SummaryIn this study, an Artificial Neural Networks (ANN) model is built and verified for quick estimation of the structural parameter obtained for a concrete gravity dam section due to seismic excitation. The database of numerous inputs and outputs obtained through Abaqus which are further converted into dimensionless forms in the statistical software (MATLAB) to build the ANN model. The developed model can be used for accurate estimation of this parameter. The results showed an excellent capability of the model to predict the outputs with high accuracy and reduced computational time
The selection of a given method for the seismic vulnerability assessment of buildings is mostly depe...
Artificial Neural Network (ANN) method is a prediction tool which is widely used in various fields o...
The objective of this study is to investigate the adequacy of Artificial Neural Networks (ANN) as a ...
SummaryIn this study, an Artificial Neural Networks (ANN) model is built and verified for quick esti...
In this study, an Artificial Neural Networks (ANN) model is built and verified for quick estimation ...
The goal of this paper is to assess the effectiveness of using artificial neural networks in the pre...
Fragility function that defines the probability of exceedance of a damage state given a ground motio...
Deformation predicting models are essential for evaluating the health status of concrete dams. Never...
This study utilizes Artificial Neural Networks to predict the structural responses multi-story reinf...
Soutenue à l'université de Tlemcen (Algérie)The main purpose of this works is to analyze the ability...
This paper discusses the adoption of Artificial Intelligence-based techniques to estimate seismic da...
This research intends to develop a method based on the Artificial Neural Network (ANN) to predict pe...
Neural networks have emerged as a powerful computational technique for modeling nonlinear multivaria...
This paper investigates the potential application of artificial neural networks in permanent deforma...
Summarization: Geotechnical earthquake engineering may generally be considered as an “imprecise” sci...
The selection of a given method for the seismic vulnerability assessment of buildings is mostly depe...
Artificial Neural Network (ANN) method is a prediction tool which is widely used in various fields o...
The objective of this study is to investigate the adequacy of Artificial Neural Networks (ANN) as a ...
SummaryIn this study, an Artificial Neural Networks (ANN) model is built and verified for quick esti...
In this study, an Artificial Neural Networks (ANN) model is built and verified for quick estimation ...
The goal of this paper is to assess the effectiveness of using artificial neural networks in the pre...
Fragility function that defines the probability of exceedance of a damage state given a ground motio...
Deformation predicting models are essential for evaluating the health status of concrete dams. Never...
This study utilizes Artificial Neural Networks to predict the structural responses multi-story reinf...
Soutenue à l'université de Tlemcen (Algérie)The main purpose of this works is to analyze the ability...
This paper discusses the adoption of Artificial Intelligence-based techniques to estimate seismic da...
This research intends to develop a method based on the Artificial Neural Network (ANN) to predict pe...
Neural networks have emerged as a powerful computational technique for modeling nonlinear multivaria...
This paper investigates the potential application of artificial neural networks in permanent deforma...
Summarization: Geotechnical earthquake engineering may generally be considered as an “imprecise” sci...
The selection of a given method for the seismic vulnerability assessment of buildings is mostly depe...
Artificial Neural Network (ANN) method is a prediction tool which is widely used in various fields o...
The objective of this study is to investigate the adequacy of Artificial Neural Networks (ANN) as a ...