Machine learning in structural engineering

Document Type : Review Article

Authors

1 ENAP-RG, CA Sistemas Dinámicos, 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

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

10.24200/sci.2020.22091

Abstract

This article presents a review of selected articles about structural engineering applications of machine learning (ML) in the past few years. It is divided into the following areas: structural system identification, structural health monitoring, structural vibration control, structural design, and prediction applications. Deepneural networkalgorithms have beenthe subject of a large number of articles in civil and structural engineering.There are, however, otherML algorithms with great potential in civil and structural engineering that are worth exploring. Four novel supervised ML algorithms developed recently by the senior author and his associates with potential applications in civil/structural engineering are reviewed in this paper. They are the Enhanced Probabilistic Neural Network (EPNN), the Neural Dynamic Classification (NDC) algorithm, the Finite Element Machine (FEMa), and the Dynamic Ensemble Learning (DEL) algorithm

Keywords


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