Prediction of Mechanical Behavior of Concrete Filled Steel Tube Structure Using Artificial Neural Network

被引：5

作者：

Wang Ying ^{[1
]}

Liu Zhiquan ^{[1
]}

Zhang Ming ^{[2
]}

机构：

[1] Shenyang Univ Technol, Sch Architecture & Civil Engn, 111 Shenliao West Rd, Shenyang 110870, Peoples R China

[2] Eastern Liaoning Univ, Sch Mech & Elect, Dandong 118003, Peoples R China

来源：

FRONTIERS OF GREEN BUILDING, MATERIALS AND CIVIL ENGINEERING III, PTS 1-3 | 2013年 / 368-370卷

关键词：

concrete filled steel tube structure; artificial neural network; load-strain relationship; prediction model;

D O I：

10.4028/www.scientific.net/AMM.368-370.1095

中图分类号：

TU [建筑科学];

学科分类号：

0813 ;

摘要：

Artificial neural network (ANN) is applied to predict load-strain relationship of concrete filled steel tube (CFT) structural parts. An ANN prediction model, which is able to predict load-strain relationship of CFT structural parts with different dimensions and parameters, is made through training the ANN prediction model with the experimental test data. Furthermore, the prediction data and experimental test data are compared. The result shows that the combination of several characteristic parameters of CFT structural parts and ANN prediction model to predict load-strain relationship of CFT structural parts are reliable and feasible. The ANN prediction model has simple, convenience and time-saving merits.

引用

页码：1095 / +

页数：2