Geotechnical Site Research Articles

Similarity characterization of geotechnical engineering sites under the site-specific data scenario

Similarity characterization of geotechnical engineering sites under the site-specific data scenario

Geotechnical aspects and site investigations on Kahramanmaras earthquakes, February 06, 2023

Geotechnical aspects and site investigations on Kahramanmaras earthquakes, February 06, 2023

Derivation of site-specific soil-water characteristic curve (SWCC) from extremely sparse experimental data by hierarchical Bayesian method with consideration of geotechnical sites similarity

Soil-water characteristic curves (SWCCs) play a crucial role in understanding soil behavior related to water movement and soil moisture effects, rendering them an essential tool in engineering geology and geotechnical engineering applications. Traditionally, SWCCs are determined through labor-intensive laboratory experiments involving varying levels of suction, a process that can take several months. Moreover, obtaining a high-quality SWCC from numerous measurements becomes particularly challenging when immediate site-specific data are required for design and analysis. This paper introduces a hierarchical Bayesian method for deriving site-specific SWCCs by integrating extremely sparse data (e.g., one or two measurements) for the site of interest with existing data from sites with similar geological and sedimentary characteristics. The SWCC parameters are estimated using a Bayesian framework and Markov chain Monte Carlo simulations. This approach not only enables the derivation of accurate SWCCs but also helps quantify the associated uncertainties. The effectiveness of the proposed method is demonstrated using both numerical and real-world data from different types of loess and unsaturated soils in the unsaturated soil database (UNSODA). The results show that site-specific SWCCs of unsaturated soils can be accurately estimated from sparse measurements by incorporating information from similar sites. This work offers an efficient and reasonably accurate approach for deriving SWCCs of unsaturated soils for geotechnical applications, especially when the number of site-specific SWCC measurement is extremely sparse and limited.

Improving the interpretation of undrained shear strength from piezocone penetration tests by integrating soil physical properties using a hybrid meta-heuristic algorithm

Conventional empirical equations for estimating undrained shear strength (su) from piezocone penetration test (CPTu) data, without incorporating soil physical properties, often lack the accuracy and robustness required for geotechnical site investigations. This study introduces a hybrid virus colony search (VCS) algorithm that integrates the standard VCS algorithm with a mutation-based search mechanism to develop high-performance XGBoost learning models to address this limitation. A dataset of 372 seismic CPTu and corresponding soil physical properties data from 26 geotechnical projects in Jiangsu Province, China, was collected for model development. Comparative evaluations demonstrate that the proposed hybrid VCS-XGBoost model exhibits superior performance compared to standard meta-heuristic algorithm-based XGBoost models. The results highlight that the consideration of soil physical properties significantly improves the predictive accuracy of su, emphasizing the importance of considering additional soil information beyond CPTu data for accurate su estimation.

Clarifying baseline seismicity for a geotechnical survey site at Gross mining and processing plant

Clarifying baseline seismicity for a geotechnical survey site at Gross mining and processing plant

Seismic site characterization baseline data for microzonation and site response analysis of Otuasega Town, Bayelsa State, Niger Delta region of Nigeria

This study presents the findings of a comprehensive geotechnical and seismic site investigation conducted at Otuasega Town located in Bayelsa State within the Niger Delta region of Nigeria. Subsurface exploration involved advancing 10 boreholes to 30 m depth using hollow stem auger drilling. Continuous disturbed and undisturbed soil sampling was performed at 1.5 m intervals for detailed geotechnical testing. Laboratory tests on the recovered soil samples established the index properties, classification, densities and consistency limits of the stratified deposits. The subsurface profile comprised alternating layers of clay, silt and sand typical of deltaic sediments, with the clay fractions exhibiting medium to high plasticity. Shear wave velocity (Vs) profiling using Multichannel Analysis of Surface Waves (WASW) techniques categorised the site predominantly as Site Class C and D based on international standards. The Standard Penetration Test (SPT) N-values ranged from 5 to 10, indicating soft normally consolidated clay conditions typical of the Niger Delta region. Predictive empirical models developed from the field and lab data showed strong correlations for estimating key geotechnical parameters such as SPT blow count, Vs and liquefaction resistance. Ground response analyses using the Vs and SPT data indicated significant site amplification potential, with peak ground accelerations up to 1.5 times the bedrock motion. Liquefaction analysis based on the empirical SPT-based methods revealed a high potential for liquefaction in the sandy layers, especially under strong earthquake shaking. The study characterized the complex sedimentology and provided baseline information for seismic microzonation and site-specific ground response analyses to advance understanding of geohazards in this delta environment.

