机器学习在地震地震学中的作用:综述

IF 9.7 2区 工程技术 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Anup Chitkeshwar
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引用次数: 0

摘要

本综合调查报告探讨了机器学习(ML)在地震工程领域的应用这一引人注目但相对未知的领域。虽然之前的综述已经涉及到了 ML 的参与,但本研究致力于填补空白,对 ML 在地震工程中的渗透程度进行了广泛分析。它深入探讨了 ML 如何促进和推动研究工作,同时帮助决策者减轻地震灾害对民用建筑的影响。地震工程是一个跨学科领域,包括地震灾害评估、特定场地影响特征描述、结构响应分析、地震风险和脆弱性评估以及地震防护措施检查。ML 算法可应用于上述各子领域的多种情况,有助于推动地震工程研究和实践。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The Role of Machine Learning in Earthquake Seismology: A Review

This comprehensive survey addresses the notable yet relatively uncharted territory of machine learning (ML) applications within the realm of earthquake engineering. While previous reviews have touched on ML’s involvement, this work strives to fill a gap by providing an extensive analysis of the extent to which ML has permeated earthquake engineering. It delves into how ML is facilitating and propelling research endeavors while aiding decision-makers in mitigating the repercussions of seismic hazards on civil structures. Earthquake engineering, an interdisciplinary field, encompasses the assessment of seismic hazards, characterization of site-specific effects, analysis of structural responses, evaluation of seismic risk and vulnerability, and examination of seismic protection measures. ML algorithms find application in a multitude of scenarios within each of these subfields, contributing to advancements in earthquake engineering research and practice.

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来源期刊
CiteScore
19.80
自引率
4.10%
发文量
153
审稿时长
>12 weeks
期刊介绍: Archives of Computational Methods in Engineering Aim and Scope: Archives of Computational Methods in Engineering serves as an active forum for disseminating research and advanced practices in computational engineering, particularly focusing on mechanics and related fields. The journal emphasizes extended state-of-the-art reviews in selected areas, a unique feature of its publication. Review Format: Reviews published in the journal offer: A survey of current literature Critical exposition of topics in their full complexity By organizing the information in this manner, readers can quickly grasp the focus, coverage, and unique features of the Archives of Computational Methods in Engineering.
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