This document summarizes research on using artificial neural networks to predict the strength and properties of concrete. It reviews several past studies that have successfully used ANNs to model the compressive strength and durability of concrete mixtures based on input parameters like cement content, water-cement ratio, and use of admixtures or supplementary cementitious materials. The document outlines the methodology of ANN modeling and highlights key findings from previous research, including that ANNs can accurately predict concrete properties for mixtures not included in the original training data. Overall, the review shows that ANN modeling is an effective technique for predicting nonlinear relationships in concrete performance based on mixture ingredients and proportions.
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 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.
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.
Multiscale Modeling Approach for Prediction the Elastic Modulus of Percolated...ijtsrd
In this study the effective elastic modulus of cellulose nanocrystal CNC network is evaluated using multiscale method and micromechanical analysis. For this purpose, the elastic modulus of CNC water phases are randomly assigned to a two dimensional 2D checkerboard structure and the elastic response is evaluated. In addition, the effect of having a different number of phases CNC, water and interface is evaluated by assigning a discreet and continuous distribution of elastic modulus to checkerboard structure. When the number of phases increases dramatically, the distribution of phases is continuous and is defined with Weibull distribution. The results show that for two phase materials CNC and water when the microstructure has organized pattern the rule of the mixture and numerical model provide the same effective modulus, however when the microstructure is completely random, the self consistent micromechanical model should be used. Also, this study suggests 50 volume fraction as the percolation threshold for the CNC network with 10 GPa effective elastic modulus. The results from percolated multiphase network reveal that for microstructures with 4 phases and above, the percolated network converge to 35 GPa. Mehrdad Bor | Jim Huang "Multiscale Modeling Approach for Prediction the Elastic Modulus of Percolated Cellulose Nanocrystal (CNC) Network" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd26553.pdfPaper URL: https://www.ijtsrd.com/engineering/mechanical-engineering/26553/multiscale-modeling-approach-for-prediction-the-elastic-modulus-of-percolated-cellulose-nanocrystal-cnc-network/mehrdad-bor
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 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.
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.
Multiscale Modeling Approach for Prediction the Elastic Modulus of Percolated...ijtsrd
In this study the effective elastic modulus of cellulose nanocrystal CNC network is evaluated using multiscale method and micromechanical analysis. For this purpose, the elastic modulus of CNC water phases are randomly assigned to a two dimensional 2D checkerboard structure and the elastic response is evaluated. In addition, the effect of having a different number of phases CNC, water and interface is evaluated by assigning a discreet and continuous distribution of elastic modulus to checkerboard structure. When the number of phases increases dramatically, the distribution of phases is continuous and is defined with Weibull distribution. The results show that for two phase materials CNC and water when the microstructure has organized pattern the rule of the mixture and numerical model provide the same effective modulus, however when the microstructure is completely random, the self consistent micromechanical model should be used. Also, this study suggests 50 volume fraction as the percolation threshold for the CNC network with 10 GPa effective elastic modulus. The results from percolated multiphase network reveal that for microstructures with 4 phases and above, the percolated network converge to 35 GPa. Mehrdad Bor | Jim Huang "Multiscale Modeling Approach for Prediction the Elastic Modulus of Percolated Cellulose Nanocrystal (CNC) Network" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd26553.pdfPaper URL: https://www.ijtsrd.com/engineering/mechanical-engineering/26553/multiscale-modeling-approach-for-prediction-the-elastic-modulus-of-percolated-cellulose-nanocrystal-cnc-network/mehrdad-bor
There are many parameters of concrete that
influence its strength gaining characteristics. This study is
an attempt to use the early compressive strength test result to
estimate compressive strength at different ages. Potential
utilization of the early day compressive strength result to
predict characteristic strength of normal weight concrete has
been investigated. A simple mathematical model capable of
predicting the compressive strength of concrete at any age is
proposed for both stone and local aggregate concrete. The
basic model consists of a rational polynomial equation having
only two coefficients. This study also proposes a simple reliable
relationship between the coefficient p (strength at infinite
time) with the strength values of concrete of a particular day.
This relation greatly simplifies the concrete strength
prediction model. The developed model is validated for
commonly used stone aggregate concrete and also for local
(brick) aggregate concrete. Data used in this study are collected
from some previous studies and recent experimental works.
The analysis carried with the model using different data
exhibit reliable prediction of concrete strength at different
ages (7, 14, 28 days etc.) with excellent efficiency.
