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Applying Machine Learning to Earthquake Engineering: A Scientometric Analysis of World Research

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Pubblicato in:Buildings vol. 14, no. 5 (2024), p. 1393
Autore principale: Hu, Yi
Altri autori: Wang, Wentao, Li, Lei, Wang, Fangjun
Pubblicazione:
MDPI AG
Soggetti:
Earthquakes
Feature extraction
Collaboration
Performance evaluation
Performance prediction
Concrete
Data search
Earthquake resistance
Citation analysis
Machine learning
Computer vision
Earthquake prediction
Data mining
Keywords
Learning algorithms
Bibliographic coupling
Structural health monitoring
Data processing
Data analysis
Computers
Seismic activity
Scientometrics
Materials science
Bibliometrics
Artificial intelligence
Earthquake construction
Earthquake damage
Seismic engineering
Algorithms
Natural language processing
Earthquake engineering
Construction materials
Information technology
Software
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Abstract:Machine Learning (ML) has developed rapidly in recent years, achieving exciting advancements in applications such as data mining, computer vision, natural language processing, data feature extraction, and prediction. ML methods are increasingly being utilized in various aspects of seismic engineering, such as predicting the performance of various construction materials, monitoring the health of building structures or components, forecasting their seismic resistance, predicting potential earthquakes or aftershocks, and evaluating the residual performance of post-earthquake damaged buildings. This study conducts a scientometric-based review on the application of machine learning in seismic engineering. The Scopus database was selected for the data search and retrieval. During the data analysis, the sources of publications relevant to machine learning applications in seismic engineering, relevant keywords, influential authors based on publication count, and significant articles based on citation count were identified. The sources, keywords, and publications in the literature were analyzed and scientifically visualized using the VOSviewer software tool. The analysis results will help researchers understand the trending and latest research topics in the related field, facilitate collaboration among researchers, and promote the exchange of innovative ideas and methods.
ISSN:2075-5309
DOI:10.3390/buildings14051393
Fonte:Engineering Database