Special issue on “Machine learning and AI in geotechnics”

Abstract

The potential for machine learning and artificial intelligence to shape geotechnical engineering practice (and possibly theory) is immense. However, the agenda for machine learning in geotechnics should not be focused on applying or developing algorithms alone. The geotechnical context that gives rise to the data is important. The context can be related to statistics, physics, or experience. Statistics refer to the attributes of geotechnical data that depart significantly from the assumptions in classical statistics (large sample size, spatial/temporal/parametric independence, homogeneity, normality, etc.). Phoon, Ching, and Shuku (2022a) argued that geotechnical site data are “ugly”, because they are spatially varying, sparse, site-specific (or unique to some extent), and incomplete in the sense that a multivariate database is full of empty entries denoting lack of some measurements at certain locations/depths. The incompleteness attribute arises from an intent to maximize information on cross correlations between different soil parameters and geotechnical/geologic spatial correlations across a given site while minimizing the site investigation budget. At this point, this value of information optimization is an art rather than a science. The scientific challenge to draw useful insights from MUSIC-3X (Multivariate, Uncertain and Unique, Sparse, Incomplete, and potentially Corrupted with “3X” denoting 3D spatial variability) data was thought to be intractable until recently (Phoon, Ching, and Shuku 2022a). These “ugly” data attributes are the norm in a site investigation report. In rock engineering, data can be categorical rather than numerical. Phoon (2023) emphasized that “decision making in every discipline is supported by its own data with unique attributes and a tradition of successful practice (investigation, design, construction, testing, monitoring, and risk management methodology) that evolved to make the best use of these data and prevailing technologies”. Physics refers to a body of rational knowledge that associates a “number” to “meaning”. Decisions supported by physics-informed results are “explainable” and “interpretable”. The finite element method is the most prevalent embodiment of physics in geotechnical engineering. Using finite element analysis, an engineer understands cause and effect (interpretability) and knows which input parameters affect the outputs (explainability). An engineer distinguishes between material and state parameters, between effective and total stress parameters, and between input and output parameters from a physical or numerical model. These distinctions exist when one approaches data from the lens of physics. Experience refers to a body of empirical knowledge accrued from deliberate practice. It is restricted by the range of projects encountered by an engineer over his/ her working life and it cannot be shared with other engineers efficiently. In contrast to statistics and physics, it is mainly subjective and qualitative. Nonetheless, many engineers regard experience as critical. For example, Simpson (2011) explained why Eurocode 7 (EC7) is worded to ensure an engineer always take full ownership in decision making: “EC7 attempts to do this by making the designer responsible for the selection of the characteristic values of materials, avoiding mathematical prescription of their derivation. Inevitably, such a process leads to values affected by the subjective experience, knowledge and judgement of the designer. The author would contest that it is better to accept such subjectivity than to discard the valuable information it provides”. In machine learning, experience is regarded as one type of “thick data” to distinguish it from the more well-known quantitative “big data”. Decision making in current practice is based on physics and experience. There is no formal basis underpinning decision making other than qualitative guidelines such as Burland’s Triangle (Burland 1987; Phoon et al. 2022b) or Wroth rules (Wroth 1984; Phoon 2023). As such, geotechnical practice is regarded more of an “art” than a “science”. Phoon (2023) argued that decision making will be increasingly data-informed given the increasing power, ubiquity, and convergence of digital technologies and presented a data-informed decision support index (DIDI) to track this evolution. Geotechnical reliability is regarded as one stage with