Machine learning in structural engineering

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 p...

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Vydané v:Scientia Iranica. Transaction A, Civil engineering Ročník 27; číslo 6; s. 2645 - 2656
Hlavní autori: Amezquita-Sanchez, J P, Valtierra-Rodriguez, M, Adeli, H
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Tehran Sharif University of Technology 01.12.2020
Predmet:
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
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Shrnutí: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.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
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content type line 14
DOI:10.24200/sci.2020.22091