Title of article :
Neurocomputing in Civil Infrastructure
Author/Authors :
amezquita-sanchez, j.p. department of electromechanical - university of quertaro campus - mexico , valtierra-rodriguez, m. department of electromechanical - university of quertaro campus - mexico , aldwaik, m. department of civil engineering - the ohio state university - USA , adeli, h. department of civil engineering - the ohio state university - USA
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 :
Artificial neural networks , Civil structures , System Identification , Structural health monitoring , Control , Prediction , Optimization , construction , geotechnical
Journal title :
Astroparticle Physics