DCT AND DFT BASED BIOMETRIC RECOGNITION AND MULTIMODAL BIOMETRIC SECURITYIAEME Publication
This Research paper discusses the study and analysis conducted during this research on various techniques in biometric domain. A close glance on biometric enhancement techniques and their limitations are presented in this research paper. This process would enable researcher to understand the research contributions in the area of DCT and DFT based recognition and security, locate some crucial limitations of these notable research. This paper having summary about the different research papers that applicable to our topic of research which mentioned above. Biometric Recognition and security is a most important subject of research in this area of image processing.
Computational Intelligence Approach for Predicting the Hardness Performances ...Waqas Tariq
This paper presents a computational approach on predicting of hardness performances for Titanium Aluminium Nitride (TiA1N) coating process. A new application in predicting the hardness performances of TiA1N coatings using a method called Support Vector Machine (SVM) and Artificial Neural Network (ANN) is implemented. TiAlN coatings are usually used in high-speed machining due to its excellent properties in surface hardness and wear resistance. Physical Vapor Deposition (PVD) magnetron sputtering process has been used to produce the TiA1N coatings. Based on the experimental dataset of previous work, the SVM and ANN model is used in predicting the hardness of TiA1N coatings. The influential factors of three coating process parameter namely substrate sputtering power, substrate bias voltage and substrate temperature were selected as input while the output parameter is the hardness. The results of proposed SVM and ANN models are compared to the experimental result and the hybrid RSM-Fuzzy model from previous work. The comparisons of SVM and ANN models against hybrid RSM-Fuzzy were based on predictive performances in order to obtain the most accurate model for prediction of hardness in TiA1N coating. In terms of predictive performance evaluation, four performances matrix were applied that are percentage error, mean square error (MSE), co-efficient determination (R 2) and model accuracy. The result has proved that the proposed SVM model shows the better result compared to the ANN and hybrid RSM-fuzzy model. The good performances of the results obtained by the SVM method shows that this method can be applied for prediction of hardness performances in TiA1N coating process with better predictive performances compared to ANN and hybrid RSM-Fuzzy.
IDENTIFICATION OF DELAMINATION SIZE AND LOCATION OF COMPOSITE LAMINATE FROM TIME DOMAIN DATA OF MAGNETOSTRICTIVE SENSOR AND ACTUATOR USING ARTIFICIAL NEURAL NETWORK.
Artificial Intelligence based optimization of weld bead geometry in laser wel...IJMER
This paper reports on a modeling and optimization of laser welding of aluminum-magnesium alloy thickness of 1.7mm. Regression analysis is used for modeling and Genetic algorithm is used for optimize the process parameters.The input values for the regression methods is taken according the Taguchi based orthogonal array. A software named Computer aided Robust Parameter Genetic Algorithm CARPGA has been developed in MATLAB 2013 which combine all of these methodologies. This software has been validated with some published paper.
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.
Enhancing the performance of cluster based text summarization using support v...eSAT Journals
Abstract Technology is evolving day by day and this increase in technology is nothing but is the efforts to reduce human work and to have systems as automatic as possible. Same thing is true in terms of existence of digital information. Due to enormous increase in the use of internet, there is striking increase in the digital information. This digital information is characterized by different form of information, same information in different form, unrelated information and also there is lot of redundant information. Another next important thing to note is that most of the time we require textual information. To search or retrieve small information one has to go through thousands of documents, read all the retrieved documents irrespective whether they contain useful information or no. It becomes very difficult to read all the retrieved documents and prepare exact summary out of it within time. Besides this, many times retrieved information is repeated in almost many documents. This leads to research in the area of text mining. Text summarization is one of the challenging tasks in the field of text mining. Keywords: Entropy, FCM, Purity, SVM
DETECTION OF HUMAN BLADDER CANCER CELLS USING IMAGE PROCESSINGprj_publication
Bladder cancer presents a spectrum of different diatheses. A precise assessment for
individualized treatment depends on the accuracy of the initial diagnosis. In this method the
performance of the level set segmentation is subject to appropriate initialization and optimal
configuration of controlling parameters, which require substantial manual intervention. A
new fuzzy level set algorithm is proposed in this paper to facilitate medical image
segmentation. It is able to directly evolve from the initial segmentation by spatial fuzzy
clustering. The Spatial induced fuzzy c-means using pixel classification and level set
methods are utilizing dynamic variational boundaries for image segmentation. The
controlling parameters of level set evolution are also estimated from the results of clustering.
The fuzzy level set algorithm is enhanced with locally regularized evolution. Such
improvements facilitate level set manipulation and lead to more robust segmentation.
