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Time capsule for geotechnical risk and reliability

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+). The key features distinguishing geotechnical and structural engineering are the natural origin of the ground and the lack of sufficient data to characterize the ground using the more familiar frequentist interpretation of probability. For the first feature, random field theory is applied to model spatial variability and the random finite element method is applied to solve soil-structure interaction problems in spatially variable soils. For the second feature, compilation of databases is essential to serve as priors for Bayesian updating and the more recent research in Bayesian machine learning. There is a gradual evolution towards reliability-based design as a result of this body of research. Interest in both research and practice has heightened significantly, because probabilistic methods offer a pathway to address big data and implement data-centric geotechnics as one step towards digital transformation. Given the complexity of the natural ground (known unknowns can be large and there are unknown unknowns), 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.