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behavior-prediction
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Predicting the Behavior of a Double-Arched Concrete Dam Using the Machine Learning Technique
, M.Sc. Thesis Sharif University of Technology ; Ghaemian, Mohsen (Supervisor) ; Toufigh, Vahab (Co-Supervisor)
Abstract
In recent years, machine learning techniques have been available to predict and interpret the structural behavior of dams. The main goal of this research is to predict structural behavior through modeling with data read by accurate instruments of dams. This requires the selection of several machine learning methods such as Random Forests (RF), Boosted Regression Trees (BRT), Artificial Neural Networks (ANN), Support Vector Regression (SVR), and Decision Tree Regression (DTR) algorithms. In this research, considering the 24 target variables defined from the detailed instrumentations studied, the behavior of the dam is modeled using selected machine learning models. These precision instruments...
Behavior prediction of corrugated steel plate shear walls with openings
, Article Journal of Constructional Steel Research ; Volume 114 , 2015 , Pages 258-268 ; 0143974X (ISSN) ; Laman, J. A ; Sharif University of Technology
Elsevier Ltd
2015
Abstract
Corrugated steel plate and simple steel plate shear wall construction is a widely accepted and efficient lateral force resisting construction. The widespread use is motivated by the large initial stiffness, high level of energy absorption, and ability to accommodate openings. There is a dearth of information regarding the detailed nonlinear, inelastic behavior of corrugated steel plate shear walls, particularly walls with openings. Presented here are the results of a detailed, numerical parametric study comparing corrugated steel plate and simple steel plate shear walls, with and without openings. Parameters studied are plate thickness, angle of corrugation, opening size, and opening...
Predicting human behavior in size-variant repeated games through deep convolutional neural networks
, Article Progress in Artificial Intelligence ; Volume 11, Issue 1 , 2022 , Pages 15-28 ; 21926352 (ISSN) ; Izadi, M ; Sharif University of Technology
Springer Science and Business Media Deutschland GmbH
2022
Abstract
We present a novel deep convolutional neural network (DCNN) model for predicting human behavior in repeated games. The model is the first deep neural network presented on repeated games that is able to be trained on games with arbitrary size of payoff matrices. Our neural network takes the players’ payoff matrices and the history of the play as input, and outputs the predicted action picked by the first player in the next round. To evaluate the model’s performance, we apply it to some experimental games played by humans and measure the rate of correctly predicted actions. The results show that our model obtains an average prediction accuracy of about 63% across all the studied games, which...