Managing Risk in Geotechnical Engineering – From Data to Digitalization

Abstract

If you scan a page from a soil report, this is called digitization. If you deploy digital technologies, both software such as building information modeling and machine learning and hardware such as autonomous drones and additive manufacturing, to support new and more collaborative forms of project delivery, this is called digitalization. Data lies at the heart of this transformation that is targeted at re-valuing infrastructure from a "brick and mortar" asset to a service for the interests of the end-users. There is a need to view the value of data completely differently from how they are routinely used in current practice. In particular, there is a need to treat data as assets in themselves, over and above their conventional roles as inputs to a physical model or as monitoring data to trigger interventions. This paper explores the availability and nature of geotechnical data and presents two recent advances made in this direction for a specific but important task of estimating soil/rock properties (compressive sampling and Bayesian machine learning). Data-driven decision making does not imply taking the engineer out of the entire life cycle management chain. It is intended to support rather than to replace human judgment.

The story of statistics in geotechnical engineering

“…We now understand that there are at least seven rather than two attributes that define our data. Phoon, Ching, and Wang (2019) refer to these attributes as "MUSIC-X": Multivariate, Uncertain and Unique, Sparse, Incomplete, and potentially Corrupted with "X" denoting the spatial/temporal dimension. The "unique" and "potentially corrupted" (in the sense of data containing outliers) attributes are very hard problems.…”

“…Needless to say, they will also be imperfect in the sense of being uncertain, incomplete, and possibly even corrupted to some extent. Phoon, Ching, and Wang (2019) coined the term Big Indirect Data (BID) to refer to any data that are potentially useful but not directly applicable to the decision at hand. All engineering decisions are ultimately black and white, be it choosing the dimensions of a structure, time interval between maintenance, or issuance of an evacuation notice, notwithstanding the imperfect nature of our data, methods, and understanding of reality.…”

“…Theoretical higherorder fields do exist (Shields and Kim 2017). Phoon, Ching, and Wang (2019) pointed to several other limitations of the widely used second-order field model. While observing that this model is a closer match to reality compared to the independent and identically distributed (i.i.d.)…”

“…Given that all site data are situated in space (and sometimes, in time), the term "sparse" is more descriptive as it is unlikely for measurements to be taken at one corner of a site. Phoon, Ching, and Wang (2019) further suggested that MUSIC can be re-interpreted to cover extremes: Multivariate, Uncertain and Unique, Sparse, Incomplete, and potentially Corrupted. Ching and Phoon (2019b) subsequently extended MUSIC to MUSIC-X, where "X" denotes the spatial/temporal context of the data.…”

“…More recent multiple point methods in geostatistics can consider more than two-point autocorrelation (or second-order) information (Mariethoz and Caers 2015), but they require richer data in the form of training images. Phoon, Ching, and Wang (2019) opined that a "taxonomy of methods based on the type/amount of data available could help guide future development in data-driven algorithms and strengthen a virtuous cycle of data collection hardware developing hand in hand with algorithms". Wang and Zhao (2016, 2017) explored a Figure 3.…”