Neurocomputing in Civil Infrastructure

Authors

1 Faculty of Engineering, Departments of Electromechanical, Civil, and Biomedical Engineering. Autonomous University of Queretaro, Campus San Juan del Rio, Moctezuma 249, Col. San Cayetano, 76807, San Juan del Rio, Queretaro, Mexico

2 Faculty of Engineering, Departments of Electromechanical and Biomedical Engineering. Autonomous University of Queretaro, Campus San Juan del Rio, Moctezuma 249, Col. San Cayetano, 76807, San Juan del Rio, Queretaro, Mexico

3 Department of Civil, Environmental, and Geodetic Engineering, The Ohio State University, 470 Hitchcock Hall, 2070 Neil Avenue, Columbus, OH 43220 U.S.A

4 Department of Civil, Environmental, and Geodetic Engineering, The Ohio State University, 470 Hitchcock Hall, 2070 Neil Avenue, Columbus, OH 43220 U.S.A.

Abstract

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, newer hybrid techniques have been used in structural engineering by a number of researchers 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.

Keywords