Bridge (graph theory)

Bayesian probability

Deep learning

Machine learning

Physics

Data mining

Internal medicine

Engineering

Artificial intelligence

Bayesian inference

Frequentist inference

Power (physics)

Geotechnical engineering

Feature engineering

Uncertainty quantification

Computer science

Reliability (semiconductor)

Probabilistic logic

Quantum mechanics

Medicine

Geotechnical investigation

Geotechnics

Georisk Assessment and Management of Risk for Engineered Systems and Geohazards

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 are reviewed for the past six decades. 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 or other methods are proposed for solving soil-structure interaction problems in spatially variable soil. For the second feature, compilation of databases is essential to serve as priors for Bayesian updating and more recently for Bayesian machine learning. There is a gradual evolution towards reliability-based design 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 practice. 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