The Experts below are selected from a list of 8559 Experts worldwide ranked by ideXlab platform

Pourya Alidoust - One of the best experts on this subject based on the ideXlab platform.

  • modeling of bentonite sepiolite plastic Concrete Compressive Strength using artificial neural network and support vector machine
    Frontiers of Structural and Civil Engineering, 2019
    Co-Authors: Ali Reza Ghanizadeh, Hakime Abbaslou, Amir Tavana Amlashi, Pourya Alidoust
    Abstract:

    Plastic Concrete is an engineering material, which is commonly used for construction of cut-off walls to prevent water seepage under the dam. This paper aims to explore two machine learning algorithms including artificial neural network (ANN) and support vector machine (SVM) to predict the Compressive Strength of bentonite/sepiolite plastic Concretes. For this purpose, two unique sets of 72 data for Compressive Strength of bentonite and sepiolite plastic Concrete samples (totally 144 data) were prepared by conducting an experimental study. The results confirm the ability of ANN and SVM models in prediction processes. Also, Sensitivity analysis of the best obtained model indicated that cement and silty clay have the maximum and minimum influences on the Compressive Strength, respectively. In addition, investigation of the effect of measurement error of input variables showed that change in the sand content (amount) and curing time will have the maximum and minimum effects on the output mean absolute percent error (MAPE) of model, respectively. Finally, the influence of different variables on the plastic Concrete Compressive Strength values was evaluated by conducting parametric studies.

  • Modeling of bentonite/sepiolite plastic Concrete Compressive Strength using artificial neural network and support vector machine
    Frontiers of Structural and Civil Engineering, 2018
    Co-Authors: Ali Reza Ghanizadeh, Hakime Abbaslou, Amir Tavana Amlashi, Pourya Alidoust
    Abstract:

    Plastic Concrete is an engineering material, which is commonly used for construction of cut-off walls to prevent water seepage under the dam. This paper aims to explore two machine learning algorithms including artificial neural network (ANN) and support vector machine (SVM) to predict the Compressive Strength of bentonite/sepiolite plastic Concretes. For this purpose, two unique sets of 72 data for Compressive Strength of bentonite and sepiolite plastic Concrete samples (totally 144 data) were prepared by conducting an experimental study. The results confirm the ability of ANN and SVM models in prediction processes. Also, Sensitivity analysis of the best obtained model indicated that cement and silty clay have the maximum and minimum influences on the Compressive Strength, respectively. In addition, investigation of the effect of measurement error of input variables showed that change in the sand content (amount) and curing time will have the maximum and minimum effects on the output mean absolute percent error (MAPE) of model, respectively. Finally, the influence of different variables on the plastic Concrete Compressive Strength values was evaluated by conducting parametric studies.

Ali Reza Ghanizadeh - One of the best experts on this subject based on the ideXlab platform.

  • modeling of bentonite sepiolite plastic Concrete Compressive Strength using artificial neural network and support vector machine
    Frontiers of Structural and Civil Engineering, 2019
    Co-Authors: Ali Reza Ghanizadeh, Hakime Abbaslou, Amir Tavana Amlashi, Pourya Alidoust
    Abstract:

    Plastic Concrete is an engineering material, which is commonly used for construction of cut-off walls to prevent water seepage under the dam. This paper aims to explore two machine learning algorithms including artificial neural network (ANN) and support vector machine (SVM) to predict the Compressive Strength of bentonite/sepiolite plastic Concretes. For this purpose, two unique sets of 72 data for Compressive Strength of bentonite and sepiolite plastic Concrete samples (totally 144 data) were prepared by conducting an experimental study. The results confirm the ability of ANN and SVM models in prediction processes. Also, Sensitivity analysis of the best obtained model indicated that cement and silty clay have the maximum and minimum influences on the Compressive Strength, respectively. In addition, investigation of the effect of measurement error of input variables showed that change in the sand content (amount) and curing time will have the maximum and minimum effects on the output mean absolute percent error (MAPE) of model, respectively. Finally, the influence of different variables on the plastic Concrete Compressive Strength values was evaluated by conducting parametric studies.

