 1/8 symmetry complex FSS shape, it demonstrations the improved performance of transmission loss at 1.2 GHz with - 40 dB. A 2 dB of transmission loss is achieved at WLAN application of 2.45 GHz with 0°, 30°, and 45° incidence angle shows
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.
Investigations on material casualty of plates under impact load conditionseSAT Journals
Abstract Impact problems are typical in nature since they involve geometry, boundary and material non-linearity. The impact of projectiles at sub-ordnance velocities against mild steel, stainless steel and aluminum plates has been studied. In this paper, target plates of 3mm thickness of materials Mild steel, Titanium are made to impact by Tungsten fragment with different velocities 300, 500, 700 and 1000 mm/ms. On impact, kinetic energy and residual velocity of this fragment is plotted to visualize the damage of the respective target plate. It is observed that the element size significantly affects the numerical results. Hence a sufficiently refined mesh is used. Kinetic energy is an essential parameter to be determined in order to study damage behavior of target. Higher the kinetic energy absorption, leads to higher is the damage target. So, higher initial velocities are required for the fragment in order to create the necessary damage in the target. Index Terms: Projectile, Target plate, Mild steel, Tungsten, Titanium
Micro Mechanical Modeling of Fiber / Epoxy Unidirectional Laminates Using Featheijes
The focus of the study was to develop the micromechanical model associated with proper damage model to predict the overall mechanical behavior of fiber/matrix unidirectional laminates. The present and first investigation studies the influence of fiber-matrix interface on the behaviour of fiber reinforced composite lamina using micromechanical models. Mechanical properties E1 and E2 are determined at various volume fractions. The second investigation studies the micro-thermo elastic behaviour of the square unit cell of a hybrid fiber reinforced composite lamina. Later this model is extended to predict the coefficients of thermal expansion of graphite-boron hybrid fiber reinforced lamina for various volume fractions.In the third investigation, an analytical solution of the thermal stresses for a fiber embedded in a matrix is presented based on the idea of the finite element and under some simplifying assumptions. The analytical solution to the problem is found for the case when the length of the embedded bar (fiber) is much greater than its radius, and the Young's modulus of the matrix is much less than that of the fiber. The problem is also solved numerically by means of finite element analysis using ANSYS 10.0. Both results are compared and it is shown that both approaches coincide very close qualitatively and quantitatively although significant discrepancies may appear at specific points for specific cases. For all above three cases 3-D finite element models have been developed from the representative volume elements of the composite which are in the form of square unit cells. The finite element software ANSYS 10.0 has been successfully executed to evaluate the properties
The most interesting aim of the study is to assess and compare the dependability of
using the multiple linear regressions (MLR) model and the artificial neural networks
(ANN) model to predict the concrete compressive strength using metakaolin (MK) and
silica fume (SF) admixtures materials. A proposed prediction model of artificial neural
network (ANN) for concrete compressive strength. That proposed model is trained,
validated and tested using the available test data of 132 concretes with various mixture
proportions that were collected from different technical literature. Next the prediction
of concrete compressive strength is conducted on those models. The collected data
organized in a form of eight input variables (parameters) which includes concrete
specimen age, water, fine aggregate, metakaolin, cement, coarse aggregate, silica
fume, and superplasticizer. Relating to these input parameters in the ANN model, the
concrete compressive strength containing MK and SF, are predicted. The results from
the training, validation, and testing stages from making use of the ANN model showed
that neural networks (NN) have strong potential use for the prediction of concrete
compressive strength that contain materials such as MK and SF. The correlation
coefficient for the ANN model in the training, validation, and test stages that achieved
are equal to 0.99661, 0.99093, and 0.98577, respectively. Whereas the correlation
coefficient for the the MLR model was 0.794. The results suggest that the prediction
using ANN model is more accurate than when using the MLR model
Prediction of Deflection and Stresses of Laminated Composite Plate with Arti...IJMER
A true understanding of their structural behaviour is required, such as the deflections, buckling loads
and modal characteristics, the through thickness distributions of stresses and strains, the large deflection
behaviour and, of extreme importance for obtaining strong, reliable multi-layered structures, the failure
characteristics. In the past, the structural behaviour of plates and shells using the finite element method has been
studied by a variety of approaches. Choudhary and Tungikaranalyzed the geometrically nonlinear behavior of
laminated composite plates using the finite element analysis.
Modelling of next zen memory cell using low power consuming high speed nano d...eSAT Journals
Abstract Hybrid SET-CMOS circuits which syndicate the assets of both the SET [Single Electron Transistor] and CMOS depicts highest possibilities to be incorporated in practical implementation for future low power VLSI/ULSI configurations. The proposed work is an attempt based on SET-CMOS hybrid circuit to realize the next gen simple Memory Cell. The authors adhered to MIB model for SET and BSIM4 model for CMOS in realizing the complex cell. The maneuver of the proposed circuit is verified subsequently in standard environment. The outcomes are in good trade off with the conventional statistics of existing memory cell. Keywords: SET, SED, Hybrid CMOS-SET, MIB and Memory Cell
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
Effect of machining parameters on surface roughness for 6063 al tic (5 & 10 %...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.
Effect of machining parameters on surface roughness for 6063 al tic (5 & ...eSAT Journals
Abstract Metal matrix Composites are new class of material which offers superior Properties over alloys. Problem associated with MMCs is that they are very difficult to machine and quality of machining specially surface finish due to the hardness and abrasive nature of Carbide particles. Characteristics of machined surfaces are known to influence the product performance significantly since they are directly linked to the ability of the material to withstand stresses, temperature, friction and corrosion. This paper presents an experimental work on the analysis of machined surface quality on 6063 Al/TiC metal matrix composites with PCD insert in hard turning leading to Response surface methodology based model to predict the surface roughness.
Index Terms: Metal matrix composite, Surface Roughness, Response surface methodology.
In this paper, a developed three-dimensional Molecular Dynamics (MD) model for AFM-based
nanomachining is applied to study mechanical indentation and scratching at the nanoscale. The
correlation between the machining conditions, including applied force, depth, tip speed, and
defect mechanism in substrate/workpiece is investigeted. The simulations of nanoscratching
process are performed on different crystal orientations of single-crystal gold substrate, Au(100),
Au(110), and Au(111). The material deformation and deformed geometry are extracted from the
final locations of atoms, which are displaced by the rigid indenter. The simulation also allows
for the prediction of forces at the interface between the indenter and substrate. Material
properties including modulus of elasticity and hardness are estimated. It is found that properties
vary significantly at the nanoscale. In addition to the modeling, an AFM is used to conduct
actual indentation and scratching at the nanoscale, and provide measurements to which the MD
simulation predictions are compared. Due to computational time limitation, the predicted forces
obtained from MD simulation only compares well qualitatively with the experimental results.
International Journal of Engineering Research and Applications (IJERA) is a team of researchers not publication services or private publications running the journals for monetary benefits, we are association of scientists and academia who focus only on supporting authors who want to publish their work. The articles published in our journal can be accessed online, all the articles will be archived for real time access.
Our journal system primarily aims to bring out the research talent and the works done by sciaentists, academia, engineers, practitioners, scholars, post graduate students of engineering and science. This journal aims to cover the scientific research in a broader sense and not publishing a niche area of research facilitating researchers from various verticals to publish their papers. It is also aimed to provide a platform for the researchers to publish in a shorter of time, enabling them to continue further All articles published are freely available to scientific researchers in the Government agencies,educators and the general public. We are taking serious efforts to promote our journal across the globe in various ways, we are sure that our journal will act as a scientific platform for all researchers to publish their works online.