Performance evaluation of the proposed algorithm was carried on medical images
Using particle swarm optimization to solve test functions problemsriyaniaes
In this paper the benchmarking functions are used to evaluate and check the particle swarm optimization (PSO) algorithm. However, the functions utilized have two dimension but they selected with different difficulty and with different models. In order to prove capability of PSO, it is compared with genetic algorithm (GA). Hence, the two algorithms are compared in terms of objective functions and the standard deviation. Different runs have been taken to get convincing results and the parameters are chosen properly where the Matlab software is used. Where the suggested algorithm can solve different engineering problems with different dimension and outperform the others in term of accuracy and speed of convergence.
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.
PREDICTION OF COMPRESSIVE STRENGTH OF HIGH PERFORMANCE CONCRETE CONTAINING IN...IAEME Publication
This paper presents artificial neural network (ANN) based model to predict the compressive strength of concrete containing Industrial Byproducts at the age of 28, 56, 90 and 120 days. A total of 71 specimens were casted with twelve different concrete mix proportions. The experimental results are training data to construct the artificial neural network model. The data used in the multilayer feed forward neural network models are arranged in a format of ten input parameters that cover the age of specimen, cement, Fly ash, Silica fume, Metakaolin, bottom ash, sand, Coarse aggregate, water and Superplasticizer. According to these parameter in the neural network models are predicted the compressive strength values of concrete containing Industrial Byproducts. T
High-Performance Concrete Compressive Strength Prediction Based Weighted Supp...IJERA Editor
Concrete is the safest and sustainable construction material which is most widely used in the world as it provides superior fire resistance, gains strength over time and gives an extremely long service life. Unfortunately high performance concrete is undoubtedly one of the most innovative materials in construction. Its Designing involves the process of selecting suitable ingredients of concrete (water, cement, fine and aggregates and a number of additives like mineral and chemical admixture) and determining their relative amounts with the objective of producing a high performance concrete of the required, strength, durability, and workability as economically as possible. Their proportions have a high influence on the final strength of the product. These relations do not seem to follow a mathematical formula and yet their knowledge is crucial to optimize the quantities of raw materials used in the manufacture of high performance concrete. Therefore, it would be important to have a tool to numerically model such relationships, even before processing. In this aspect the main purpose of this paper is to predict the compressive strength of the high performance concrete by using classification algorithms. For building these models, training and testing using the available experimental results for 1030 specimens produced with 8 different mixture proportions are used. The result from this study suggests that weighted Support Vector Machines (wSVM) based models perform remarkably well in predicting the compressive strength of the concrete mix.
There are many parameters of concrete that
influence its strength gaining characteristics. This study is
an attempt to use the early compressive strength test result to
estimate compressive strength at different ages. Potential
utilization of the early day compressive strength result to
predict characteristic strength of normal weight concrete has
been investigated. A simple mathematical model capable of
predicting the compressive strength of concrete at any age is
proposed for both stone and local aggregate concrete. The
basic model consists of a rational polynomial equation having
only two coefficients. This study also proposes a simple reliable
relationship between the coefficient p (strength at infinite
time) with the strength values of concrete of a particular day.
This relation greatly simplifies the concrete strength
prediction model. The developed model is validated for
commonly used stone aggregate concrete and also for local
(brick) aggregate concrete. Data used in this study are collected
from some previous studies and recent experimental works.
The analysis carried with the model using different data
exhibit reliable prediction of concrete strength at different
ages (7, 14, 28 days etc.) with excellent efficiency.
DCT AND DFT BASED BIOMETRIC RECOGNITION AND MULTIMODAL BIOMETRIC SECURITYIAEME Publication
This Research paper discusses the study and analysis conducted during this research on various techniques in biometric domain. A close glance on biometric enhancement techniques and their limitations are presented in this research paper. This process would enable researcher to understand the research contributions in the area of DCT and DFT based recognition and security, locate some crucial limitations of these notable research. This paper having summary about the different research papers that applicable to our topic of research which mentioned above. Biometric Recognition and security is a most important subject of research in this area of image processing.