  • Modeling of bentonite/sepiolite plastic Concrete Compressive Strength using artificial neural network and support vector machine
    Frontiers of Structural and Civil Engineering, 2018
    Co-Authors: Ali Reza Ghanizadeh, Hakime Abbaslou, Amir Tavana Amlashi, Pourya Alidoust
    Abstract:

    Plastic Concrete is an engineering material, which is commonly used for construction of cut-off walls to prevent water seepage under the dam. This paper aims to explore two machine learning algorithms including artificial neural network (ANN) and support vector machine (SVM) to predict the Compressive Strength of bentonite/sepiolite plastic Concretes. For this purpose, two unique sets of 72 data for Compressive Strength of bentonite and sepiolite plastic Concrete samples (totally 144 data) were prepared by conducting an experimental study. The results confirm the ability of ANN and SVM models in prediction processes. Also, Sensitivity analysis of the best obtained model indicated that cement and silty clay have the maximum and minimum influences on the Compressive Strength, respectively. In addition, investigation of the effect of measurement error of input variables showed that change in the sand content (amount) and curing time will have the maximum and minimum effects on the output mean absolute percent error (MAPE) of model, respectively. Finally, the influence of different variables on the plastic Concrete Compressive Strength values was evaluated by conducting parametric studies.

Amir Tavana Amlashi - One of the best experts on this subject based on the ideXlab platform.

  • modeling of bentonite sepiolite plastic Concrete Compressive Strength using artificial neural network and support vector machine
    Frontiers of Structural and Civil Engineering, 2019
    Co-Authors: Ali Reza Ghanizadeh, Hakime Abbaslou, Amir Tavana Amlashi, Pourya Alidoust
    Abstract:

    Plastic Concrete is an engineering material, which is commonly used for construction of cut-off walls to prevent water seepage under the dam. This paper aims to explore two machine learning algorithms including artificial neural network (ANN) and support vector machine (SVM) to predict the Compressive Strength of bentonite/sepiolite plastic Concretes. For this purpose, two unique sets of 72 data for Compressive Strength of bentonite and sepiolite plastic Concrete samples (totally 144 data) were prepared by conducting an experimental study. The results confirm the ability of ANN and SVM models in prediction processes. Also, Sensitivity analysis of the best obtained model indicated that cement and silty clay have the maximum and minimum influences on the Compressive Strength, respectively. In addition, investigation of the effect of measurement error of input variables showed that change in the sand content (amount) and curing time will have the maximum and minimum effects on the output mean absolute percent error (MAPE) of model, respectively. Finally, the influence of different variables on the plastic Concrete Compressive Strength values was evaluated by conducting parametric studies.

  • Modeling of bentonite/sepiolite plastic Concrete Compressive Strength using artificial neural network and support vector machine
    Frontiers of Structural and Civil Engineering, 2018
    Co-Authors: Ali Reza Ghanizadeh, Hakime Abbaslou, Amir Tavana Amlashi, Pourya Alidoust
    Abstract:

    Plastic Concrete is an engineering material, which is commonly used for construction of cut-off walls to prevent water seepage under the dam. This paper aims to explore two machine learning algorithms including artificial neural network (ANN) and support vector machine (SVM) to predict the Compressive Strength of bentonite/sepiolite plastic Concretes. For this purpose, two unique sets of 72 data for Compressive Strength of bentonite and sepiolite plastic Concrete samples (totally 144 data) were prepared by conducting an experimental study. The results confirm the ability of ANN and SVM models in prediction processes. Also, Sensitivity analysis of the best obtained model indicated that cement and silty clay have the maximum and minimum influences on the Compressive Strength, respectively. In addition, investigation of the effect of measurement error of input variables showed that change in the sand content (amount) and curing time will have the maximum and minimum effects on the output mean absolute percent error (MAPE) of model, respectively. Finally, the influence of different variables on the plastic Concrete Compressive Strength values was evaluated by conducting parametric studies.

Hakime Abbaslou - One of the best experts on this subject based on the ideXlab platform.

  • modeling of bentonite sepiolite plastic Concrete Compressive Strength using artificial neural network and support vector machine
    Frontiers of Structural and Civil Engineering, 2019
    Co-Authors: Ali Reza Ghanizadeh, Hakime Abbaslou, Amir Tavana Amlashi, Pourya Alidoust
    Abstract:

    Plastic Concrete is an engineering material, which is commonly used for construction of cut-off walls to prevent water seepage under the dam. This paper aims to explore two machine learning algorithms including artificial neural network (ANN) and support vector machine (SVM) to predict the Compressive Strength of bentonite/sepiolite plastic Concretes. For this purpose, two unique sets of 72 data for Compressive Strength of bentonite and sepiolite plastic Concrete samples (totally 144 data) were prepared by conducting an experimental study. The results confirm the ability of ANN and SVM models in prediction processes. Also, Sensitivity analysis of the best obtained model indicated that cement and silty clay have the maximum and minimum influences on the Compressive Strength, respectively. In addition, investigation of the effect of measurement error of input variables showed that change in the sand content (amount) and curing time will have the maximum and minimum effects on the output mean absolute percent error (MAPE) of model, respectively. Finally, the influence of different variables on the plastic Concrete Compressive Strength values was evaluated by conducting parametric studies.