Number of Iteration Analysis for Complex FSS Shape Using GA for Efficient ESGjournalBEEI
ESG stand for Energy-Saving Glass is a special shielded glass with a metallic oxide layer to abuse undesirable of infrared and ultraviolet radiation into construction assemblies like our home. Firstly, different number of the iteration is the main thing to study a performance of the frequency selective surface shape using genetic algorithm (GA) for efficient energy saving glass (ESG). Three different values for the number of iterations were taken that is 1500, 2000 1nd 5000. Before that, the response of this complex FSS shape on incident electromagnetic wave with different symmetry shape are investigating. Three of them are no symmetrical shape, ¼ symmetrical shape, and 1/8 symmetrical shape. The 1500 number simulation considered about 89.000 per second, compared with 2000 iteration and 5000 iterations had consumed 105.09 per second and 196.00 per second, respectively. For 1/8 symmetry complex FSS shape, it demonstrations the improved performance of transmission loss at 1.2 GHz with - 40 dB. A 2 dB of transmission loss is achieved at WLAN application of 2.45 GHz with 0°, 30°, and 45° incidence angle shows
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.
Investigations on material casualty of plates under impact load conditionseSAT Journals
Abstract Impact problems are typical in nature since they involve geometry, boundary and material non-linearity. The impact of projectiles at sub-ordnance velocities against mild steel, stainless steel and aluminum plates has been studied. In this paper, target plates of 3mm thickness of materials Mild steel, Titanium are made to impact by Tungsten fragment with different velocities 300, 500, 700 and 1000 mm/ms. On impact, kinetic energy and residual velocity of this fragment is plotted to visualize the damage of the respective target plate. It is observed that the element size significantly affects the numerical results. Hence a sufficiently refined mesh is used. Kinetic energy is an essential parameter to be determined in order to study damage behavior of target. Higher the kinetic energy absorption, leads to higher is the damage target. So, higher initial velocities are required for the fragment in order to create the necessary damage in the target. Index Terms: Projectile, Target plate, Mild steel, Tungsten, Titanium
Micro Mechanical Modeling of Fiber / Epoxy Unidirectional Laminates Using Featheijes
The focus of the study was to develop the micromechanical model associated with proper damage model to predict the overall mechanical behavior of fiber/matrix unidirectional laminates. The present and first investigation studies the influence of fiber-matrix interface on the behaviour of fiber reinforced composite lamina using micromechanical models. Mechanical properties E1 and E2 are determined at various volume fractions. The second investigation studies the micro-thermo elastic behaviour of the square unit cell of a hybrid fiber reinforced composite lamina. Later this model is extended to predict the coefficients of thermal expansion of graphite-boron hybrid fiber reinforced lamina for various volume fractions.In the third investigation, an analytical solution of the thermal stresses for a fiber embedded in a matrix is presented based on the idea of the finite element and under some simplifying assumptions. The analytical solution to the problem is found for the case when the length of the embedded bar (fiber) is much greater than its radius, and the Young's modulus of the matrix is much less than that of the fiber. The problem is also solved numerically by means of finite element analysis using ANSYS 10.0. Both results are compared and it is shown that both approaches coincide very close qualitatively and quantitatively although significant discrepancies may appear at specific points for specific cases. For all above three cases 3-D finite element models have been developed from the representative volume elements of the composite which are in the form of square unit cells. The finite element software ANSYS 10.0 has been successfully executed to evaluate the properties
The most interesting aim of the study is to assess and compare the dependability of
using the multiple linear regressions (MLR) model and the artificial neural networks
(ANN) model to predict the concrete compressive strength using metakaolin (MK) and
silica fume (SF) admixtures materials. A proposed prediction model of artificial neural
network (ANN) for concrete compressive strength. That proposed model is trained,
validated and tested using the available test data of 132 concretes with various mixture
proportions that were collected from different technical literature. Next the prediction
of concrete compressive strength is conducted on those models. The collected data
organized in a form of eight input variables (parameters) which includes concrete
specimen age, water, fine aggregate, metakaolin, cement, coarse aggregate, silica
fume, and superplasticizer. Relating to these input parameters in the ANN model, the
concrete compressive strength containing MK and SF, are predicted. The results from
the training, validation, and testing stages from making use of the ANN model showed
that neural networks (NN) have strong potential use for the prediction of concrete
compressive strength that contain materials such as MK and SF. The correlation
coefficient for the ANN model in the training, validation, and test stages that achieved
are equal to 0.99661, 0.99093, and 0.98577, respectively. Whereas the correlation
coefficient for the the MLR model was 0.794. The results suggest that the prediction
using ANN model is more accurate than when using the MLR model
Prediction of Deflection and Stresses of Laminated Composite Plate with Arti...IJMER
A true understanding of their structural behaviour is required, such as the deflections, buckling loads
and modal characteristics, the through thickness distributions of stresses and strains, the large deflection
behaviour and, of extreme importance for obtaining strong, reliable multi-layered structures, the failure
characteristics. In the past, the structural behaviour of plates and shells using the finite element method has been
studied by a variety of approaches. Choudhary and Tungikaranalyzed the geometrically nonlinear behavior of
laminated composite plates using the finite element analysis.
Modelling of next zen memory cell using low power consuming high speed nano d...eSAT Journals
Abstract Hybrid SET-CMOS circuits which syndicate the assets of both the SET [Single Electron Transistor] and CMOS depicts highest possibilities to be incorporated in practical implementation for future low power VLSI/ULSI configurations. The proposed work is an attempt based on SET-CMOS hybrid circuit to realize the next gen simple Memory Cell. The authors adhered to MIB model for SET and BSIM4 model for CMOS in realizing the complex cell. The maneuver of the proposed circuit is verified subsequently in standard environment. The outcomes are in good trade off with the conventional statistics of existing memory cell. Keywords: SET, SED, Hybrid CMOS-SET, MIB and Memory Cell
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
Effect of machining parameters on surface roughness for 6063 al tic (5 & 10 %...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.
Effect of machining parameters on surface roughness for 6063 al tic (5 & ...eSAT Journals
Abstract Metal matrix Composites are new class of material which offers superior Properties over alloys. Problem associated with MMCs is that they are very difficult to machine and quality of machining specially surface finish due to the hardness and abrasive nature of Carbide particles. Characteristics of machined surfaces are known to influence the product performance significantly since they are directly linked to the ability of the material to withstand stresses, temperature, friction and corrosion. This paper presents an experimental work on the analysis of machined surface quality on 6063 Al/TiC metal matrix composites with PCD insert in hard turning leading to Response surface methodology based model to predict the surface roughness.
Index Terms: Metal matrix composite, Surface Roughness, Response surface methodology.
In this paper, a developed three-dimensional Molecular Dynamics (MD) model for AFM-based
nanomachining is applied to study mechanical indentation and scratching at the nanoscale. The
correlation between the machining conditions, including applied force, depth, tip speed, and
defect mechanism in substrate/workpiece is investigeted. The simulations of nanoscratching
process are performed on different crystal orientations of single-crystal gold substrate, Au(100),
Au(110), and Au(111). The material deformation and deformed geometry are extracted from the
final locations of atoms, which are displaced by the rigid indenter. The simulation also allows
for the prediction of forces at the interface between the indenter and substrate. Material
properties including modulus of elasticity and hardness are estimated. It is found that properties
vary significantly at the nanoscale. In addition to the modeling, an AFM is used to conduct
actual indentation and scratching at the nanoscale, and provide measurements to which the MD
simulation predictions are compared. Due to computational time limitation, the predicted forces
obtained from MD simulation only compares well qualitatively with the experimental results.
