Select liquefaction case histories from the 2001 Nisqually, Washington, earthquake: A digital data set and assessment of model performance
Select liquefaction case histories from the 2001 Nisqually, Washington, earthquake: A digital data set and assessment of model performance
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DOI：
10.1177/87552930231174244
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发表时间：
2023-05
影响因子：
5
通讯作者：
Ryan A Rasanen;M. Geyin;B. Maurer
中科院分区：
文献类型：
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作者：
Ryan A Rasanen;M. Geyin;B. Maurer
虽然土壤液化在地震中很常见，但由于构成单个案例历史的数据广泛且昂贵，训练和测试实践状态预测模型所需的案例历史数据仍然相对稀缺。例如，2001 年华盛顿州尼斯夸利地震发生在大都市地区，并在西雅图和奥林匹亚的城市核心引发了破坏性液化，但此前尚未公布过案例历史数据。因此，本文编译了来自自由场位置的 24 个锥体渗透测试 (CPT) 案例历史。本文详细介绍了用于获取和处理数据的许多方法，以及数字数据集的结构。然后通过 18 个现有的液化响应模型对案例历史进行分析，以确定是否有更好的模型，并将 Nisqual 的模型性能与全球观测结果进行比较。虽然测量了模型之间和之前的全球案例历史之间的差异，但考虑到有限样本的不确定性，这些差异在统计上通常是微不足道的。这暗示了基于单个地震或其他小数据集的支持模型普遍不合适，也暗示了对额外案例历史数据的持续需求以及更严格地遵守模型训练和测试中的最佳实践。
While soil liquefaction is common in earthquakes, the case-history data required to train and test state-of-practice prediction models remains comparatively scarce, owing to the breadth and expense of data that comprise a single case history. The 2001 Nisqually, Washington, earthquake, for example, occurred in a metropolitan region and induced damaging liquefaction in the urban cores of Seattle and Olympia, yet case-history data have not previously been published. Accordingly, this article compiles 24 cone-penetration-test (CPT) case histories from free-field locations. The many methods used to obtain and process the data are detailed herein, as is the structure of the digital data set. The case histories are then analyzed by 18 existing liquefaction response models to determine whether any is better, and to compare model performance in Nisqually against global observations. While differences are measured, both between models and against prior global case histories, these differences are often statistically insignificant considering finite-sample uncertainty. This alludes to the general inappropriateness of championing models based on individual earthquakes or otherwise small data sets, and to the ongoing needs for additional case-history data and more rigorous adherence to best practices in model training and testing.