Prediction of shear strength for squat RC walls using a hybrid ANN-PSO model

被引：58
|
作者：
Chen, X. L. [1 ]
Fu, J. P. [2 ]
Yao, J. L. [1 ]
Gan, J. F. [1 ]
机构：
[1] Chongqing Univ, Dept Civil Engn, Chongqing 400044, Peoples R China
[2] Chongqing Univ, Dept Key Lab New Technol Construct Cities Mt Area, Chongqing 400044, Peoples R China
来源：
ENGINEERING WITH COMPUTERS | 2018年 / 34卷 / 02期
基金：
美国国家科学基金会;
关键词：
Artificial neural network; Hybrid intelligence algorithm; Particle swarm optimization; Squat reinforced concrete walls; Shear strength; NEURAL-NETWORK; SEISMIC BEHAVIOR; PARTICLE SWARM; CONCRETE; SURFACE; DESIGN;
D O I：
10.1007/s00366-017-0547-5
中图分类号：
TP39 [计算机的应用];
学科分类号：
081203 ; 0835 ;
摘要：
The squat reinforced concrete (RC) shear wall having low aspect ratio is a crucial structural component for both conventional buildings and nuclear-related structures due to the substantial role in resisting the lateral seismic loading. The prediction model for shear capacity of these walls becomes essential in ensuring the seismic safety of the building. Therefore, a model to predict the shear strength of squat RC walls has been proposed using a hybrid intelligence algorithm including the artificial neural network and particle swarm optimization algorithm (ANN-PSO). A total of 139 test results of squat walls are collected and utilized to train and test the hybrid ANN-PSO model. The performance of the proposed model has been assessed against the other shear strength models. The proposed model demonstrates good prediction capability with high accuracy for predicting shear strength of the RC walls.
引用
收藏
页码：367 / 383
页数：17
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