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
Electrical discharge machining of the composites a literature revieweSAT 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
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
Study on the effects of ceramic particulates (sic, al2 o3 and cenosphere) on ...eSAT Journals
Abstract This paper investigates the sliding wear behaviour of three different composites. Three different reinforcements are under taken for this study namely SiC, Al2O3 and Cenosphere. Along with it percentage reinforcement is also varied from 8wt% to 16wt.%. Other factors applied normal load and sliding speed are also considered. Taguchi design of experimental technique is employed for the study of sliding wear. It is observed that SiC reinforced composites show better wear resistance than Al2O3 and Cenosphere reinforced composites. Regression and artificial neural network (ANN) is used to develop a model to predict the wear loss. It is observed that artificial neural network is more efficient than regression. Keywords: A. Metal-matrix composites (MMCs); B.Wear; C.Taguchi D. Nueral Network
Optimization of Surface Roughness for EN 1010 Low Alloy Steel on WEDM Using R...IJAEMSJORNAL
The term steel is used for many different alloys of iron. All steels cover small amounts of carbon and manganese. There do exist many types of steels which are(among others) plain carbon steel, stainless steel, alloysteel and tool steel. Carbon steel is the most extensively used kind of steel. The properties of carbon steel depend mainly on the amount of carbon it contains. Maximum carbon steel has a carbon content of less than 1%. Carbon steel is made into an extensive range of products, including structural beams, car bodies. In fact, there are 3 types of plain carbon steel namely low carbon steel, medium carbon steel, high carbon steel. It is good to exact that plain carbon steel is a type of steel having a maximum carbon content of 1.5% along with small percentages of silica, Sulphur, phosphorus and manganese. EN 1010 is a lowest amount of carbonalloy steel alloy with carbon content of 0.10%. Machineability of EN 1010 carbon steel is measured to be fairly good. EN 1010 is usually used for rivets and bolts, construction and automotive applications such as pans, nails and transmission cover. The objective of paper is to study the effect of process parameters namely pulse on time, pulse off time, peak current and servo voltage on surface roughness(SR).The effect of process parameters on productivity and accuracy facts is material dependent. To study parametric effect on Surface Roughness a Central Composite design approach of response surface methodology (RSM) is used to plan and study the experiments. The mathematical relationships between WEDM input process parameters and response parameter namely surface roughness is established to determine optimal values of surface roughness mathematically and graphically.The Analysis of variance (ANOVA) is performed to find statistically significant process parameters. Interaction effects of process parameters on surface roughness are analysed using statistical and graphical representations.
Analysis of Conditions in Boundary Lubrications Using Bearing MaterialsIJMER
In order to clearly establish the tribological potential of these alloys as bearing materials, the tribological parameters of the RAR Zn-Al alloys are compared to parameters of the CuPb15Sn8 lead-tin bronze, as a widely applied conventional bearing material. Existing Bearing of connecting rod is manufactured by using non ferrous materials like Gunmetal, Phosphor Bronze etc.. This paper describes the tribological behavior analysis for the conventional materials i.e. Brass and Gunmetal as well as New non metallic material Cast Nylon. Friction and Wear are the most important parameters to decide the
performance of any bearing. In this paper attempt is made to check major tribological parameters for three material and try to suggest better new material compared to conventional existing material. It could help us to minimize the problem of handling materials like Lead , Tin, Zinc etc.After Test on wear machine. Our experimental results are accessing efficient processing in bearing conditions in semantic data representation of extracted related data materials
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.
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.
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 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.
The deposition of the flow of suspended particles has always been a problematic case in the process of flow transmission through sewers. Deposition of suspended materials decreases transmitting capacity. Therefore, it is necessary to have a method capable of precisely evaluating the flow velocity in order to prevent deposition. In this paper, using Gene-Expression Programming, a model is presented which properly predicts sediment transport in the sewer. In order to present Gene-Expression Programming model, firstly parameters which are effective on velocity are surveyed and considering every of them, six different models are presented. Among the presented models the best is being selected. The results show that using verification criteria, the presented model presents the results as Root Mean Squared Error, RMSE=0.12 and Mean Average Percentage Error, MAPE=2.56 for train and RMSE=0.14 and MAPE=2.82 for verification. Also, the model presented in this study was compared with the other existing sediment transport equations which were obtained using nonlinear regression analysis.
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.
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.
More from Journal of Soft Computing in Civil Engineering (6)
Explore the innovative world of trenchless pipe repair with our comprehensive guide, "The Benefits and Techniques of Trenchless Pipe Repair." This document delves into the modern methods of repairing underground pipes without the need for extensive excavation, highlighting the numerous advantages and the latest techniques used in the industry.
Learn about the cost savings, reduced environmental impact, and minimal disruption associated with trenchless technology. Discover detailed explanations of popular techniques such as pipe bursting, cured-in-place pipe (CIPP) lining, and directional drilling. Understand how these methods can be applied to various types of infrastructure, from residential plumbing to large-scale municipal systems.
Ideal for homeowners, contractors, engineers, and anyone interested in modern plumbing solutions, this guide provides valuable insights into why trenchless pipe repair is becoming the preferred choice for pipe rehabilitation. Stay informed about the latest advancements and best practices in the field.
Final project report on grocery store management system..pdfKamal Acharya
In today’s fast-changing business environment, it’s extremely important to be able to respond to client needs in the most effective and timely manner. If your customers wish to see your business online and have instant access to your products or services.
Online Grocery Store is an e-commerce website, which retails various grocery products. This project allows viewing various products available enables registered users to purchase desired products instantly using Paytm, UPI payment processor (Instant Pay) and also can place order by using Cash on Delivery (Pay Later) option. This project provides an easy access to Administrators and Managers to view orders placed using Pay Later and Instant Pay options.
In order to develop an e-commerce website, a number of Technologies must be studied and understood. These include multi-tiered architecture, server and client-side scripting techniques, implementation technologies, programming language (such as PHP, HTML, CSS, JavaScript) and MySQL relational databases. This is a project with the objective to develop a basic website where a consumer is provided with a shopping cart website and also to know about the technologies used to develop such a website.
This document will discuss each of the underlying technologies to create and implement an e- commerce website.
About
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Technical Specifications
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
Key Features
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface
• Compatible with MAFI CCR system
• Copatiable with IDM8000 CCR
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
Application
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Sachpazis:Terzaghi Bearing Capacity Estimation in simple terms with Calculati...Dr.Costas Sachpazis
Terzaghi's soil bearing capacity theory, developed by Karl Terzaghi, is a fundamental principle in geotechnical engineering used to determine the bearing capacity of shallow foundations. This theory provides a method to calculate the ultimate bearing capacity of soil, which is the maximum load per unit area that the soil can support without undergoing shear failure. The Calculation HTML Code included.
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.
Water scarcity is the lack of fresh water resources to meet the standard water demand. There are two type of water scarcity. One is physical. The other is economic water scarcity.
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.
2. R. Mikaeil et al./ Journal of Soft Computing in Civil Engineering 1-2 (2017) 52-69 53
1. Introduction
There are several methods in dimension stone block. Nowadays, diamond wire saw with bead
diamond is the most widely used process for dimension stone quarries.
