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Probabilistic framework for regional loss assessment due to earthquake-induced liquefaction including epistemic uncertainty
Soil Dynamics and Earthquake Engineering ( IF 4 ) Pub Date : 2021-02-01 , DOI: 10.1016/j.soildyn.2020.106493
Cigdem Yilmaz , Vitor Silva , Graeme Weatherill

Abstract For seismic risk assessments, ground shaking is generally considered the primary cause of building damage. However, earthquake-induced liquefaction can also create significant structural damage and losses. Predicting the expected degree of damage in structures exposed to ground failure due to liquefaction is thus essential for seismic risk assessment. There are well-established tools and methods to evaluate the seismic risk due to ground shaking. However, despite the large number of studies related to liquefaction assessments, modelling ground failure due to an earthquake is far less mature. The incorporation of liquefaction into a seismic loss estimation framework presents several challenges. The use of simplified procedures requires a multitude of assumptions, which increase the uncertainty in the loss assessment, whilst detailed approaches based on in-situ data are resource-demanding and impractical for large-scale risk analysis. To address these shortcomings, a fully probabilistic hazard and risk assessment due to liquefaction has been employed using the OpenQuake-engine and demonstrated through a case study applied to mainland Portugal. The liquefaction hazard and risk assessment framework followed in this study allows for the incorporation of both aleatory and epistemic uncertainties, which are herein explored through a sensitivity analysis.

中文翻译:

由于地震引起的液化引起的区域损失评估的概率框架,包括认知不确定性

摘要 对于地震风险评估,地面震动通常被认为是建筑物损坏的主要原因。然而,地震引起的液化也会造成重大的结构损坏和损失。因此,预测因液化而遭受地面破坏的结构的预期损坏程度对于地震风险评估至关重要。已有完善的工具和方法来评估由地面震动引起的地震风险。然而,尽管有大量与液化评估相关的研究,但对地震引起的地面破坏进行建模还远远不够成熟。将液化纳入地震损失估计框架提出了几个挑战。使用简化程序需要大量假设,这增加了损失评估的不确定性,而基于原位数据的详细方法需要资源,对于大规模风险分析不切实际。为了解决这些缺点,已使用 OpenQuake 引擎对液化引起的完全概率危害和风险评估进行了评估,并通过适用于葡萄牙大陆的案例研究进行了证明。本研究中遵循的液化危害和风险评估框架允许纳入随机和认知不确定性,本文通过敏感性分析对其进行了探讨。
更新日期:2021-02-01
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