Neurocomputing in civil infrastructure

被引：43

作者：

Amezquita-Sanchez, J. P. ^{[1
,2
,3
]}

Valtierra-Rodriguez, M. ^{[1
,3
]}

Aldwaik, M. ^{[4
]}

Adeli, H. ^{[4
]}

机构：

[1] Autonomous Univ Queretaro, Fac Engn, Dept Electromech Engn, Campus San Juan del Rio,Moctezuma 249, San Juan Del Rio 76807, Queretaro, Mexico

[2] Autonomous Univ Queretaro, Fac Engn, Dept Civil Engn, Campus San Juan del Rio,Moctezuma 249, San Juan Del Rio 76807, Queretaro, Mexico

[3] Autonomous Univ Queretaro, Fac Engn, Dept Biomed Engn, Campus San Juan del Rio,Moctezuma 249, San Juan Del Rio 76807, Queretaro, Mexico

[4] Ohio State Univ, Dept Civil Environm & Geodet Engn, 470 Hitchcock Hall,2070 Neil Ave, Columbus, OH 43220 USA

来源：

SCIENTIA IRANICA | 2016年 / 23卷 / 06期

关键词：

Artificial Neural Networks; Civil Structures; System Identification; Structural Health Monitoring; Control; Prediction; Optimization; Construction; Geotechnical; WAVELET NEURAL-NETWORK; FREQUENCY-RESPONSE FUNCTIONS; TRUSS-TYPE STRUCTURE; DAMAGE DETECTION; STRUCTURAL OPTIMIZATION; OPTIMUM DESIGN; NONLINEAR IDENTIFICATION; BUILDING STRUCTURES; VIBRATION CONTROL; MODAL PARAMETERS;

D O I：

10.24200/sci.2016.2301

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

This article presents a review of the recent applications of Artificial Neural Networks (ANN) for civil infrastructure including structural system identification, structural health monitoring, structural vibration control, structural design and optimization, prediction applications, construction engineering, and geotechnical engineering. The most common ANN used in structural engineering is the backpropagation neural network followed by recurrent neural networks and radial basis function neural networks. In recent years, a number of researchers have used newer hybrid techniques in structural engineering such as the neuro-fuzzy inference system, time-delayed neuro-fuzzy inference system, and wavelet neural networks. Deep machine learning techniques are among the newest techniques to find applications in civil infrastructure systems. (C) 2016 Sharif University of Technology. All rights reserved.

引用

页码：2417 / 2428

页数：12

共 124 条

[11] Prediction of principal ground-motion parameters using a hybrid method coupling artificial neural networks and simulated annealing [J].