In diamond wire cutting operations, the cutting action primarily includes the pulling of
continuous loops of spinning wire mounted with diamond beads through the dimension stone. In
this cutting operation, firstly the horizontal cutting is done for avoiding the gravity effect of
dimension stone block. Then, vertical cuttings are done. The initial step for a vertical cutting is to
drill two holes that intersect at a 90° angle. Then, the diamond wire threaded through these holes,
and over the drive wheel, clamps at the two ends to form a continuous loop. The diamond wire
cutting machine is mounted on a temporary track, along which it reverses to maintain tension in
the wire as it cuts through the stone. Water is applied with the spin direction of the wire as a
coolant and as a means of removing the particles of stone [1].
Diamond wire saw wear in rock cutting is one of the major criteria in determining the diamond
wire saw life, energy consumption, production cost, and determine the cutting method selected
for a given rock type. There are some important factors, which need to be considered to evaluate
the wear rate of diamond wire saw. These factors can be divided into three key categories: (1) the
characteristics of the diamond wire saw, (2) the operating parameters and (3) the characteristics
of the cut rock. Many researchers have attempted to investigate the effect of these parameters on
wear up to now. Some researchers have studied the wear of circular diamond saw blade, and
diamond wire saw in rock cutting process [1–12]. In the field of diamond, wire saw wear,
Özçelik et al. [1] studied the effects of textural properties on marble cutting with diamond wire.
They evaluated the relationships between textural characteristics and wore rate. The results
showed that decreasing grain size increases the wear rate. Also, there is a significant relationship
between the texture coefficients and wear on diamond beads. This study indicated that textural
characteristics could be considered in the selection and design of diamond beads in marble
industry. Özçelik and Kulaksız studied the relationship between cutting angles and wear on
diamond beads in diamond wire cutting process [11]. Özçelik et al. investigated the wear rate of
diamond beads in the cutting of different rock types. They used the ridge regression method to
evaluate the wear of beads in the cutting of different rock with different physical, chemical,
mechanical and mineralogical-petrographical properties. They concluded that the developed
statistical models could be used to determine diamond wire life and to cut efficiency [12].
Similarly, Özçelik applied the multivariate statistical analysis of the wear on diamond beads in
the cutting of andesitic rocks according to physical and mechanical properties of rock [3].
Mikaeil et al. predicted the wear of diamond wire saw concerning the uniaxial compressive
3. 54 R. Mikaeil et al./ Journal of Soft Computing in Civil Engineering 1-2 (2017) 52-69
strength, Schimazek F-abrasivity factor, Shore hardness, and Young's modulus using the
harmony search algorithm. The results showed that the applied algorithm could be used to
evaluate the wear of diamond bead [4]. Almasi et al. carried out an investigation for the 11 types
of igneous rocks based on the rock properties and production rate. For this purpose, they used
linear and nonlinear regressions for analysis. The results indicated that the developed model
could be a suitable system to predict the wear rate of diamond beads [13]. Mikaeil et al.,
investigated different carbonate rocks in some famous quarries located in Iran, according to some
important mechanical and physical properties of stone such as elasticity modulus, similar quartz
content and uniaxial compressive strength. They used the application of multivariate regression
analysis to evaluate the performance of diamond wire saw [14].
All of these studies were simply studied the diamond bead wear with statistical analysis and
metaheuristic algorithm. No study has been found relating to the influence of rock characteristics
on the diamond bead wear rate in diamond wire sawing with soft computing such as artificial and
intelligence algorithm. In this research, it is aimed to develop an accurate, practical and
applicable model for predicting the wear rate of diamond bead based on rock properties using
intelligent systems. The remainder of this paper is organized as follows. In Section 2, the
methodology is briefly summarized. Section 3 presents the rock properties and laboratory testing
of the case study. In Section 4, the development of the MLP and a combination of GA-ANN
models for wear rate prediction are explained. Section 5 discusses and assesses the results and
performances of moldings. Finally, Section 6 gives conclusions and recommendations for future
work.
2. Methodology
The methodology of this study is organized as following steps.
Step 1: Quarries studies (Cutting of dimension stone with a diamond wire saw and determination
of wear rate and sampling of stone blocks)
Step 2: Laboratory studies (Preparation of cylindrical specimens from stone samples and
determination of physical and mechanical properties)
Step 3: Investigation of the relationship between wear rate and characteristics of rock with GA-
ANN and MLP
Step 4: Evaluation of results
A flowchart followed in this study is illustrated in Figure 1.
4. R. Mikaeil et al./ Journal of Soft Computing in Civil Engineering 1-2 (2017) 52-69 55
Fig. 1. Flowchart of study
3. Quarries and laboratory studies
This study has been performed at 38 quarries including a different type of rocks including
andesites, limestones and real marbles in eleven areas in Turkey (Table 1). The extractions of
andesite, limestones and real marble blocks have been achieved by diamond wire sawing. Wear
rates of diamond beads for any rock types from 38 different localities have been recorded.
After quarries studies, experimental studies were done on rock block samples. To determine the
main physical and mechanical properties of studied rocks, laboratory studies were done. Uniaxial
compressive strength (UCS), Shore hardness (SH), Young modulus (YM) and Schmiazek F-
abrasivity (SF-a) were selected as major rock characteristics. Results of laboratory tests for
studied rock and quarries studies are given in Table (1).
Start
Quarries studies Laboratory studies
Cutting of dimension stone with
diamond wire saw
Determination of wear rate Sampling of stone blocks
Preparation of cylindrical
specimens from stone samples
Determination of physical and
mechanical properties
Investigation of relationship between
wear rate and characteristics of rock
Genetic Algorithm-Artificial
Neural Network
Multilayer Perceptron
Evaluation of results
6. R. Mikaeil et al./ Journal of Soft Computing in Civil Engineering 1-2 (2017) 52-69 57
their performances are conducted and discussed. Some statistical functions indices, i.e., root
mean square error (RMSE), the coefficient of determination (R2
) and standard deviation (STD)
were computed to check for assessment and evaluating the accuracy and performance capacity of
models as shown in Eqs.1 to 4, respectively.
2
1
2 2
m
2 1 1
2
m
1
1
1
( ) (1)
[ ( ) ] [ ( ) ]
(2)
[ ( ) ]
1
[ ] (3)
n
i i
i
n n
i ean i i
i i
n
i ean
i
n
i i i i
i i i
RMSE x y
n
x x x y
R
x x
x y x y
STD mean
n x x
Where n explains the number of data sets. The yi and xi are the forecasted and measured wear
rate values, respectively. Note that, in modeling with high and acceptable accuracy, the values of
RMSE, R2,
and STD should be close to 0, 1 and 0, respectively.