Computational Intelligence Approach for Predicting the Hardness Performances ...Waqas Tariq
This paper presents a computational approach on predicting of hardness performances for Titanium Aluminium Nitride (TiA1N) coating process. A new application in predicting the hardness performances of TiA1N coatings using a method called Support Vector Machine (SVM) and Artificial Neural Network (ANN) is implemented. TiAlN coatings are usually used in high-speed machining due to its excellent properties in surface hardness and wear resistance. Physical Vapor Deposition (PVD) magnetron sputtering process has been used to produce the TiA1N coatings. Based on the experimental dataset of previous work, the SVM and ANN model is used in predicting the hardness of TiA1N coatings. The influential factors of three coating process parameter namely substrate sputtering power, substrate bias voltage and substrate temperature were selected as input while the output parameter is the hardness. The results of proposed SVM and ANN models are compared to the experimental result and the hybrid RSM-Fuzzy model from previous work. The comparisons of SVM and ANN models against hybrid RSM-Fuzzy were based on predictive performances in order to obtain the most accurate model for prediction of hardness in TiA1N coating. In terms of predictive performance evaluation, four performances matrix were applied that are percentage error, mean square error (MSE), co-efficient determination (R 2) and model accuracy. The result has proved that the proposed SVM model shows the better result compared to the ANN and hybrid RSM-fuzzy model. The good performances of the results obtained by the SVM method shows that this method can be applied for prediction of hardness performances in TiA1N coating process with better predictive performances compared to ANN and hybrid RSM-Fuzzy.
IDENTIFICATION OF DELAMINATION SIZE AND LOCATION OF COMPOSITE LAMINATE FROM TIME DOMAIN DATA OF MAGNETOSTRICTIVE SENSOR AND ACTUATOR USING ARTIFICIAL NEURAL NETWORK.
Artificial Intelligence based optimization of weld bead geometry in laser wel...IJMER
This paper reports on a modeling and optimization of laser welding of aluminum-magnesium alloy thickness of 1.7mm. Regression analysis is used for modeling and Genetic algorithm is used for optimize the process parameters.The input values for the regression methods is taken according the Taguchi based orthogonal array. A software named Computer aided Robust Parameter Genetic Algorithm CARPGA has been developed in MATLAB 2013 which combine all of these methodologies. This software has been validated with some published paper.
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.
Enhancing the performance of cluster based text summarization using support v...eSAT Journals
Abstract Technology is evolving day by day and this increase in technology is nothing but is the efforts to reduce human work and to have systems as automatic as possible. Same thing is true in terms of existence of digital information. Due to enormous increase in the use of internet, there is striking increase in the digital information. This digital information is characterized by different form of information, same information in different form, unrelated information and also there is lot of redundant information. Another next important thing to note is that most of the time we require textual information. To search or retrieve small information one has to go through thousands of documents, read all the retrieved documents irrespective whether they contain useful information or no. It becomes very difficult to read all the retrieved documents and prepare exact summary out of it within time. Besides this, many times retrieved information is repeated in almost many documents. This leads to research in the area of text mining. Text summarization is one of the challenging tasks in the field of text mining. Keywords: Entropy, FCM, Purity, SVM
DETECTION OF HUMAN BLADDER CANCER CELLS USING IMAGE PROCESSINGprj_publication
Bladder cancer presents a spectrum of different diatheses. A precise assessment for
individualized treatment depends on the accuracy of the initial diagnosis. In this method the
performance of the level set segmentation is subject to appropriate initialization and optimal
configuration of controlling parameters, which require substantial manual intervention. A
new fuzzy level set algorithm is proposed in this paper to facilitate medical image
segmentation. It is able to directly evolve from the initial segmentation by spatial fuzzy
clustering. The Spatial induced fuzzy c-means using pixel classification and level set
methods are utilizing dynamic variational boundaries for image segmentation. The
controlling parameters of level set evolution are also estimated from the results of clustering.
The fuzzy level set algorithm is enhanced with locally regularized evolution. Such
improvements facilitate level set manipulation and lead to more robust segmentation.
Performance evaluation of the proposed algorithm was carried on medical images
Using particle swarm optimization to solve test functions problemsriyaniaes
In this paper the benchmarking functions are used to evaluate and check the particle swarm optimization (PSO) algorithm. However, the functions utilized have two dimension but they selected with different difficulty and with different models. In order to prove capability of PSO, it is compared with genetic algorithm (GA). Hence, the two algorithms are compared in terms of objective functions and the standard deviation. Different runs have been taken to get convincing results and the parameters are chosen properly where the Matlab software is used. Where the suggested algorithm can solve different engineering problems with different dimension and outperform the others in term of accuracy and speed of convergence.
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.
