This paper is motivated by the time capsule project (TCP) of the International Society for Soil Mechanics and Geotechnical Engineering (ISSMGE). The historical developments of geotechnical risk and reliability primarily for soil mechanics was covered over the past six or more decades (1960 – 2010+). Key milestones include application of probability to quantify uncertainties and compute probability of failure, spatial variability, random field theory, first-order reliability method, random finite element method, reliability-based design, load and resistance factor design, Bayesian updating, soil and load test databases, and machine learning methods. Given the complexity of natural ground, engineering judgment remains important to bridge the gap between theory and reality. However, the role of engineering judgment needs to be updated as modern machine learning methods become more powerful.

Time capsule for geotechnical risk and reliability / Marcin Chwała, Kok-Kwang Phoon, Marco Uzielli, Jie Zhang, Limin Zhang, Jianye Ching. - In: GEORISK. - ISSN 1749-9526. - ELETTRONICO. - -:(2022), pp. 0-0. [10.1080/17499518.2022.2136717]

Time capsule for geotechnical risk and reliability

Marco Uzielli
Writing – Original Draft Preparation
;
2022

Abstract

This paper is motivated by the time capsule project (TCP) of the International Society for Soil Mechanics and Geotechnical Engineering (ISSMGE). The historical developments of geotechnical risk and reliability primarily for soil mechanics was covered over the past six or more decades (1960 – 2010+). Key milestones include application of probability to quantify uncertainties and compute probability of failure, spatial variability, random field theory, first-order reliability method, random finite element method, reliability-based design, load and resistance factor design, Bayesian updating, soil and load test databases, and machine learning methods. Given the complexity of natural ground, engineering judgment remains important to bridge the gap between theory and reality. However, the role of engineering judgment needs to be updated as modern machine learning methods become more powerful.
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Goal 11: Sustainable cities and communities
Goal 15: Life on land
Marcin Chwała, Kok-Kwang Phoon, Marco Uzielli, Jie Zhang, Limin Zhang, Jianye Ching
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2158/1285192
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