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In order to achieve higher fidelity (i.e., higher accuracy) in the model predictions, the solution models employed in geological and/or geotechnical engineering are becoming complex and sophisticated. However, simple and robust models are preferred by engineers in practice. Thus, a dilemma exists between the choice of a complex model for fidelity and that of a simple model for high robustness (i.e., lower variation in the discrepancy between model prediction and observation). This issue becomes more profound when model parameters exhibit uncertainty, which is quite common in geological and geotechnical problems. In this paper, we examine the issue of model selection in the face of uncertainty with three problems: the selection of the order of polynomial fit (i.e., lower order vs. higher order) in the development of data-driven empirical model, the selection of the level of sophistication (i.e., random variable vs. random field) in the probabilistic characterization of the detrended soil property, and the selection of the level of complexity (i.e., simple vs. complex) of the soil constitutive model in numerical modelling. The results illustrate that although the complex and sophisticated models could yield predictions that are more accurate, the simple models might yield predictions that are more robust. This paper provides an insight regarding the question, “Does a complex model always outperform a simple model?”

Due to the inherent spatial variation of the site information and the limited number of boreholes which could be afforded in a specified project, the geotechnical and geological information at a site could not be fully characterized, which imposes a significant challenge to the analysis, design and construction of the geotechnical system of concern. Indeed, the issue of site characterization has long puzzled engineers. This paper presents a three-dimensional site characterization in the urban area of Suzhou where plenty of borehole explorations have been undertaken. With the borehole database established, the relationship between the shear-wave velocity and the depth is analyzed, this relationship can be captured by regression-based models; and then, a sequence extrapolation method that is based upon the linear relationship between the time average shear-wave velocities is developed for the prediction of the shear-wave velocity along the depth. Next, a three-dimensional characterization and visualization of the stratigraphic model in the urban area of Suzhou are presented. The characterization of the stratigraphic model, in this paper, is realized through the characterization of the elevations of the interfaces between adjacent strata in the sequence stratigraphic structure. The sequence stratigraphic structure is derived with a method that is based upon the one-by-one comparison and updating of the borehole data; and then, the best unbiased estimator of the elevations of the interfaces between adjacent strata at the concerned site is predicted with the Kriging-based interpolation method. With the aid of ArcGIS Engine, a three-dimensional visualization of the characterized stratigraphic model is made possible.

Site characterization, which aims to characterize the subsurface stratigraphic configuration and the associated geo-properties, has long been a significant challenge in geological and geotechnical practice. Due to the complexity and inherent spatial variability of the geological bodies and the limited availability of borehole data, uncertainty is unavoidable in the characterized subsurface stratigraphic configuration and the associated geo-properties. In previous studies, the stratigraphic uncertainty and the geo-properties uncertainty were characterized separately. This paper proposes a conditional random field approach for a coupled characterization of stratigraphic and geo-properties uncertainties. The spatial correlation of the stratum existence between different subsurface elements and the spatial correlation of geo-properties are characterized by two autocorrelation functions, determined with the maximum likelihood principle. With the knowledge of the spatial correlation of the stratum existence, the stratigraphic configuration can be sampled using a modified random field approach. Then, the spatial correlation of the geo-properties is updated based on the sampled stratigraphic configuration. With the updated spatial correlation of the geo-properties, the spatial distribution of the geo-properties can readily be simulated with the conditional random field theory. The effectiveness of the proposed approach is demonstrated through a case study of probabilistic site characterization of an offshore wind farm site in Taiwan. To extend the applicability of the proposed approach, a probabilistic evaluation of liquefaction potential at this site under a given seismic shaking level is performed.

Unmanned Aerial Vehicle (UAV) technique has been widely utilized in geohazards assessment. In this work, the effectiveness of the UAV photogrammetry in the remote sensing and assessment of the landslide behavior is demonstrated through a case study of a landslide that occurred in Guizhou, China on 10 June 2018. The post-landslide assessments were conducted through a field investigation by a team of experts; and, two UAV photogrammetry-based surveys were carried out. On the basis of a detailed inspection of the high-resolution aerial photographs collected from the UAV photogrammetry and historical satellite imagery, subsurface stratigraphic configuration revealed from borehole explorations and the local rainfall data collected, the failure mechanism of this landslide is investigated. The occurrence of this landslide is probably attributed to the combined influence of the long-term rainfall and the engineering activities at this site. An automatic landslide cracks recognition model, which is based on the deep learning-based image recognition technique of RetinaNet, is further developed to map the cracks at the landslide site. The effectiveness of this automatic crack recognition model is validated through quantitative comparisons between the landslide cracks recognized and the field survey results. Based upon the landslide cracks identified on two different dates after this landslide event (by the field survey and automatic crack recognition model) and the subsurface displacement revealed from a drilled hole, the evolution behavior of this landslide is analyzed. The results show that the stability of this landslide was not achieved during the first slide in June 2018 and the limit of the landslide was increased much from June to November 2018. In such a situation, the elements at risk in the zones that are potentially impacted by this landslide are identified (with the aid of the UAV images collected), and a preliminary consequence assessment is conducted.

