Abstract

A sound understanding of subsurface geological conditions is crucial for the digitalisation of underground infrastructure. The building and updating of underground digital twins heavily rely on sparse geotechnical measurements (e.g., boreholes) retrieved from the ground, and an efficient sampling strategy can facilitate the interpretation of subsurface heterogeneities. Geotechnical sampling design can be viewed as a constrained optimization process that aims to obtain as much geological information as possible from a limited number of sampling locations within a given site boundary. In this study, a data-driven intelligent sampling strategy is proposed to optimize borehole locations for a multi-stage site investigation of a three-dimensional (3D) geological domain. The initial sampling plan is determined using weighted centroidal Voronoi tessellation, which assigns uniform sampling densities to zones of different importance. Measurements obtained from the initial stage are combined with prior geological knowledge to build underground digital twins using an image-based stochastic modelling method. Multiple realizations of the geological domain can be developed under the framework of Monte Carlo simulation, and stratigraphic uncertainties associated with multiple random realizations can be quantified using information entropy. The location with the maximum entropy is adaptively selected as the next optimal sampling location. The proposed method is the first sampling strategy that can explicitly consider 3D subsurface stratigraphic variations. The performance of the proposed multi-stage sampling strategy is demonstrated using a simulation example. Results indicate that the proposed method can efficiently identify the optimal sampling locations while accounting for irregular site geometries and 3D subsurface stratigraphic uncertainties.

Abstract

A sound understanding of subsurface geological conditions is crucial for the digitalisation of underground infrastructure. The building and updating of underground digital twins heavily rely on sparse geotechnical measurements (e.g., boreholes) retrieved from the ground, and an efficient sampling strategy can facilitate the interpretation of subsurface heterogeneities. Geotechnical sampling design can be viewed as a constrained optimization process that aims to obtain as much geological information as possible from a limited number of sampling locations within a given site boundary. In this study, a data-driven intelligent sampling strategy is proposed to optimize borehole locations for a multi-stage site investigation of a three-dimensional (3D) geological domain. The initial sampling plan is determined using weighted centroidal Voronoi tessellation, which assigns uniform sampling densities to zones of different importance. Measurements obtained from the initial stage are combined with prior geological knowledge to build underground digital twins using an image-based stochastic modelling method. Multiple realizations of the geological domain can be developed under the framework of Monte Carlo simulation, and stratigraphic uncertainties associated with multiple random realizations can be quantified using information entropy. The location with the maximum entropy is adaptively selected as the next optimal sampling location. The proposed method is the first sampling strategy that can explicitly consider 3D subsurface stratigraphic variations. The performance of the proposed multi-stage sampling strategy is demonstrated using a simulation example. Results indicate that the proposed method can efficiently identify the optimal sampling locations while accounting for irregular site geometries and 3D subsurface stratigraphic uncertainties.

Abstract

Quantifying uncertainties in subsurface properties and stratigraphy can lead to better understanding of the ground conditions and enhance the design and assessment of geotechnical structures. Several studies have utilized Cone Penetration Test (CPT) data and employed Bayesian and Machine Learning methods to quantify the geological uncertainty, based on the Robertson’s soil classification charts and the Soil Behaviour Type Index (Ic). The incorporation of borehole data can reduce the stratigraphic uncertainty. Significant challenges can arise, however, mainly due to the intrinsic differences between field and laboratory-based soil classification systems, which can potentially lead to inconsistent soil classification. To this end, this study proposes a multivariate Gaussian Process model that utilizes site-specific data and: i) jointly models multiple categorical (USCS labels) and continuous (Ic) variables, ii) learns a (shared) spatial correlation structure and the betweenoutputs covariance, and iii) produces two types of dependent classification outputs. The results indicate that the integration of geotechnical and geological information into a unified model can provide more reliable predictions of the subsurface stratification, by allowing simultaneous interpretation of USCS and Ic profiles. Importantly, the model demonstrates the potential to integrate multiple variables of different types, aiming to contribute to the development of a methodology for joint modeling of geotechnical, geological and geophysical data.