4.1. Multilayer perceptron (MLP)
The soft computing acts as a huge incentive to solve complex problems [15–17]. Artificial neural
networks have a special place among soft computing methods considering their high ability in
complex and imprecise data analysis and processing. The performance of the human brain and
neural systems considering million years of evolution can be used as the most complete and
efficient pattern for the recognition of the surrounding events. In recent decades, neural networks
have had a great impact on the development and modeling of industrial problems, as well as the
control and optimization of the production process. One of the most practical and appropriate
types of neural networks is the multilayer perceptron network used with a special type of
learning algorithm in optimization problems. In a multilayer perceptron network, the linear
relationship between input and output vectors is shown through connections between neurons in
each node and previous and next layers. The weight of network is determined through the
minimum error between input and output data and or through the end of a number of teachings to
a predetermined value [18,19]. Different methods are used for teaching artificial neural networks,
among which the backpropagation algorithm is one of the most efficient and appropriate methods
for teaching the multi-layer perceptron neural network and has the maximum consistency with
this network. Therefore, for learning weights of a multi-layer perceptron network, the back
propagation rule is used. This method was proposed by Wiliams Rumelhalt in 1986. In this
method, using the gradient descent, it is attempted to minimize the square error between network
outputs and objective function [20,21]. In fact, the error produced by the comparison between
output data and estimated data must be smaller than the mean square error (MSE), or the root
mean square error (RMSE); otherwise, the network must be propagated back in order to correct
weights and reduce errors. The computation of output sensitivity to weights is started from the
end of the network, and finally, weights are updated at once. The network’s output error is
computed in Eq (5) based on BP:
7. 58 R. Mikaeil et al./ Journal of Soft Computing in Civil Engineering 1-2 (2017) 52-69
2
1
( ) ( )
2
kd kd
d D k outputs
E W t o
(5)
where ( )
E W is the total output error, d is training samples, k is output data set, kd
t and kd
o
are kth value of the objective function (corresponding to the kth output unit) for dth training
sample and kth value of output function (corresponding to the kth output unit) for dth training
sample, respectively.
4.1.1. MLP modeling
In this work, Multilayer Perceptron (MLP) is utilized to build a prediction model for the
assessment of diamond wire saw performance from the samples of stones as data set using the
MATLAB software. The same datasets are used in three simulations in this study. Wear rate was
considered as the dependent variable (output), and the uniaxial compressive strength (UCS),
Schmiazek F-abrasivity (SF-a), Shore hardness (SH), and Young's modulus (YM) were
considered as the independent variables (input). The dataset of 38 different varieties of
dimension stones from Turkey quarry mines is considered in the current study, while 26 data
points (70%) are utilized for constructing the model as train data, 8 data points (20%) are used as
test data and the rest data points (4 data points) are considered as validation data for evaluation
of the degree of accuracy and robustness.
The number of hidden layers and the number of neurons in each hidden layers are two important
factors in the MLP structure. Hence; in this modeling by the contribution of experimental
equations and after several simulations conducted, Ni =4 and N0 =1 are considered for the
number of input neuron and number of output neuron, respectively. Furthermore, hidden layer
size is used as a range of 1-10 with one hidden layer for more accurate computing. Some of these
equations are shown in Table (2).
Table 2
The equations for determining the number of neuron in the hidden layer [22].
Researchers Heuristic
Hecht-Nielsen [23] 2 1
i
N
Hush [24] 3 i
N
Kaastra and Boyd [25]
Kannellopoulas and Wilkinson [26]
2 i
N
Ripley [27] 0
( ) / 2
i
N N
Paola [28]
2
0 0 0
0
2 0.5 ( ) 3
i i
i
N N N N N
N N
Wang [29] 2 / 3
i
N
Masters [30] 0
i
N N
Ni: Number of input neuron, N0: Number of the output neuron
8. R. Mikaeil et al./ Journal of Soft Computing in Civil Engineering 1-2 (2017) 52-69 59
Different studies are conducted about conventional gradient descent techniques. Levenberg–
Marquardt (LM) is one of the most effective and accurate algorithm based on the suggestion of
Hagan and Menhaj [31]. Therefore, Levenberg–Marquardt (LM) learning algorithm is
considered in constructing MLP models for training net. In this study, the tansig and purelin are
considered as transfer functions of the hidden layers and output, respectively. The effects of
hidden layer size on the results of RMSE, R2,
and STD. are shown in Tables (3), and ranking of
each model are listed in Table (4) based on a simple ranking method [32,33].
Table 3
Effects of hidden layer size on statistical functions performance in MLP network.
Model
No.
Hidden
Layer Size
(HLS)
The Results of Network for R2 The Results of Network for
RMSE
The Results of Network for
STD.
Training Validation Testing Training Validation Testing Training Validation Testing
1 1 0.54 0.99 0.7 0.0026 0.0021 0.0048 0.0027 0.0024 0.0042
2 2 0.73 0.81 0.56 0.0027 0.0028 0.0018 0.0027 0.0031 0.0019
3 3 0.7 0.21 0.14 0.0028 0.0042 0.0023 0.0029 0.0044 0.0018
4 4 0.8 0.84 0.8 0.0021 0.0036 0.003 0.0021 0.0039 0.0028
5 5 0.83 0.78 0.73 0.002 0.0037 0.0023 0.002 0.0031 0.0023
6 6 0.96 0.97 0.54 0.0011 0.0028 0.0049 0.0011 0.0022 0.0042
7 7 0.76 0.84 0.77 0.0024 0.0022 0.0027 0.0024 0.0025 0.0025
8 8 0.95 0.55 0.92 0.0011 0.0035 0.0021 0.0011 0.0038 0.0017
9 9 0.7 0.92 0.88 0.0028 0.0011 0.0022 0.0028 0.0011 0.0021
10 10 0.67 0.99 0.91 0.0027 0.0018 0.002 0.0026 0.002 0.0021
Table 4
Ranking of each model using MLP network.
Model
No.
Hidden
Layer Size
(HLS)
The Ranking of Network for R2 The Ranking of Network for
RMSE
The Ranking of Network for
STD. Total
rank
Training Validation Testing Training Validation Testing Training Validation Testing
1 1 2 10 4 6 8 3 5 7 3 48
2 2 5 6 3 5 6 10 5 5 8 53
3 3 4 3 1 4 2 6 3 2 9 34
4 4 7 7 6 8 4 4 8 3 5 52
5 5 8 5 5 9 3 6 9 5 7 57
6 6 10 9 2 10 6 2 10 8 3 60
7 7 6 7 7 7 7 5 7 6 6 58
8 8 9 4 10 10 5 8 10 4 10 70
9 9 4 8 8 4 10 7 4 10 4 59
10 10 3 10 9 5 9 9 6 9 4 64
Furthermore, Fig (2) shows a correlation of determination between measured and predicted wear
rate that there is a reasonable R2
with a coefficient higher than 0.88. Figs(3) and (4) present
RMSE values and the histograms of errors for training, validation, and testing steps for all
datasets in the eighth model using MLP, respectively. According to the statistical functions and
9. 60 R. Mikaeil et al./ Journal of Soft Computing in Civil Engineering 1-2 (2017) 52-69
their total rank in Table (4), the model number 8 indicates higher performance capacities
compared to other models with hidden layer size of 8 and a total rank of 70.
Fig. 2. R2
of predicted and measured WR values for all data set using the MLP model.
Fig. 3. RMSE values for training, validation and testing steps.
10. R. Mikaeil et al./ Journal of Soft Computing in Civil Engineering 1-2 (2017) 52-69 61
Fig. 4. Histograms of errors for all data set using the MLP model
4.2. Hybrid GA-ANN algorithm
In recent years, Meta-heuristic algorithms have attracted the interest of researcher in many
engineering fields and industry. These algorithms are the precise scientific tools instead of
statistical methods to deal with the uncertain systems [34–36]. A genetic algorithm is a
population-based algorithm like particle swarm optimization, firstly proposed by John Holland in
1975 at the University of Michigan [37]. The GA is a Meta-heuristic algorithm that is suitable for
dealing with complex problems, especially when the goal is to find an optimization result. The
genetic algorithm can model the qualitative and quantitative aspects of uncertain systems in the
industry. The genetic algorithm (GA) was inspired by Darwin's principle of natural evolution.
