Neural networks model and adaptive neuro-fuzzy inference system for predicting the moment capacity of ferrocement members

被引：35
|
作者：
Mashrei, Mohammed A. [1 ]
Abdulrazzaq, Nabeel [1 ]
Abdalla, Turki Y. [1 ]
Rahman, M. S. [1 ]
机构：
[1] N Carolina State Univ, Dept Civil Engn, Raleigh, NC 27695 USA
来源：
ENGINEERING STRUCTURES | 2010年 / 32卷 / 06期
关键词：
Ferrocement; Moment; Neural networks adaptive neuro-fuzzy inference system; SHEAR DESIGN PROCEDURE; CONCRETE BEAMS; STRENGTH; STIRRUPS;
D O I：
10.1016/j.engstruct.2010.02.024
中图分类号：
TU [建筑科学];
学科分类号：
0813 ;
摘要：
In this paper, back-propagation neural networks (BPNN) and an adaptive neuro-fuzzy inference system (ANFIS) models developed to predict the moment capacity of ferrocement members are presented. A database from tests on ferrocement members is developed from the review of literature and some new tests. The selected input variables include the width and the depth of specimens, cube compressive strength of mortar, and tensile strength and volume fraction of wire mesh. A parametric study is carried out using BPNN to study the influence of each parameter affecting the moment capacity of the ferrocement member. The results of this study indicate that both BPNN and ANFIS provide good predictions which are better than those from other available methods. These models can serve as reliable and simple predictive tools for the prediction of moment capacity of ferrocement members. (C) 2010 Elsevier Ltd. All rights reserved.
引用
收藏
页码：1723 / 1734
页数：12
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