Machine learning in structural engineering

• Published in: Scientia Iranica. Transaction A, Civil engineering Vol. 27; no. 6; pp. 2645 - 2656
• Main Authors: Amezquita-Sanchez, J P, Valtierra-Rodriguez, M, Adeli, H
• Format: Journal Article
• Language: English
• Published: Tehran Sharif University of Technology 01.12.2020
• Subjects: Algorithms; Artificial intelligence; Artificial neural networks; Brain research; Civil engineering; Classification; Clustering; Decision trees; Deep learning; Earthquakes; High rise buildings; Identification; Machine learning; Neural networks; Structural design; Structural engineering; Structural health monitoring; Structural vibration; Support vector machines; System identification; Time series; Vibration; Vibration control; Vibration monitoring; Wavelet transforms
• DOI: 10.24200/sci.2020.22091

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. Deep neural network algorithms have been the subject of a large number of articles in civil and structural engineering. There are, however, other ML 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.