Accurate and precise characterization of the subsurface stratigraphic configuration (geological model) at a given site is crucial to geotechnical engineering work. The uncertainty in the derived stratigraphic configuration can be significant, due to the strata's complexity and inherent spatial variability coupled with the limited availability of borehole data. The characterization and reduction of this uncertainty should be part of any site characterization project. This paper presents a method for characterization of the subsurface stratigraphic configuration with limited borehole data. Within the framework of the proposed method, the spatial correlation between the existence of a stratum in one subsurface zone and that in the other subsurface zone (or the spatial correlation of the existence of the stratum) is captured by an autocorrelation function determined with the maximum likelihood principle. The initial stratigraphic configurations are first sampled with the conditional random field theory. Next, the maximum-a-posteriori (MAP) estimates of the initial stratigraphic configurations are derived using Markov Chain Monte Carlo (MCMC) and taken as the final stratigraphic realizations. The effectiveness of the proposed method and its advantages over the existing stratigraphic characterization methods are demonstrated through a series of comparative analyses. The versatility of the new approach in modeling the 3-D stratigraphic configuration is further revealed through a case study of a site in Western Australia. This paper adds to the literature on stratigraphic uncertainty characterization and provides a basis for a risk-based geotechnical assessment that considers geological and geotechnical uncertainties.

This paper presents a probabilistic approach for seismic stability analysis of a slope at a given site in a specified exposure time. For a probabilistic seismic stability analysis, the ground motion parameter, in terms of the peak ground acceleration (PGA), at a given site in a specified exposure time of interest (say, 30 years) is treated as a random variable, and the PGA distribution at the given site is derived based on the USGS National Seismic Hazard Maps data. Further, the spatial variability of the soil property is simulated herein by a random field, and the fluctuation of the groundwater level is simulated by a random variable. Within the probabilistic framework, a deterministic model for evaluating the slope stability is required; here, a pseudo-static analysis is adopted and implemented through 2D finite difference program FLAC version 7.0. In the face of the uncertainties in the input parameters, the performance or safety of the slope is expressed as a failure probability; within the proposed probabilistic analysis framework, a recently developed sampling method is adopted for the uncertainties propagation through the deterministic solution model. This probabilistic analysis framework is demonstrated with an illustrative example of a two-layer earth slope. Finally, a parametric study is undertaken to investigate how the failure probability of the slope (at a given site in a specified exposure time) is affected by the uncertain factors such as the earthquake-induced ground motion and the spatial variability of soil property. The study results demonstrate the versatility and effectiveness of the proposed framework for probabilistic seismic stability analysis of slope at a given site in a specified exposure time.

Energy piles provide clean energy in lieu of non-renewable energy resources such as petroleum and coal. The current geotechnical design of energy piles is merely realized through enlarging the factor of safety. The factor of safety used for energy piles is at least twice that used for traditional piles, which can lead to considerable additional costs and inconsistent levels of safety. Although the performance of existing energy pile technology is satisfactory and recent investigators have significantly advanced the understanding of the load-transfer mechanisms of energy piles, it is still necessary to quantify the uncertainty in the thermal–mechanical analysis and to develop reliability-based designs for energy piles. This paper presents a pioneering probabilistic study on the geotechnical design of energy piles. The critical soil parameters for ultimate limit state (ULS) and serviceability limit state (SLS) are identified through sensitivity study. The point estimate method is adopted to understand the uncertainty propagation and to estimate the probability of ULS failure and the probability of SLS failure. The adverse effects of thermal loading in the geotechnical design of energy piles are explicitly demonstrated through a case study.

