Applying Machine Learning to Earthquake Engineering: A Scientometric Analysis of World Research
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| 出版年: | Buildings vol. 14, no. 5 (2024), p. 1393 |
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| 第一著者: | |
| その他の著者: | , , |
| 出版事項: |
MDPI AG
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| 主題: | |
| オンライン･アクセス: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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MARC
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| 045 | 2 | |b d20240101 |b d20241231 | |
| 084 | |a 231437 |2 nlm | ||
| 100 | 1 | |a Hu, Yi |u China Minmetals Corporation, Beijing 100000, China; <email>huyi@mcc17.cn</email> (Y.H.); <email>CamelW3@163.com</email> (F.W.); China MCC17 Group Co., Ltd., Maanshan 243000, China | |
| 245 | 1 | |a Applying Machine Learning to Earthquake Engineering: A Scientometric Analysis of World Research | |
| 260 | |b MDPI AG |c 2024 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a 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. | |
| 653 | |a Earthquakes | ||
| 653 | |a Feature extraction | ||
| 653 | |a Collaboration | ||
| 653 | |a Performance evaluation | ||
| 653 | |a Performance prediction | ||
| 653 | |a Concrete | ||
| 653 | |a Data search | ||
| 653 | |a Earthquake resistance | ||
| 653 | |a Citation analysis | ||
| 653 | |a Machine learning | ||
| 653 | |a Computer vision | ||
| 653 | |a Earthquake prediction | ||
| 653 | |a Data mining | ||
| 653 | |a Keywords | ||
| 653 | |a Learning algorithms | ||
| 653 | |a Bibliographic coupling | ||
| 653 | |a Structural health monitoring | ||
| 653 | |a Data processing | ||
| 653 | |a Data analysis | ||
| 653 | |a Computers | ||
| 653 | |a Seismic activity | ||
| 653 | |a Scientometrics | ||
| 653 | |a Materials science | ||
| 653 | |a Bibliometrics | ||
| 653 | |a Artificial intelligence | ||
| 653 | |a Earthquake construction | ||
| 653 | |a Earthquake damage | ||
| 653 | |a Seismic engineering | ||
| 653 | |a Algorithms | ||
| 653 | |a Natural language processing | ||
| 653 | |a Earthquake engineering | ||
| 653 | |a Construction materials | ||
| 653 | |a Information technology | ||
| 653 | |a Software | ||
| 700 | 1 | |a Wang, Wentao |u School of Civil Engineering, Xi’an University of Architecture and Technology, Xi’an 710055, China | |
| 700 | 1 | |a Li, Lei |u School of Civil Engineering, Xi’an University of Architecture and Technology, Xi’an 710055, China | |
| 700 | 1 | |a Wang, Fangjun |u China Minmetals Corporation, Beijing 100000, China; <email>huyi@mcc17.cn</email> (Y.H.); <email>CamelW3@163.com</email> (F.W.); China MCC17 Group Co., Ltd., Maanshan 243000, China | |
| 773 | 0 | |t Buildings |g vol. 14, no. 5 (2024), p. 1393 | |
| 786 | 0 | |d ProQuest |t Engineering Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3059506099/abstract/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3059506099/fulltextwithgraphics/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3059506099/fulltextPDF/embedded/6A8EOT78XXH2IG52?source=fedsrch |