The optimization and searching method in this algorithm is based on the principle of natural
biological evolution and inheritance rules.
In the genetic algorithm, numbers are expressed in terms of binary strings and converged toward
the range of solutions during the stepwise algorithm’s implementation using the probability
distribution function. The population to population searching is a technique for obtaining an
optimal solution. Also, in problems with a complex hypothesis space with the unknown effect of
components on the general hypothesis, GA can be used for searching and finding an approximate
solution for an optimal answer. GA has a significant flexible nature compared to other Meta-
heuristic algorithms and is formed based on the natural selection mechanism and stochastic
techniques. Furthermore, in this algorithm, differentiation is not required, and only the objective
function and basic information fitting methods are used. In GA, each set of chromosomes and
each replication of algorithm are called population and generation, respectively. GA first fits the
existing population in each replication by determining the initial population and using the fitness
11. 62 R. Mikaeil et al./ Journal of Soft Computing in Civil Engineering 1-2 (2017) 52-69
function and then starts optimization. In fact, the compatibility of the initial population is
computed and assessed through the objective function. Next, the new generation (population) is
produced based on GA operators, i.e., reproduction, crossover, and mutation. The fitness steps
for answers and production of new generations continue until an optimal answer is reached. GA
has a wide range in the optimization and solution of complex and uncertain problems. The
landslide was evaluated and studied by Terranova et al. The results and the subsequent validation
showed that the genetic-algorithms-based hydrological model was a reliable approach for their
research. [38]. The flood risk management was done by Woodward et al. using a multi-objective
genetic algorithm. The results indicated that the simulations were very suitable [39].
Also, one of the most important applications of GA is teaching neural networks. Since GA can
run away from trapping in local optimums, does not depend on any special structure of the
network and is applied for any defined structure, it can be considered as a proper and efficient
tool for being combined with neural networks and teaching neural networks. Therefore, in this
research, the wear rate is anticipated using a GA and ANN combination for optimizing the
performance of diamond wire saw. Figure (5) shows a combination of GA-ANN structure.
Fig. 5. The structure of the hybrid GA-ANN flowchart [40]
In recent years, different studies were conducted in different scientific areas using a hybrid GA-
ANN algorithm. Armaghani and Khandelwal proposed a model for the anticipation of the drilling
rate index using GA and neural network and used on the rock strength characteristics [41]. Good
anticipation of the pile bearing capacity was done by Momeni et al. by developing a hybrid
algorithm model. In their study, they obtained answers with very good accuracy by combining
GA and ANN [42]. A model for the anticipation of flyrock and back break in open pit mines was
proposed by Monjezi et al. based on GA-ANN, and a model with high efficiency was developed
for anticipation with the minimum possible error and the maximum correlation coefficient [43].
12. R. Mikaeil et al./ Journal of Soft Computing in Civil Engineering 1-2 (2017) 52-69 63
4.2.1. Hybrid GA-ANN algorithm modeling
As mentioned, the predicting diamond wire saw performance in this study is based on four
important measures of rocks which the same datasets performed in the assessment of MLP
simulations were applied. A hybrid GA-ANN algorithm is considered as a flexible predicting
method. This technique is based on artificial intelligence for solving complex issues and
uncertain systems, which is one of the most efficient soft computing methods. Hagan and
Menhaj (1994) introduced more details for the hybrid GA-based ANN model [31].
In order to obtain a high level of precision in data analysis and predicting process, it is necessary
to determine the appropriate control parameters. Hence; firstly the pseudo-code of the hybrid
GA-ANN algorithm is written in MATLAB software. Some parameters can define based on
visual observations and suggestion of previous studies [31,44]. The recombination percent (RP)
was determined at 15%, the mutation percent (MP) and cross-over percent (CP) were fixed at
35% and 50%. The maximum number of generation (GMax) and population size are two of the
effective factors in during algorithm implementation process. In the next step, in order to obtain
the optimum GMax value, the efficacy of the number of generation on the network performance
for RMSE as statistical functions is carried out for deferent population size as a range of 50-500
with GMax = 500. The result of the analysis is illustrated in Fig (6). Based on the results, it is
obvious that the optimum GMax was set to be 400 because the network performance is unchanged
after this value of GMax. Furthermore, the ANN structure is determined based on Table (2).
Fig. 6. The efficacy of the number of generation on the network performance based on RMSE
In the final step, in order to determine the optimum population size in GA-ANN algorithm, 10
hybrids GA-ANN models are constructed for the optimum GMax = 400, the results of root mean
square error (RMSE), coefficient of determination (R2
) and standard deviation (STD) are listed
in Table (5). The models are ranked according to the suggestion of Zorlu et al. (2008) as a simple
ranking approach [33].
13. 64 R. Mikaeil et al./ Journal of Soft Computing in Civil Engineering 1-2 (2017) 52-69
Table 5
Effects of population size on statistical functions performance in Hybrid GN-ANN algorithm.
Model
No.
Population
Size
The Results of Network
for R2
The Results of Network for
RMSE
The Results of Network for
STD.
Training Testing Training Testing Training Testing
1 50 0.89 0.25 0.168 0.489 0.459 0.435
2 75 0.69 0.64 0.261 0.375 0.376 0.384
3 100 0.67 0.81 0.2 0.55 0.264 0.337
4 150 0.64 0.62 0.299 0.357 0.389 0.375
5 200 0.68 0.68 0.31 0.264 0.418 0.442
6 250 0.89 0.24 0.174 0.495 0.443 0.389
7 300 0.75 0.56 0.264 0.41 0.467 0.595
8 350 0.7 0.37 0.297 0.417 0.444 0.447
9 400 0.65 0.7 0.276 0.348 0.374 0.407
10 500 0.49 0.69 0.319 0.318 0.455 0.467
The results of the ranking shown in Table (6). As shown in Table (6), the third model has the
highest rank among other models with the rank of 44. Therefore, Figs (7) and (8) illustrate the
value of R2
and the graphical comparison between measured and predicted wear rate using a
hybrid model for test data set of the third simulation, respectively.
Table 6
Ranking of each model using Hybrid GN-ANN algorithm.
Model
No.
Population
Size
The Ranking of
Network for R2
The Ranking of Network
for RMSE
The Ranking of Network
for STD.
Total
rank
Training Testing Training Testing Training Testing
1 50 10 2 10 3 2 5 32
2 75 7 6 7 6 8 8 42
3 100 5 10 8 1 10 10 44
4 150 3 5 3 7 7 9 34
5 200 6 7 2 10 6 4 35
6 250 10 1 9 2 5 7 34
7 300 9 4 6 5 1 1 26
8 350 8 3 4 4 4 3 26
9 400 4 9 5 8 9 6 41
10 500 2 8 1 9 3 2 25
14. R. Mikaeil et al./ Journal of Soft Computing in Civil Engineering 1-2 (2017) 52-69 65
Fig. 7. R2
between measured and predicted wear rate using hybrid GA-ANN model for Test data.
Fig. 8. The graphical comparison between measured and predicted wear rate using hybrid GA-ANN
model for test data.
