Machine Learning Applications in Geotechnical Earthquake Engineering: Progress, Gaps, and Opportunities
Machine Learning Applications in Geotechnical Earthquake Engineering: Progress, Gaps, and Opportunities
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DOI：
10.1061/9780784484692.050
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发表时间：
2023-03
期刊：
影响因子：
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通讯作者：
Katherine Cheng;K. Ziotopoulou
中科院分区：
文献类型：
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作者：
Katherine Cheng;K. Ziotopoulou
数据捕获和存储能力的提高导致了岩土地震工程中更大的数据粒度和数据集的共享。向大数据的这种更广泛的转变需要各种方法来处理大数据并从中提取价值，这得益于计算机科学领域方法论的进步和计算机硬件能力的进步。通用机器学习(ML)模型通常接收一组输入参数，并通过算法运行它们以获得输出，而不受参数或算法过程的限制。回顾和总结了ML在岩土地震工程中的三个应用领域：地震响应、液化触发分析和基于性能的评估(侧向位移和沉降分析)。总结了最大似然法目前的进展，同时指出了采用这种方法的挑战和潜力。
The rise of data capture and storage capabilities have led to greater data granularity and sharing of data sets in geotechnical earthquake engineering. This broader shift to big data requires ways to process and extract value from it and is aided by the progress in methodologies from the computer science domain and advancements in computer hardware capabilities. General machine learning (ML) models typically receive a set of input parameters and run them through an algorithm to gain outputs with no constraints on the parameters or algorithm process. Three topic areas of ML applications in geotechnical earthquake engineering are reviewed and summarized in this paper: seismic response, liquefaction triggering analysis, and performance-based assessments (lateral displacements and settlement analysis). The current progress of ML is summarized, while the challenges and potential in adopting such approaches are addressed.