Most data-driven empirical models adopted in the geotechnical design have various degrees of uncertainty. Consequently, it is important to properly calibrate this uncertainty prior to its application in the geotechnical analysis to ensure the integrity of the final design. The conventional probabilistic model calibration approaches, such as maximum likelihood estimate and Bayesian method, focus only on model fidelity (measured through, for example, likelihood). These approaches often yield models with a large model uncertainty, especially when the observed data are scattered. A large model uncertainty leads to a large variation in the model prediction, which poses a significant challenge in a geotechnical design. In this paper, we propose a new model calibration approach that can consider both model fidelity and model robustness. The new model calibration approach offers a way to balance the objectives of model fidelity and model robustness. Because model fidelity and model robustness are two conflicting objectives, the new approach involves a multi-objective optimization that leads to a Pareto front, which defines a tradeoff relationship between model fidelity and model robustness. By enforcing a certain level of robustness, the variation in the model prediction can be reduced, which overcomes the major weakness of the traditional probabilistic approaches that focus solely on model fidelity. The new approach is demonstrated through applications to the problems of liquefaction-induced settlement prediction.

In the article “Engineering geology-A fifty year perspective”(Juang et al., 2016), which celebrated the 50th anniversary of Engineering Geology, the authors described Engineering Geology for Engineering Projects (EGEP) together with Geological Engineering and Geotechnical Engineering (GEGE) as important elements of Engineering Geology. For example, four-hundred and twelve papers were published in Engineering Geology from 1986–1995. Among which, one-hundred and twenty (about 30%) were related to EGEP. The research topics mainly included design and construction of foundations, tunnels, and slopes (eg dams, embankments, landslides). However, the papers published on EGEP and GEGE declined significantly from 2006 to 2015. It was speculated that the worldwide economic recession that began in 2008 might have contributed to the decline of infrastructure development and the number of …

The construction of the huge Three Gorges reservoir affected a large region, and the resultant geological and environmental impacts have caused global concern. The remarkable, 30-m annual fluctuation in the reservoir water level poses a significant threat to slope stability in this area. Four hundred sixty-two landslides were identified in the Zigui basin using historical records, satellite images, field investigations and unmanned aerial vehicle (UAV) observations, enabling the construction of a complete landslide database and distribution map. Three failure modes of landslides in the Zigui basin are used to illustrate the major factors that govern reservoir-induced landslides. The results show that (1) > 99% of identified landslides occur on slopes angle 80% occur at elevations below 600 m; (2) Jurassic Niejiashan Formation is highly prone to landslides; (3) Low reservoir levels of 145 m to 155 m greatly reduce slope stability. Based on the information entropy method that use the conditional probability of different influencing factors and principal component analysis as inputs, the relative contributions of various influencing factors are quantified and a landslide susceptibility map was drawn. This susceptibility map helps define the countermeasures that will best reduce fatalities and property losses for areas having different landslide conditions and susceptibilities.

Strong earthquake events often result in liquefaction-induced ground movements such as settlements and lateral spreads that are a major cause of damage to buildings, bridges, and lifelines. This paper focuses on the subject of liquefaction-induced lateral spreads. Case histories with detailed cone penetration test (CPT) data are derived from the 2010–2011 Canterbury, New Zealand earthquake sequence and used to examine an existing empirical model for liquefaction-induced lateral spreads. The results confirmed the existence of a substantial discrepancy between the predicted and observed lateral spreads, indicating a need for improved models that consider uncertainties in the input parameters. Using a maximum likelihood analysis of the derived case histories, a new empirical probabilistic model was developed for the estimation of lateral spreads in the Christchurch, New Zealand area. Emphasis is placed on assessing the probability of exceedance of limiting displacements for design against the threat of lateral spreads. The new CPT-based probabilistic model with detailed formulations is presented along with an illustrative example. The limitations of the model are discussed.

The uncertainty involved in the interpreted geological model may be categorized as the stratigraphic uncertainty and the properties uncertainty. Note that although the influence of the properties uncertainty on the behavior or performance of the geotechnical system and the geotechnical design has been extensively reported in the literature, the studies that address the stratigraphic uncertainty are limited. This paper presents a study regarding the influence of the stratigraphic uncertainty on the behavior of the geotechnical system and the geotechnical design. In which, the uncertainty in the stratigraphic configuration is characterized using the stochastic Markov random field-based approach with a large number of potential stratigraphic realizations. With these stratigraphic realizations as inputs, the influence of the stratigraphic uncertainty on the behavior of the geotechnical system is evaluated in a probabilistic manner; then, the design of the geotechnical system is formulated as a bi-objective optimization-based problem that considers the design safety and the cost simultaneously. To demonstrate the effectiveness of the proposed probabilistic analysis and design approach, the problem of designing stabilizing piles in a slope consisting of multiple strata is studied. The parametric study is further conducted to analyze how the probabilistic analysis results are influenced by the pile parameters and how the probabilistic design results are influenced by the additional boreholes.