  • Modeling of bentonite/sepiolite plastic Concrete Compressive Strength using artificial neural network and support vector machine
    Frontiers of Structural and Civil Engineering, 2018
    Co-Authors: Ali Reza Ghanizadeh, Hakime Abbaslou, Amir Tavana Amlashi, Pourya Alidoust
    Abstract:

    Plastic Concrete is an engineering material, which is commonly used for construction of cut-off walls to prevent water seepage under the dam. This paper aims to explore two machine learning algorithms including artificial neural network (ANN) and support vector machine (SVM) to predict the Compressive Strength of bentonite/sepiolite plastic Concretes. For this purpose, two unique sets of 72 data for Compressive Strength of bentonite and sepiolite plastic Concrete samples (totally 144 data) were prepared by conducting an experimental study. The results confirm the ability of ANN and SVM models in prediction processes. Also, Sensitivity analysis of the best obtained model indicated that cement and silty clay have the maximum and minimum influences on the Compressive Strength, respectively. In addition, investigation of the effect of measurement error of input variables showed that change in the sand content (amount) and curing time will have the maximum and minimum effects on the output mean absolute percent error (MAPE) of model, respectively. Finally, the influence of different variables on the plastic Concrete Compressive Strength values was evaluated by conducting parametric studies.

Ali Akbar Ramezanianpour - One of the best experts on this subject based on the ideXlab platform.

  • EMS - Evolutionary Fuzzy Function with Support Vector Regression for the Prediction of Concrete Compressive Strength
    2011 UKSim 5th European Symposium on Computer Modeling and Simulation, 2011
    Co-Authors: Siamak Safarzadegan Gilan, Ali Akbar Ramezanianpour
    Abstract:

    The main purpose of this paper is to develop an evolutionary fuzzy function with support vector regression (EFF-SVR) model to predict the Compressive Strength of Concrete. Fuzzy functions alter conventional fuzzy system modelling methods structurally. They take advantage of utilizing membership values calculated by fuzzy c-mean (FCM) clustering, and their possible transformations, as additional explanatory variables augmented to the original input space. Since support vector regression (SVR) methods have considerable capability of minimizing both empirical and complexity risks simultaneously, the hybrid model of EFF-SVR is expected to yield robust results. Finally, the generalization capability and robustness of EFF-SVR are compared with some existing system modelling methods, i.e., artificial neural network (ANN), adaptive neural-fuzzy inference system (ANFIS), fuzzy function with least squared estimation (FF-LSE), and improved FF-LSE. The results show that EFF-SVR has a great ability as a feasible tool for prediction of the Concrete Compressive Strength.

  • Evolutionary Fuzzy Function with Support Vector Regression for the Prediction of Concrete Compressive Strength
    2011 UKSim 5th European Symposium on Computer Modeling and Simulation, 2011
    Co-Authors: Siamak Safarzadegan Gilan, Ali Akbar Ramezanianpour
    Abstract:

    The main purpose of this paper is to develop an evolutionary fuzzy function with support vector regression (EFF-SVR) model to predict the Compressive Strength of Concrete. Fuzzy functions alter conventional fuzzy system modelling methods structurally. They take advantage of utilizing membership values calculated by fuzzy c-mean (FCM) clustering, and their possible transformations, as additional explanatory variables augmented to the original input space. Since support vector regression (SVR) methods have considerable capability of minimizing both empirical and complexity risks simultaneously, the hybrid model of EFF-SVR is expected to yield robust results. Finally, the generalization capability and robustness of EFF-SVR are compared with some existing system modelling methods, i.e., artificial neural network (ANN), adaptive neural-fuzzy inference system (ANFIS), fuzzy function with least squared estimation (FF-LSE), and improved FF-LSE. The results show that EFF-SVR has a great ability as a feasible tool for prediction of the Concrete Compressive Strength.