Determination of site-specific soil-water characteristic curve from a limited number of test data-A Bayesian perspective

Determining soilewater characteristic curve(SWCC) at a site is an essential step for implementing unsaturated soil mechanics in geotechnical engineering practice, which can be measured directly through various in-situ and/or laboratory tests. Such direct measurements are, however, costly and timeconsuming due to high standards for equipment and procedural control and limits in testing apparatus. As a result, only a limited number of data points(e.g., volumetric water content vs. matric suction)on SWCC at some values of matric suction are obtained in practice. How to use a limited number of data points to estimate the site-specific SWCC and to quantify the uncertainty(or degrees-of-belief) in the estimated SWCC remains a challenging task. This paper proposes a Bayesian approach to determine a site-specific SWCC based on a limited number of test data and prior knowledge(e.g., engineering experience and judgment). The proposed Bayesian approach quantifies the degrees-of-belief on the estimated SWCC according to site-specific test data and prior knowledge, and simultaneously selects a suitable SWCC model from a number of candidates based on the probability logic. To address computational issues involved in Bayesian analyses, Markov Chain Monte Carlo Simulation(MCMCS), specifically Metropolis-Hastings(M-H) algorithm, is used to solve the posterior distribution of SWCC model parameters, and Gaussian copula is applied to evaluating model evidence based on MCMCS samples for selecting the most probable SWCC model from a pool of candidates. This removes one key limitation of the M-H algorithm, making it feasible in Bayesian model selection problems. The proposed approach is illustrated using real data in Unsaturated Soil Database(UNSODA) developed by U.S. Department of Agriculture. It is shown that the proposed approach properly estimates the SWCC based on a limited number of site-specific test data and prior knowledge, and reflects the degrees-of-belief on the estimated SWCC in a rational and quantitative manner.

Probabilistic outlier detection for sparse multivariate geotechnical site investigation data using Bayesian learning

Various uncertainties arising during acquisition process of geoscience data may result in anomalous data instances(i.e.,outliers)that do not conform with the expected pattern of regular data instances.With sparse multivariate data obtained from geotechnical site investigation,it is impossible to identify outliers with certainty due to the distortion of statistics of geotechnical parameters caused by outliers and their associated statistical uncertainty resulted from data sparsity.This paper develops a probabilistic outlier detection method for sparse multivariate data obtained from geotechnical site investigation.The proposed approach quantifies the outlying probability of each data instance based on Mahalanobis distance and determines outliers as those data instances with outlying probabilities greater than 0.5.It tackles the distortion issue of statistics estimated from the dataset with outliers by a re-sampling technique and accounts,rationally,for the statistical uncertainty by Bayesian machine learning.Moreover,the proposed approach also suggests an exclusive method to determine outlying components of each outlier.The proposed approach is illustrated and verified using simulated and real-life dataset.It showed that the proposed approach properly identifies outliers among sparse multivariate data and their corresponding outlying components in a probabilistic manner.It can significantly reduce the masking effect(i.e.,missing some actual outliers due to the distortion of statistics by the outliers and statistical uncertainty).It also found that outliers among sparse multivariate data instances affect significantly the construction of multivariate distribution of geotechnical parameters for uncertainty quantification.This emphasizes the necessity of data cleaning process(e.g.,outlier detection)for uncertainty quantification based on geoscience data.

Editorial for Advances and applications of deep learning and soft computing in geotechnical underground engineering

We are privileged to be invited by the Honorary Editor-in-Chief,Professor Qihu Qian,Editor-in-Chief,Professor Xia-Ting Feng,and the editorial staff of the Journal of Rock Mechanics and Geotechnical Engineering(JRMGE),to serve as Guest Editors for this Special Issue(SI).The purpose of this SI is to review the latest development of machine learning(ML)techniques including the soft computing(SC)and deep learning(DL)methods as well as their key applications in geotechnical underground engineering problems.

Unpacking data-centric geotechnics

The purpose of this paper(presented online as a keynote lecture at the 25th Annual Indonesian Geotechnical Conference on 10 Nov 2021)is to broadly conceptualize the agenda for data-centric geotechnics,an emerging field that attempts to prepare geotechnical engineering for digital transformation.The agenda must include(1)development of methods that make sense of all real-world data(not selective input data for a physical model),(2)offering insights of significant value to critical real-world decisions for current or future practice(not decisions for an ideal world or decisions of minor concern to geotechnical engineers),and(3)sensitivity to the physical context of geotechnics(not abstract data-driven analysis connected to geotechnics in a peripheral way,i.e.,engagement with the knowledge and experience base should be substantial).These three elements are termed“data centricity”,“fit for(and transform)practice”,and“geotechnical context”in the agenda.Given that a knowledge of the site is central to any geotechnical engineering project,datadriven site characterization(DDSC)must constitute one key application domain in data-centric geotechnics,although other infrastructure lifecycle phases such as project conceptualization,design,construction,operation,and decommission/reuse would benefit from data-informed decision support as well.One part of DDSC that addresses numerical soil data in a site investigation report and soil property databases is pursued under Project DeepGeo.In principle,the source of data can also go beyond site investigation,and the type of data can go beyond numbers,such as categorical data,text,audios,images,videos,and expert opinion.The purpose of Project DeepGeo is to produce a 3D stratigraphic map of the subsurface volume below a full-scale project site and to estimate relevant engineering properties at each spatial point based on actual site investigation data and other relevant Big Indirect Data(BID).Uncertainty quantification is necessary,as current real-world data is insufficient,incomplete,and/or not directly relevant to construct a deterministic map.The value of a deterministic map for decision support is debatable.The computational cost to do this for a 3D true scale subsurface volume must be reasonable.Ultimately,geotechnical structures need to be a part of a completely smart infrastructure that fits the circular economy and need to focus on delivering service to end-users and the community from project conceptualization to decommission/reuse with full integration to smart city and smart society.Although current geotechnical practice has been very successful in taking“calculated risk”informed by limited data,imperfect theories,prototype testing,observations,among others and exercising judicious caution and engineering judgment,there is no clear pathway forward to leverage on big data and digital technologies such as machine learning,BIM,and digital twin to meet more challenging needs such as sustainability and resilience engineering.

Editorial

This special issue of Underground Space contains extended papers related to selected contributions at the International Conference on Computational Methods in Tunneling and Subsurface Engineering(EURO:TUN 2017).EURO:TUN 2017 was organized at the University of Innsbruck,Austria.It was the fourth conference of a series of successful ECCOMAS Thematic Conferences on Computational Methods in Tunneling,which started in 2007 in Vienna and continued in 2009 and 2013 in Bochum.