A rigorous possibility approach for the geotechnical reliability assessment supported by external database and local experience
A rigorous possibility approach for the geotechnical reliability assessment supported by external database and local experience
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DOI：
10.1016/j.compgeo.2023.105967
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发表时间：
2024-02
影响因子：
5.3
通讯作者：
A. Tombari;Marcus Dobbs;Liam M.J. Holland;Luciano Stefanini
中科院分区：
文献类型：
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作者：
A. Tombari;Marcus Dobbs;Liam M.J. Holland;Luciano Stefanini
基于概率论的可靠度分析在岩土工程中得到了广泛的应用，目前已有几种基于破坏发生概念的分析或数值方法。然而，常见的岩土工程现实问题处理稀缺或稀疏的信息，其中实验数据并不总是可用到足够的程度和质量来推断可靠的概率分布函数。本文严格结合模糊聚类和可能性理论，在有用但异构的信息源可用时，除了完全面向概率的评估之外，还推导出数据驱动的、定量的、可靠性的方法。当考虑理想随机数据时，所提出的非概率方法与失效概率在数学上是一致的。此外，当数据不确定、稀缺和稀疏时，它提供了一个健壮的工具来解释认知不确定性。利用平均累积函数变换得到间接数据库的模糊聚类推断的可能性分布。目标可靠性指标值与欧洲规范0规定的值一致。此外，还提出了一种基于从业者本地经验的理解程度等级体系。本文详细讨论了使用国家规模数据库的两个数值示例，强调了与传统概率方法相比的潜在优势。
Reliability analyses based on probability theory are widely applied in geotechnical engineering, and several analytical or numerical methods have been built upon the concept of failure occurrence. Nevertheless, common geotechnical engineering real-world problems deal with scarce or sparse information where experimental data are not always available to a sufficient extent and quality to infer a reliable probability distribution function.This paper rigorously combines Fuzzy Clustering and Possibility Theory for deriving a data-driven, quantitative, reliability approach, in addition to fully probability-oriented assessments, when useful but heterogeneous sources of information are available.The proposed non-probabilistic approach is mathematically consistent with the failure probability, when ideal random data are considered. Additionally, it provides a robust tool to account for epistemic uncertainties when data are uncertain, scarce, and sparse. The Average Cumulative Function transformation is used to obtain possibility distributions inferred from the fuzzy clustering of an indirect database. Target Reliability Index Values, consistent with the prescribed values provided by Eurocode 0, are established.Moreover, a Degree of Understanding tier system based on the practitioner’s local experience is also proposed. The proposed methodology is detailed and discussed for two numerical examples using national-scale databases, highlighting the potential benefits compared to traditional probabilistic approaches.