Geotechnical data-driven possibility reliability assessment

Tombari, Alessandro ORCID: https://orcid.org/0000-0001-8218-7400; Stefanini, Luciano; Nicosia, Giovanni Li Destri; Holland, Liam M.J.; Dobbs, Marcus. 2025 Geotechnical data-driven possibility reliability assessment. Computers and Geotechnics, 185, 107311. 10.1016/j.compgeo.2025.107311

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Abstract/Summary

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. Step-by-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.

Item Type:	Publication - Article
Digital Object Identifier (DOI):	10.1016/j.compgeo.2025.107311
ISSN:	0266352X
Date made live:	18 Jun 2025 13:29 +0 (UTC)
URI:	https://nora.nerc.ac.uk/id/eprint/539623

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