Straightforward Prediction for Responses of the Concrete Shear Wall Buildings Subject to Ground Motions Using Machine Learning Algorithms

被引:17
作者
Barkhordari, M. S. [1 ]
Es-haghi, M. S. [2 ]
机构
[1] Amirkabir Univ Technol, Dept Civil & Environm Engn, Tehran, Iran
[2] Khajeh Nasir Toosi Univ Technol, Sch Civil Engn, Tehran, Iran
来源
INTERNATIONAL JOURNAL OF ENGINEERING | 2021年 / 34卷 / 07期
关键词
Artificial Neural Networks; Regression Model; Tall Buildings; Seismic Response; REGRESSION; SELECTION; MODEL;
D O I
10.5829/ije.2021.34.07a.04
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
The prediction of responses of the reinforced concrete shear walls subject to strong ground motions is critical in designing, assessing, and deciding the recovery strategies. This study evaluates the ability of regression models and a hybrid technique (ANN-SA model), the Artificial Neural Network (ANN), and Simulated Annealing (SA), to predict responses of the reinforced concrete shear walls subject to strong ground motions. To this end, four buildings (15, 20, 25, and 30-story) with concrete shear walls were analyzed in OpenSees.150 seismic records are used to generate a comprehensive database of input (characteristics of records) and output (responses). The maximum acceleration, maximum velocity, and earthquake characteristics are used as predictors. Different machine learning models are used, and the accuracy of the models in identifying the responses of the shear walls is compared. The sensitivity of input variables to the seismic demand model is investigated. It has been seen from the results that the ANN-SA model has reasonable accuracy in the prediction.
引用
收藏
页码:1586 / 1601
页数:16
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