Although fragility function development for structures is a mature field, it has recently thrived on new algorithms propelled by machine learning (ML) methods along with heightened emphasis on functions tailored for community- to regional-scale application. This article seeks to critically assess the implications of adopting alternative traditional and emerging fragility modeling practices within seismic risk and resilience quantification to guide future analyses that span from the structure to infrastructure network scale. For example, this article probes the similarities and differences in traditional and ML techniques for demand modeling, discusses the shift from one-parameter to multiparameter fragility models, and assesses the variations in fragility outcomes via statistical distance concepts. Moreover, the previously unexplored influence of these practices on a range of performance measures (e.g. conditional probability of damage, risk of losses to individual structures, portfolio risks, and network recovery trajectories) is systematically evaluated via the posed statistical distance metrics. To this end, case studies using bridges and transportation networks are leveraged to systematically test the implications of alternative seismic fragility modeling practices. The results show that, contrary to the classically adopted archetype fragilities, parameterized ML-based models achieve similar results on individual risk metrics compared to structure-specific fragilities, promising to improve portfolio fragility definitions, deliver satisfactory risk and resilience outcomes at different scales, and pinpoint structures whose poor performance extends to the global network resilience estimates. Using flexible fragility models to depict heterogeneous portfolios is expected to support dynamic decisions that may take place at different scales, space, and time, throughout infrastructure systems.

中文翻译：

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脆弱性建模实践及其对风险和弹性分析的影响：从结构到网络规模

尽管结构的脆弱性功能开发是一个成熟的领域，但它最近在机器学习 (ML) 方法推动的新算法以及对为社区到区域规模应用量身定制的功能的高度重视的推动下蓬勃发展。本文旨在批判性地评估在地震风险和复原力量化中采用替代传统和新兴脆弱性建模实践的影响，以指导未来从结构到基础设施网络规模的分析。例如，本文探讨了需求建模的传统技术和机器学习技术的异同，讨论了从单参数脆弱性模型到多参数脆弱性模型的转变，并通过统计距离概念评估脆弱性结果的变化。此外，通过所提出的统计距离度量，系统地评估了这些实践对一系列性能指标（例如，损坏的条件概率、单个结构的损失风险、投资组合风险和网络恢复轨迹）的先前未探索的影响。为此，利用桥梁和交通网络的案例研究来系统地测试替代地震脆弱性建模实践的影响。结果表明，与传统采用的原型脆弱性相反，基于机器学习的参数化模型与特定结构的脆弱性相比，在个体风险指标上取得了相似的结果，有望改善投资组合脆弱性定义，在不同规模下提供令人满意的风险和弹性结果，并且查明性能不佳延伸到全球网络弹性估计的结构。使用灵活的脆弱性模型来描述异构投资组合有望支持整个基础设施系统中可能在不同规模、空间和时间发生的动态决策。