5. Evaluation of the results and discussion
Assessment of diamond wire saw performance had been considered as one of the most notable
topics to study in mining engineering and rock mechanics. Investigation of effective parameters
on the diamond wire saw performance is frequently encountered with complex and non-linear
problems; hence, soft computing techniques are the approaches that are suitable for dealing with
complex and uncertain processes. In this research, in order to investigate the applications and
performances of each optimization technique for predicting diamond wire saw performance, after
implementation simulations, the most appropriate structures of MLP and hybrid GA-ANN model
were determined. Based on the optimum models, all the datasets were randomly selected to 4
various datasets. Also, 80% of samples were used randomly for the training dataset, and the
remaining 20 % was considered as testing data in each simulation. In Tables (7), the results of
15. 66 R. Mikaeil et al./ Journal of Soft Computing in Civil Engineering 1-2 (2017) 52-69
R2
, RMSE, and STD. of ANN and GA-ANN methods and final ranking are shown. Finally, Table
(8) listed shows the final ranking of all simulations.
Table 7
Ranking of each model for 4 datasets randomly selected using MLP and hybrid GN-ANN.
Optimization
Techniques
Model
No.
Value of
R2
Score of
R2
Value of
RMSE
Score of
RMSE
Value of
STD.
Score of
STD.
Total
Score
MLP
Train 1. 0.8 3 0.0014 3 0.0016 3 9
Train 2. 0.88 4 0.001 4 0.001 4 12
Train 3. 0.62 1 0.0034 2 0.0034 2 5
Train 4. 0.65 2 0.0049 1 0.0048 1 4
Test 1. 0.91 4 0.0013 4 0.0015 4 12
Test 2. 0.77 2 0.0044 2 0.0017 3 7
Test 3. 0.81 3 0.006 1 0.007 1 5
Test 4. 0.5 1 0.004 3 0.0033 2 6
GA-ANN
Train 1. 0.968 3 0.11 4 0.88 1 8
Train 2. 0.95 4 0.15 3 0.54 4 11
Train 3. 0.95 4 0.18 2 0.73 3 9
Train 4. 0.889 2 0.31 2 0.74 2 6
Test 1. 0.652 2 0.31 4 0.55 2 8
Test 2. 0.991 4 0.61 1 0.53 3 8
Test 3. 0.601 1 0.43 3 0.11 4 8
Test 4. 0.881 3 0.56 2 0.6 1 6
Table 8
The final ranking of each model using two optimization techniques.
Optimization Techniques Simulation No. Total Score
MLP
1 21
2 19
3 10
4 10
Hybrid
GA-ANN
1 16
2 19
3 17
4 12
Based on the results of Tables (7) and (8), the first model in MLP simulation and the second
model in GA-ANN simulation obtained the highest score according to statistical functions. The
first MLP simulation has the highest total rank with 21 among other MLP simulations. Also, the
second GA-AAN simulation obtained the most score in other GA-ANN simulations with
rank=19. In comparison between the best MLP and hybrid GA-ANN simulations, the high and
reasonable R2
values between the model predictions and the measured data for training = 0.95
and testing =0.991 using the hybrid GA-ANN method describes its high capability in the
prediction of diamond wire saw performance.
16. R. Mikaeil et al./ Journal of Soft Computing in Civil Engineering 1-2 (2017) 52-69 67
6. Conclusion
In this research, the aim is to develop prediction models for assessment of diamond wire saw
performance using two optimization approaches including MLP and hybrid GA-ANN algorithm.
This work compared the application and performance of the hybrid GA-ANN algorithm with
MnLR on the database of 38 different varieties of dimension stones from Turkey quarry mines.
Four important physical and mechanical rock characteristics on the cutting process, namely wear
rate from diamond wire cutting machine was set as output data, and uniaxial compressive
strength, Schmiazek F-abrasivity, Shore hardness, and Young's modulus were considered as input
data set. From the results found in this study, it can be concluded that the performance of the
hybrid GA-ANN algorithm is superior to MLP in terms of some model performance indices such
as RMSE, R2,
and STD. The comparison was made between the three simulations based upon the
performance indices, hybrid GA-ANN algorithm with a coefficient of determination (R2
) of
training = 0.95 and testing = 0.991 was selected as the best predictive model. Also, it
outperforms MLP based on robustness and solution quality for simulation some problems
involved in rock mechanics engineering. From what has been discussed above, it can be
concluded that hybrid GA-ANN algorithm is a reliable system simulation technique for
predicting the performance of diamond bead with the highly acceptable level of accuracy and it
can be applied as an appropriate alternative which has a wide application in management and
planning for costs and designs of quarries. In future research, prediction of diamond wire saw
performance can also be investigated and improved the using ICA-ANN, PSO-ANN, Hybrid
Harmony Search (HS-BFGS) and Grey Wolf Optimizer (GWO) and other performance
indicators such as the mean absolute percentage error (MAPE) and value account for (VAF).
Acknowledgment
We would like to express our sincerest thanks to Professor Mahdi Ghaem for his excellent
advice.
References
[1] Ozcelik Y, Polat E, Bayram F, Ay AM. Investigation of the effects of textural properties on marble
cutting with diamond wire. Int J Rock Mech Min Sci 2004;41:228–34.
[2] Luo SY, Liao YS. Effects of diamond grain characteristics on sawblade wear. Int J Mach Tools
Manuf 1993;33:257–66. doi:10.1016/0890-6955(93)90078-9.
[3] Özçelik Y. Multivariate Statistical Analysis of the Wear on Diamond Beads in the Cutting of
Andesitic Rocks. Key Eng Mater 2003;250:118–30. doi:10.4028/www.scientific.net/KEM.250.118.
[4] Mikaeil R, Ozcelik Y, Ataei M, Shaffiee Haghshenas S. Application of harmony search algorithm
to evaluate performance of diamond wire saw. J Min Environ 2016. doi:10.22044/jme.2016.723.
[5] Luo SY, Liao YS. Study of the behaviour of diamond saw-blades in stone processing. J Mater
Process Technol 1995;51:296–308. doi:10.1016/0924-0136(94)01603-X.
[6] Luo SY. Characteristics of diamond sawblade wear in sawing. Int J Mach Tools Manuf
1996;36:661–72. doi:10.1016/0890-6955(95)00071-2.
17. 68 R. Mikaeil et al./ Journal of Soft Computing in Civil Engineering 1-2 (2017) 52-69
[7] Luo SY. Investigation of the worn surfaces of diamond sawblades in sawing granite. J Mater
Process Technol 1997;70:1–8. doi:10.1016/S0924-0136(97)00033-2.
[8] Xu X. Study on the thermal wear of diamond segmented tools in circular sawing of granites. Tribol
Lett 2001;10:245–50. doi:10.1023/A:1016614231427.
[9] Wei X, Wang CY, Zhou ZH. Study on the fuzzy ranking of granite sawability. J Mater Process
Technol 2003;139:277–80. doi:10.1016/S0924-0136(03)00235-8.
[10] Ersoy A, Buyuksagic S, Atici U. Wear characteristics of circular diamond saws in the cutting of
different hard abrasive rocks. Wear 2005;258:1422–36. doi:10.1016/j.wear.2004.09.060.
[11] Özçelik Y, Kulaksız S. Investigation of the relationship between cutting angles and wear on beads
in diamond wire cutting method. 9th Mine Plan. Equip. Sel. Symp. Athens, Greece, 2000, p. 6–9.