PREDICTION OF COMPRESSIVE STRENGTH OF HIGH PERFORMANCE CONCRETE CONTAINING IN...IAEME Publication
This paper presents artificial neural network (ANN) based model to predict the compressive strength of concrete containing Industrial Byproducts at the age of 28, 56, 90 and 120 days. A total of 71 specimens were casted with twelve different concrete mix proportions. The experimental results are training data to construct the artificial neural network model. The data used in the multilayer feed forward neural network models are arranged in a format of ten input parameters that cover the age of specimen, cement, Fly ash, Silica fume, Metakaolin, bottom ash, sand, Coarse aggregate, water and Superplasticizer. According to these parameter in the neural network models are predicted the compressive strength values of concrete containing Industrial Byproducts. T
High-Performance Concrete Compressive Strength Prediction Based Weighted Supp...IJERA Editor
Concrete is the safest and sustainable construction material which is most widely used in the world as it provides superior fire resistance, gains strength over time and gives an extremely long service life. Unfortunately high performance concrete is undoubtedly one of the most innovative materials in construction. Its Designing involves the process of selecting suitable ingredients of concrete (water, cement, fine and aggregates and a number of additives like mineral and chemical admixture) and determining their relative amounts with the objective of producing a high performance concrete of the required, strength, durability, and workability as economically as possible. Their proportions have a high influence on the final strength of the product. These relations do not seem to follow a mathematical formula and yet their knowledge is crucial to optimize the quantities of raw materials used in the manufacture of high performance concrete. Therefore, it would be important to have a tool to numerically model such relationships, even before processing. In this aspect the main purpose of this paper is to predict the compressive strength of the high performance concrete by using classification algorithms. For building these models, training and testing using the available experimental results for 1030 specimens produced with 8 different mixture proportions are used. The result from this study suggests that weighted Support Vector Machines (wSVM) based models perform remarkably well in predicting the compressive strength of the concrete mix.
Artificial Neural Network Model for Compressive Strength of Lateritic BlocksIJAEMSJORNAL
Lateritic soil are locally abundant and relatively cheap to be used for block production. Its use has gone a long way in reducing the cost of block production and construction work in general. In order to optimize the usefulness of lateritic soil, there is need to model the properties of lateritic blocks. Compressive strength is an important property of lateritic block that must be known, but it cannot be guessed easily due to the block mix proportion and processes. Statistical models used in predicting the properties of lateritic blocks operate on restricted range of data. That is, the model cannot predict input data that are outside the range of data used in developing the model. The need for a model that can predict the compressive strength of lateritic blocks for any given mix ratio became necessary. This study developed Artificial Neural Network model for predicting the compressive strength of lateritic blocks. Lateritic blocks were produced with mix ratios ranging from 1:4 to 1:12. The blocks were cured for 7, 14 and 28 days. The 28th day experimental results and results obtained from literatures on similar works were used to formulate the model. The test data were a total of 155 samples.The maximum compressive strength predicted by the model was 3.06 N/mm^2 corresponding to a mix ratio of 0.4:1:4 of water-cement ratio, cement and lateritic soil. The model accuracy was tested using Fisher test. The result of the Fisher test computations obtained 1.008 for calculated F and 3.5 for F obtained from the table. Hence the model satisfied the test. The model result also compares favourably with the experimental result.
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.
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
Compressive Strength of Ready Mix Concrete Using Soft Computing TechniquesIJERA Editor
Ready mixed concrete (RMC) is an essential material in contemporary construction and engineering projects. Compressive strength of concrete is a major and perhaps the most important mechanical property, which is usually measured after a standard curing of 28 days. In this research work, 28-day compressive strength of Ready Mix Concrete has been estimated by using feed forward back propagation neural network, Fuzzy Logic and Adaptive Neuro Fuzzy Inference System (ANFIS) modeling. The data for the ready mixed concretes (RMC) were collected from RMC batching plant. Various models has been has been developed for different input scenarios. The compressive strength was modeled as a function of five variables, the effects of each parameter on networks were studied for Artificial Neural Network (ANN), Fuzzy Logic and ANFIS models.
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.
VALIDATION OF HARDNESS AND TENSILE STRENGTH OF AL 7075 BASED HYBRID COMPOSITE...IAEME Publication
than those of usual composites. Based on the extensive literature review, it is concluded that majority of investigations were carried out on Aluminium alloy based composite materials involving Silicon Carbide and Alumina as
reinforcements. The investigations using fly ash and E-glass have been carried out with matrix aluminium alloys other
than Al 7075 alloy. In the present investigation a new class of hybrid composite, Al 7075 alloy reinforced with fly ash
particulates, E-glass short fibers has been formed and experimental results have been validated by using Artificial Neural Networks (ANNs). The ANN predictions were in very good agreement with experimental results, with correlation
coefficient 0.99918.
