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Geotechnical data-driven possibility reliability assessment
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DOI：10.1016/j.compgeo.2025.107311.png)
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Abstract
En 中文
Managing scarce, incomplete, or corrupted data is a persistent challenge in geotechnical engineering, often leading to conservative designs. However, the ongoing digitalization has enabled access to large, national-scale databases of indirect geotechnical data containing both qualitative and quantitative information, which can be exploited to support optioneering, site characterization, and design. Based on a newly proposed concept of possibilistic data-driven reliability, this Technical Note outlines a practical, fast, and accessible implementation procedure that does not require specialized expertise. Stepby-step guidance is provided for reliability-based assessment and design of geotechnical problems, ensuring consistency with standard code safety prescriptions. The procedure demonstrates how to utilize possibility distributions generated from Big Indirect Databases managed by third-party administrators, such as the British Geological Survey, to derive design input values for deterministic evaluations of geotechnical capacity or limit state domains. Engineering judgement is rigorously incorporated through a three-tier 'degree of understanding' framework worked example of an axially-loaded pile in bilayer soil, characterized using cone penetration test data, is also provided.
Keywords:
Possibility theory
Data-driven methods
Fuzzy clustering and partitioning
Degree of understanding
Reliability assessment
Piles
Journal
IF:
6.2
Papers:
7.0K
Citations:
2.9W
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