Machine Learning Methods for Geotechnical Site Characterization and Scour Assessment

Machine Learning Methods for Geotechnical Site Characterization and Scour Assessment

Geotechnical Site Characterizations Using a Bayesian-Optimized Multi-Output Gaussian Process

Geotechnical site characterizations aim to determine site-specific subsurface profiles and provide a comprehensive understanding of associated soil properties, which are important for geotechnical engineering design. Traditional methods often neglect the inherent cross-correlations among different soil properties, leading to high bias in site characterization interpretations. This paper introduces a novel data-driven site characterization (DDSC) method that employs the Bayesian-optimized multi-output Gaussian process (BO-MOGP) to capture both the spatial correlations across different site locations and the cross-correlations among various soil properties. By considering the dual-correlation feature, the proposed BO-MOGP method enhances the accuracy of predictions of soil properties by leveraging information as much as possible across multiple soil properties. The superiority of the proposed method is demonstrated through a simulated example and the case study of a Taipei construction site. These examples illustrate that the proposed BO-MOGP method outperforms traditional methods that fail to consider both types of correlations, as evidenced by the reduced prediction uncertainty and the accurate identification of cross-correlations. Furthermore, the ability of the proposed BO-MOGP method to generate conditional random fields supports its effectiveness in geotechnical site characterizations.

A Bayesian Vine Algorithm for Geotechnical Site Characterization Using High Dimensional, Multivariate, Limited, and Missing Data

A Bayesian Vine Algorithm for Geotechnical Site Characterization Using High Dimensional, Multivariate, Limited, and Missing Data

Three-dimensional reconstruction of subsurface stratigraphy using machine learning with neighborhood aggregation

In engineering geology and geotechnical engineering, it is well recognized that subsurface soils/rocks are natural materials and exhibit variability in stratigraphy due to the complex geological formation processes they have undergone. Knowledge of subsurface soil stratigraphy is of great importance to geotechnical engineers. However, accurate and reliable interpretation of subsurface soil stratigraphy is challenging due to the limited number of site investigation boreholes available at the site and the highly heterogeneous properties of soil stratigraphy (e.g., interbedded or non-ordered layers). This paper proposes an improved data-driven machine learning framework boosted with the neighborhood aggregation technique for modelling three-dimensional (3D) subsurface soil stratigraphy in a more general and robust manner. Neighborhood aggregation, a technique often adopted in graph network learning, is integrated into this framework to regulate and improve the prediction results of classical machine learning models. The proposed framework is then cross-validated using 165 real site investigation boreholes for four selected machine learning models respectively. Cross-validation results suggest that the eXtreme Gradient Boosting (XGBoost) and Random Forest (RF) models are more suitable for the task of soil stratigraphy prediction than Artificial Neural Network (ANN) and Support Vector Machine (SVM). Particularly, the XGBoost and RF are also amenable to neighborhood aggregation and can yield around 5% improvement in terms of average borehole prediction accuracy after introducing neighborhood aggregation. The improved machine learning framework allows for explicit 1D to 3D geological modelling, uncertainty quantification, and convenient visualization. The proposed framework facilitates digital transformation of geological and geotechnical site investigation.

