Bayesian Learning Methods for Geotechnical Data

The special collection on Bayesian Learning Methods for Geotechnical Data is available in the ASCE Library (https://ascelibrary.org/ajrua6/bayesian_methods_geotechnical).

Probabilistic methods are frequently criticized as inapplicable to geotechnical data because sample size is small and uncertainties are significant. Because geotechnical data emerge from naturally occurring materials, every site is to some extent unique. Multivariate data (which are commonly encountered in site investigation programs) can be substantially incomplete and typically indirect (that is, a transformation model is needed to convert the measurements to the desired design parameters). One notable feature of big data is veracity. The indicative percentage of outliers in commercial site investigation reports is unknown at present. In short, the characteristics of geotechnical data, which can vary in space and time, can be succinctly described as MUSIC-X: multivariate, uncertain and unique, sparse, incomplete, and potentially corrupted, with X denoting the time and/or spatial dimension (Phoon et al. 2019).

The frequentist approach that remains the dominant paradigm for dealing with uncertainties defines probabilities in terms of the occurrence frequencies of repeated events. Its application to geotechnical engineering has been problematic—the most common limitation is that sample size is too small to characterize statistics with any confidence and to identify the correct probability distribution function. Some engineers also argue that different sites cannot be treated as repeated events because each site is unique in its own ways. This issue is denoted by the U in MUSIC. It is not well recognized among engineers that these objections do not apply to the Bayesian approach. The Bayesian approach is, at heart, a logic for reasoning in the presence of uncertainty in a principled way. The benefits of Bayesian reasoning include natural and unified modeling of many difficult data-driven problems, the ability to accommodate unstructured data, and powerful algorithms for data fitting and analysis under uncertainty.

Although Bayesian methods have gained increasing attention in the last decade, there are still some critical and important issues for further investigation:

In this special collection, the papers address the preceding issues to certain extent. In addition to the theoretical developments, they also cover a variety of physical problems in geotechnical engineering, including estimation of clay parameters, estimation of compressive strength of rock, slope stability, liquefaction, and site investigation. Research has shifted from models that are motivated by theoretical tractability to models that are motivated by features of real data. The guest editors believe that this increasing emphasis on real data and developing fast algorithms to maximize value for decision-making is critical for the digitalization of geotechnical engineering. We hope to attract further attention and interest in this area.