Hybrid composites have unique features that can be used to meet diverse and competing design requirements in
a more cost-effective way than either - advanced or, conventional composites. The Natural-polymer hybrid composites reinforced with carbon fiber and jute with three different orientations - 0°, 30° and 45° using epoxy resin is fabricated by hand lay-up process. The tensile test is carried out to study the tensile behavior of the developed hybrid composite.
Algorithm for Modeling Unconventional Machine Tool Machining Parameters using...IDES Editor
Unconventional machining process finds a lot of
application in aerospace and precision industries. It is
preferred over other conventional methods because of the
advent of composite and high strength to weight ratio
materials, complex parts and also because of its high accuracy
and precision. Usually in unconventional machine tools, trial
and error method is used to fix the values of process
parameters. In the proposed work an algorithm which is
developed using Artificial Neural Network (ANN) is proposed
to create mathematical model functionally relating process
parameters and operating parameters of any unconventional
machine tool. This is accomplished by training a feed forward
network with back propagation learning algorithm. The
required data which are used for training and testing the ANN
in the case study is obtained by conducting trial runs in EBW
machine. By adopting the proposed algorithm there will be a
reduction in production time and set-up time along with
reduction in manufacturing cost in unconventional machining
processes. This in general increases the overall productivity.
The programs for training and testing the neural network are
developed, using MATLAB package
Hyperelastic material models in finite element analysis of polymersKartik Srinivas
This paper describes the Hyperelastic material models and strain energy functions used in the finite element analysis (FEA) of polymers. Material characterization testing
Optimizing and Predicting Compressive Strength of One-Part Geopolymer ConcreteAnoop Meshram
Developed and implemented a machine learning model to optimize and predict the compressive strength of one-part geopolymer concrete, improving concrete performance and reducing material waste.
Similar to IRJET- A Review on Application of Artificial Intelligence to Predict Strength of Concrete (20)
Student information management system project report ii.pdfKamal Acharya
Our project explains about the student management. This project mainly explains the various actions related to student details. This project shows some ease in adding, editing and deleting the student details. It also provides a less time consuming process for viewing, adding, editing and deleting the marks of the students.
Saudi Arabia stands as a titan in the global energy landscape, renowned for its abundant oil and gas resources. It's the largest exporter of petroleum and holds some of the world's most significant reserves. Let's delve into the top 10 oil and gas projects shaping Saudi Arabia's energy future in 2024.
Industrial Training at Shahjalal Fertilizer Company Limited (SFCL)MdTanvirMahtab2
This presentation is about the working procedure of Shahjalal Fertilizer Company Limited (SFCL). A Govt. owned Company of Bangladesh Chemical Industries Corporation under Ministry of Industries.
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.
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.
CFD Simulation of By-pass Flow in a HRSG module by R&R Consult.pptxR&R Consult
CFD analysis is incredibly effective at solving mysteries and improving the performance of complex systems!
Here's a great example: At a large natural gas-fired power plant, where they use waste heat to generate steam and energy, they were puzzled that their boiler wasn't producing as much steam as expected.
R&R and Tetra Engineering Group Inc. were asked to solve the issue with reduced steam production.
An inspection had shown that a significant amount of hot flue gas was bypassing the boiler tubes, where the heat was supposed to be transferred.
R&R Consult conducted a CFD analysis, which revealed that 6.3% of the flue gas was bypassing the boiler tubes without transferring heat. The analysis also showed that the flue gas was instead being directed along the sides of the boiler and between the modules that were supposed to capture the heat. This was the cause of the reduced performance.
Based on our results, Tetra Engineering installed covering plates to reduce the bypass flow. This improved the boiler's performance and increased electricity production.
It is always satisfying when we can help solve complex challenges like this. Do your systems also need a check-up or optimization? Give us a call!
Work done in cooperation with James Malloy and David Moelling from Tetra Engineering.
More examples of our work https://www.r-r-consult.dk/en/cases-en/
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.
Welcome to WIPAC Monthly the magazine brought to you by the LinkedIn Group Water Industry Process Automation & Control.
In this month's edition, along with this month's industry news to celebrate the 13 years since the group was created we have articles including
A case study of the used of Advanced Process Control at the Wastewater Treatment works at Lleida in Spain
A look back on an article on smart wastewater networks in order to see how the industry has measured up in the interim around the adoption of Digital Transformation in the Water Industry.