Uncertainty reduction strategies to enhance geotechnical site characterization: a case study of the red roof landslide in Wyoming

This study presents a method for identifying strategic locations to drill additional boreholes by quantifying and reducing subsurface uncertainties in geotechnical site investigations. The case study is the Red Roof landslide site located near milepost 140 on US Highway 26/89 in Teton County, Wyoming. A landslide remediation report had recommended additional boreholes before completion of the project. Two primary sources of uncertainty in geomaterials, namely geological and ground, are evaluated to determine the locations for these additional boreholes. The study aims to enhance site characterization and improve the accuracy of geotechnical assessments by strategically selecting and drilling additional boreholes that will reduce these subsurface uncertainties. The method involves reviewing and collecting the available site investigation data. An extensive geostatistical simulation is conducted based on the available data to identify spatial locations at the site that have uncertain subsurface conditions. Data from the additional boreholes could be utilized in these areas to reduce the uncertainty. This approach aligns with the current multi-phasing of site investigation in engineering practice, where a preliminary investigation is conducted before a detailed investigation. The results show that the locations of additional boreholes identified using this method differ from the ones earlier recommended, showcasing the effectiveness of the methodology in this application. This study also provides insights into effective strategies for reducing uncertainties through strategic borehole placement in similar geotechnical investigations. By quantifying geological and ground uncertainties, the method enables informed decision-making for slope stability analysis and risk assessment, with implications for infrastructure stability and geohazard mitigation.

Effect of sampling locations on reliability of pile groups

Geotechnical site investigations represent an imperative prerequisite in the pursuit of reliable foundation designs. However, site investigations are frequently restricted to a limited number of locations owing to the constraints imposed by budget and time considerations, thereby potentially yielding a range of adverse consequences. Hence, the development of an efficient site investigation plan — one that optimally selects the number and location of tests, is important to gain adequate information for a given cost. This paper proposes a framework to identify optimal investigation locations by minimizing the probability of erroneous decisions (i.e., error types I and II). A pile group was adopted for the demonstration. The best locations corresponding to various numbers of tests were identified based on the derived probabilities of type I and II errors.

Limitations in Tunnel Portal Design: An Evaluation Using Numerical Models and Line Surveys

In this study, engineering geology and geotechnical design studies of a highway tunnel project, located on the route of Zonguldak-Amasra-Kurucasile, was expressed. Owing to the low overburden thickness the subjected slope tunnel, especially the right tube in accordance with the project kilometer incremental direction, will be exposed to asymmetrical load after commence of the excavation. Besides, thicknesses between right tube right wall and the slope face in this section has decreased up to 6 m level. Moreover, as the tunnel is passing under the 1st grade archeological protection area, some of the site investigation studies, such as geotechnical drilling and site laboratory works could not have been performed. Thus, this study is explaining the determination of excavation support system of slope tunnel by using engineering geology studies and numerical models. As the tunnel site is located under the 1st grade archeological protection area, any of the required geotechnical site investigation drilling studies and corresponding site testing was not performed on the tunnel axis. Instead of this, line surveys, local sampling, and internationally accepted rock mass classification studies (RMR, Q, GSI) were performed on the rock mass outcrops, which are positioned against to the Black Sea. At the conclusion of these studies, rock mass engineering properties were determined through the utilization of empirical equations that incorporate data derived from site investigation studies and laboratory test results as input.

Interpolation of non-stationary geo-data using Kriging with sparse representation of covariance function

Interpolation of spatially varying measurements, e.g., spatial variation of a geotechnical property along depths, is often needed in many disciplines, including geotechnical engineering. Among various interpolation methods, geostatistical methods, such as Kriging, are popular since they are able to not only provide the best estimates but also quantify the interpolated uncertainty. However, Kriging relies on an assumption of data stationarity, and a transformation from non-stationary data to stationary data by, e.g., detrending, is required when using Kriging to interpolate non-stationary data. Detrending, however, is a tricky and long-lasting challenge in geo-data analyses. To tackle this challenge, this study develops a novel Kriging method for direct interpolation of spatially non-stationary data without detrending. A sparse representation of covariance function is proposed for Kriging interpolation of spatially non-stationary data, bypassing the tricky detrending process. The proposed Kriging method is particularly appealing for non-stationary geotechnical site investigation data often encountered in geotechnical practice (e.g., spatial variation of soil properties over different soil layers along depth). Equations are derived for the proposed Kriging method, and it is illustrated and validated using numerical examples. Results show that the proposed Kriging method with sparse representation of covariance function is directly applicable to spatially non-stationary data and has the advantages of improved accuracy and reduced interpolation uncertainty when interpolating spatially varying, non-stationary data.