[12] Özçelik Y, Kulaksız S, Çetin M. Assessment of the wear of diamond beads in the cutting of
different rock types by the ridge regression. J Mater Process Technol 2002;127:392–400.
doi:10.1016/S0924-0136(02)00429-6.
[13] Najmedin Almasi S, Bagherpour R, Mikaeil R, Ozcelik Y. Analysis of bead wear in diamond wire
sawing considering the rock properties and production rate. Bull Eng Geol Environ 2017;76:1593–
607. doi:10.1007/s10064-017-1057-9.
[14] Shaffiee Haghshenas S, Ozcelik Y, Shaffiee Haghshenas S, Mikaeil R, Sirati P. Ranking and
Assessment of Tunneling Projects Risks Using Fuzzy MCDM (Case Study: Toyserkan Doolayi
Tunnel). 25th Int. Min. Congr. Exhib. Turkey, 2017, p. 122–8.
[15] Haghshenas SS, Neshaei MAL, Pourkazem P, Haghshenas SS. The Risk Assessment of Dam
Construction Projects Using Fuzzy TOPSIS (Case Study: Alavian Earth Dam). Civ Eng J
2016;2:158–67.
[16] Haghshenas SS, Haghshenas SS, Barmal M, Farzan N. Utilization of soft computing for risk
assessment of a tunneling project using geological units. Civ Eng J 2016;2:358–64.
[17] Rad MY, Haghshenas SS, Haghshenas SS. Mechanostratigraphy of cretaceous rocks by fuzzy logic
in East Arak, Iran. 4th Int. Work. Comput. Sci. Eng. WCSE, 2014.
[18] Onyari EK, Ilunga FM. Application of MLP neural network and M5P model tree in predicting
streamflow: A case study of Luvuvhu catchment, South Africa. Int J Innov Manag Technol
2013;4:11.
[19] Hornik K, Stinchcombe M, White H. Multilayer feedforward networks are universal
approximators. Neural Networks 1989;2:359–66. doi:https://doi.org/10.1016/0893-6080(89)90020-
8.
[20] Daniels H, Kamp B. Application of MLP Networks to Bond Rating and House Pricing. Neural
Comput Appl 1999;8:226–34. doi:10.1007/s005210050025.
[21] Khotanzad A, Chung C. Application of multi-layer perceptron neural networks to vision problems.
Neural Comput Appl 1998;7:249–59. doi:10.1007/BF01414886.
[22] Sonmez H, Gokceoglu C, Nefeslioglu HA, Kayabasi A. Estimation of rock modulus: For intact
rocks with an artificial neural network and for rock masses with a new empirical equation. Int J
Rock Mech Min Sci 2006;43:224–35. doi:10.1016/j.ijrmms.2005.06.007.
[23] Hecht-Nielsen R. Kolmogorov’s mapping neural network existence theorem. Proc. Int. Conf.
Neural Networks, IEEE Press; 1987, p. 11–4.
[24] Hush. Classification with neural networks: a performance analysis. IEEE Int. Conf. Syst. Eng.,
IEEE; 1989, p. 277–80. doi:10.1109/ICSYSE.1989.48672.
[25] Kaastra I, Boyd M. Designing a neural network for forecasting financial and economic time series.
Neurocomputing 1996;10:215–36. doi:10.1016/0925-2312(95)00039-9.
18. R. Mikaeil et al./ Journal of Soft Computing in Civil Engineering 1-2 (2017) 52-69 69
[26] Kanellopoulos I, Wilkinson GG. Strategies and best practice for neural network image
classification. Int J Remote Sens 1997;18:711–25.
[27] Ripley BD. Statistical aspects of neural networks. Networks Chaos—statistical Probabilistic Asp
1993;50:40–123.
[28] Paola JD. Neural network classification of multispectral imagery. Master Thesisi, The University of
Arizona, USA, 1994.
[29] Wang C. A theory of generalization in learning machines with neural network applications. Ph.D.
Thesis, University of Pennsylvania, 1994.
[30] Masters T. Practical neural network recipes in C++. Academic Press, San Diego, CA.; 1993.
[31] Hagan MT, Menhaj MB. Training feedforward networks with the Marquardt algorithm. IEEE
Trans Neural Networks 1994;5:989–93. doi:10.1109/72.329697.
[32] Jahed Armaghani D, Hasanipanah M, Tonnizam Mohamad E. A combination of the ICA-ANN
model to predict air-overpressure resulting from blasting. Eng Comput 2016;32:155–71.
doi:10.1007/s00366-015-0408-z.
[33] Zorlu K, Gokceoglu C, Ocakoglu F, Nefeslioglu HA, Acikalin S. Prediction of uniaxial
compressive strength of sandstones using petrography-based models. Eng Geol 2008;96:141–58.
doi:10.1016/j.enggeo.2007.10.009.
[34] Mikaeil R, Haghshenas SS, Shirvand Y, Hasanluy MV, Roshanaei V. Risk assessment of
geological hazards in a tunneling project using harmony search algorithm (case study: Ardabil-
Mianeh railway tunnel). Civ Eng J 2016;2:546–54.
[35] Mikaeil R, Haghshenas SS, Haghshenas SS, Ataei M. Performance prediction of circular saw
machine using imperialist competitive algorithm and fuzzy clustering technique. Neural Comput
Appl 2018;29:283–92. doi:10.1007/s00521-016-2557-4.
[36] Haghshenas SS, Haghshenas SS, Mikaeil R, Sirati Moghadam P, Haghshenas AS. A new model for
evaluating the geological risk based on geomechanical properties—case study: the second part of
emamzade hashem tunnel. Electron J Geotech Eng 2017;22:309–20.
[37] Holland JH. Adaptation in natural and artificial systems: an introductory analysis with applications
to biology, control, and artificial intelligence. MIT press; 1992.
[38] Terranova OG, Gariano SL, Iaquinta P, Iovine GGR. GA SAKe: forecasting landslide activations
by a genetic-algorithms-based hydrological model. Geosci Model Dev 2015;8:1955–78.
[39] Woodward M, Kapelan Z, Gouldby B. Adaptive flood risk management under climate change
uncertainty using real options and optimization. Risk Anal 2014;34:75–92.
[40] Saemi M, Ahmadi M, Varjani AY. Design of neural networks using genetic algorithm for the
permeability estimation of the reservoir. J Pet Sci Eng 2007;59:97–105.
doi:10.1016/j.petrol.2007.03.007.
[41] Khandelwal M, Armaghani DJ. Prediction of Drillability of Rocks with Strength Properties Using a
Hybrid GA-ANN Technique. Geotech Geol Eng 2016;34:605–20. doi:10.1007/s10706-015-9970-9.
[42] Momeni E, Nazir R, Jahed Armaghani D, Maizir H. Prediction of pile bearing capacity using a
hybrid genetic algorithm-based ANN. Measurement 2014;57:122–31.
doi:10.1016/j.measurement.2014.08.007.
[43] Monjezi M, Amini Khoshalan H, Yazdian Varjani A. Prediction of flyrock and backbreak in open
pit blasting operation: a neuro-genetic approach. Arab J Geosci 2012;5:441–8. doi:10.1007/s12517-
010-0185-3.
[44] Aghajanloo M-B, Sabziparvar A-A, Hosseinzadeh Talaee P. Artificial neural network–genetic
algorithm for estimation of crop evapotranspiration in a semi-arid region of Iran. Neural Comput
Appl 2013;23:1387–93. doi:10.1007/s00521-012-1087-y.