Feature analysis of precipitation-induced subgrade defects on a high-speed rail ballasted track using multiple track inspection data: A case study

Heavy rainfall has posed a great challenge to the service performance of high-speed rail (HSR) substructure, resulting in a reduction in the ride quality and safety of high-speed trains. To carry out proper repair work for the substructure, it is imperative to realize efficient identification of precipitation-induced subgrade defects. To this end, this paper aims to extract the features of typical precipitation-induced subgrade defects from the multiple track inspection data to provide a basis for defect identification. Firstly, the geotechnical site investigation including Ground Penetrating Radar (GPR) detection, moisture content test, and dynamic cone penetration (DCP) test of a typical defective spot is performed to determine the condition of the subgrade after heavy rainfall; then, the analysis methods of track inspection data are introduced; finally, the track geometry data and carbody acceleration data of four typical defective sections are analyzed, and the time-domain, frequency-domain and discrete wavelet transform (DWT)-based features which are highly correlated with the precipitation-induced subgrade defects are extracted. The results show that the feature indexes extracted from track surface irregularity and carbody vertical acceleration increase significantly after heavy rainfall; the long wavelength components (8 m and above) of both track irregularity and carbody vibration are more sensitive to the subgrade defects, which is reflected by the sharp increase of the DWT-based features at some levels corresponding to long wavelength ranges. The results of defect feature extraction based on the track inspection data agree well with the geotechnical site investigation results, which demonstrate the feasibility of utilizing multiple track inspection data to identify the typical precipitation-induced subgrade defects.

Safety evaluation in the geotechnical site applications: A comprehensive analysis and forecasting model

Geotechnical site works play a crucial role in the construction projects. In this study, data mining was used to evaluate the accidents occurred during geotechnical site works. OSHA Integrated Management Information System was used to collect 1010 cases in a time frame from 1984 to 2022. These cases were analyzed to evaluate the root causes of the accidents. Input variables of this study were the soil type and condition, project type and cost, end use of the work, cause and type of accident, type and degree of injury, unsafe acts, and occupation / union status of the victim. The evaluation showed that accidents not only had a high recurrence frequency but also a high severity level (57.2% fatalities). Moreover, an artificial estimating model based on a Multi-Layer Artificial Neural Network (MLANN) architecture was developed using MATLAB® platform in this study. In this regard, metaheuristic technique so-called Differential Evolution incorporated Virtual Mutant (FDEVM) was employed to optimize the weighted coefficients of the system. The FDEVM method is introduced recently and FDEVM in combination with MLANN is used for the first time in this study. As the model was trained for discrete set of data, the F1-Score test was performed to evaluate the performance of the models and based on the attained outcomes, it can predict the tasks with an accuracy of 78%. The root causes of accidents showed that project-specific countermeasures should be of high priority along with the implementation of vigorous strategies to develop safety measures.

Exploring Shear Wave Velocity—NSPT Correlations for Geotechnical Site Characterization: A Review

Shear wave velocity (Vs) is a critical parameter in geophysical investigations, micro-zonation research, and site classification. In instances where conducting direct tests at specific locations is challenging due to equipment unavailability, limited space, or initial instrumentation costs, it becomes essential to estimate Vs directly, using empirical correlations for effective site characterization. The present review paper explores the correlations of Vs with the standard penetration test (SPT) for geotechnical site characterization. Vs, a critical parameter in geotechnical and seismic engineering, is integral to a wide range of projects, including foundation design and seismic hazard assessment. The current paper provides a detailed analysis of the key findings, implications for geotechnical engineering practice, and future research needs in this area. It emphasizes the importance of site-specific calibration, the impact of geological background, depth-dependent behavior, data quality control, and the integration of Vs data with other geophysical methods. The review underlines the continuous monitoring of Vs values due to potential changes over time. Addressing these insights and gaps in research contributes to the accuracy and safety of geotechnical projects, particularly in seismic-prone regions.

Numerical simulation of soil structure damage upon sampling

Sampling disturbance is a perennial problem of geotechnical site investigation. The structure of soft soils is particularly fragile and prone to disturbance during sampling. The extent by which different sampling procedures modify soil structure has been generally assessed on an empirical basis, observing the outcomes of tests performed on specimens obtained with different sampling technologies. That kind of experiment is slow, costly and laborious. In this communication we present a procedure to perform similar experiments “in silico”, by simulating the sampling procedure, examining the effect of tube sampling on the initial soil structure, obtaining sub specimens from the simulated core and testing them afterwards in simulated triaxial undrained compression and in the oedometer. The simulations are based on G-PFEM, a code developed for soil-structure interaction at large strains. The numerical experiments clarify the origin of sampling related property variability.

Assigning resistivity values to rock quality designation indices using integrated unmanned aerial vehicle and 2D electrical resistivity tomography in granitic rock

Rock quality designation (RQD) is a standard technique in mining and geotechnical investigation for quantifying the quality and degree of jointing of the rock mass. However, the need for expeditious and inexpensive geotechnical site characterization is the prime motivation for modifying RQD. This research work integrates 2D Electrical Resistivity Tomography (2D ERT) and Unmanned Aerial Vehicle (UAV) conducted at Site 1 and Site 2. The UAV survey was performed to calculate RQD on the rock surface using volumetric Joint Counts (Jv) at Sites 1 and 2. A 2D ERT survey was conducted to obtain the corresponding subsurface resistivity values. Using k-nearest neighbours (k-NN), resistivity values were assigned to various RQD indices such as very poor rocks (<350 Ωm), poor rock (350–1150 Ωm), fair rock (1150–1850 Ωm), good rock (1850–2500 Ωm) and >2500 Ωm excellent rock. Based on the established correlation of RQD and resistivity, the subsurface rock mass quality at Site 1 was predominantly good and excellent rock, while at Site 2, poor rock was significantly reported. This research concluded that rock mass characterization using RQD is more rapid and inexpensive than the direct or indirect calculation of RQD. The combined 2D ERT and UAV deployed in this research study provide an environmentally non-destructive approach for slope stability assessment.

A fused sampling method integrating geotechnical and geophysical data for assessing three-dimensional soil liquefaction-induced damage capacity

Combining intrusive geotechnical site investigations with non-intrusive geophysical surveys is a cost-effective approach to producing data with varying levels of accuracy, uncertainty, and different spatial scales to better characterize the site's liquefaction properties. Moreover, the demand for three-dimensional (3D) subsurface models in geotechnical engineering is increasing, but the models contain uncertainties and spatial variability associated with the use of relevant stochastic and geostatistical methods, and it remains a challenging task to obtain reliable liquefaction assessment results and the corresponding damage capacity. This study proposes a data-driven and non-parametric form of 3D multi-source fusion Bayesian compressive sampling (3D MSF-BCS) method for assessing 3D soil liquefaction-induced damage capacity. It consists of three main components: (i) 3D MSF-BCS fusing sparse geotechnical data (e.g., cone penetration test (CPT)) and geophysical data (e.g., multi-channel analysis of surface waves (MASW)) for 3D site modeling, (ii) quantifying the accuracy and uncertainty of 3D MSF-BCS, and (iii) Soil liquefaction-induced damage capacity analysis in 3D space. The method was applied to numerical examples and a real case study at the Cresselly Place site, and the results showed that the proposed method performs well.