Geotechnical Site Investigation Research Articles

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.

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.

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.

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.

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.

Fusion of sparse non-co-located measurements from multiple sources for geotechnical site investigation

A profile of geotechnical properties is often needed for geotechnical design and analysis. However, site-specific data might be characterized as MUSIC-X (i.e., Multivariate, Uncertain and Unique, Sparse, Incomplete, and potentially Corrupted with “X” denoting the spatial/temporal variability), posing a significant challenge in accurately interpreting geotechnical property profiles. Different sources, or types, of data are commonly available from a specific site investigation program, and they are usually cross-correlated, and thus can provide complementary information. This leads to an important question in geotechnical site investigation: how to integrate multiple sources of sparse data for enhancing the profiling of different geotechnical properties. To address this issue, this study proposes a novel method, called fusion Bayesian compressive sampling (Fusion-BCS), for integrating sparse and non-co-located geotechnical data. In the proposed method, the auto- and cross-correlation structures of different sources of data are exploited in a data-driven manner through a joint sparse representation. Then, profiles of different geotechnical properties are jointly reconstructed from all measurements under a framework of compressive sampling/sensing. The proposed method is illustrated using simulated and real geotechnical data. The results indicate that the accuracy of the interpreted geotechnical property profiles may be significantly improved by integrating multiple sources of site investigation data.

Monopile Foundation Stiffness Estimation of an Instrumented Offshore Wind Turbine through Model Updating

Rapid development of offshore wind foundation models has resulted in a large number of built structures with generally underestimated foundation stiffness properties and a need to update and validate both the individual structural models and the underlying foundation design frameworks. This paper outlines a structural health monitoring approach, based on the combination of output only structural health monitoring methods and model updating, to estimate foundation stiffness parameters using field monitored data. Field monitoring data from an offshore wind turbine under idling conditions, over a large monitoring period, are presented and operational modal analysis is applied to estimate the modal parameters. Those are compared to modal properties predicted by finite element models, employing either old (API/DNVGL) or new (PISA) foundation design properties, which are calibrated using geotechnical site investigation data. A new approach to interpret seabed level statically equivalent foundation stiffness, in terms of effective lateral and rotational stiffness against load eccentricity, is presented. Seabed level statically equivalent foundation properties are updated by comparison against the observed modal behaviour and the optimised foundation parameters are presented, demonstrating a close match to the predictions of the PISA method.

An innovative approach: Combining OTV logging and manual core logging for geotechnical site investigation - A case study from Lesser Himalaya of eastern Nepal

Geotechnical exploration in the youngest Himalayan range requires a thorough understanding of subsurface geological conditions, including lithological, structural, and mechanical properties. Accurate data obtained from test boring, in-situ borehole tests, and logging are essential for this purpose. Down-hole optical imaging, such as the optical televiewer (OTV), can provide a cost-effective and efficient approach to capturing structural geotechnical data by providing high-resolution, oriented images of borehole walls. This study aims to test the effectiveness of a hybrid logging method that combines OTV imaging with manual core logging for geotechnical investigations in the eastern part of the Lesser Himalaya, Nepal. The hybrid method addresses inherent problems such as actual depth matching, errors in the reorientation of acquired cores, and ambiguity in the placement of weak zones like intensely fractured zones, fault gouges, and sheared zones. This approach is critical for identifying weak zones and ensuring safe and stable construction.

Performance assessment of borehole arrangements for the design of rectangular shallow foundation systems

This study proposes a framework to evaluate the performance of borehole arrangements for the design of rectangular shallow foundation systems under spatially variable soil conditions. Performance measures are introduced to quantify, for a fixed foundation layout and given soil sounding locations, the variability level of the foundation system bearing capacities in terms of their mean values and standard deviations. To estimate these measures, the recently proposed random failure mechanism method (RFMM) has been adopted and extended to consider any arrangement of rectangular foundations and boreholes. Hence, three-dimensional bearing capacity estimation under spatially variable soil can be efficiently performed. Several numerical examples are presented to illustrate the applicability of the proposed framework, including diverse foundation arrangements and different soil correlation structures. Overall, the proposed framework represents a potentially useful tool to support the design of geotechnical site investigation programs, especially in situations where very limited prior knowledge about the soil properties is available.

Adaptive Fusion Sampling Strategy Combining Geotechnical and Geophysical Data for Evaluating Two-Dimensional Soil Liquefaction Potential and Reconsolidation Settlement

In engineering practice, properly characterizing the spatial distribution of soil liquefaction potential and induced surface settlement is essential for seismic hazard assessment and mitigation. However, geotechnical site investigations (e.g., cone penetration test (CPT)) usually provide limited and sparse data with high accuracy. Geophysical surveys provide abundant two-dimensional (2D) data, yet their accuracy is lower than that of geotechnical investigations. Moreover, correlating geotechnical and geophysical data can effectively reduce site investigation costs. This study proposes a data-driven adaptive fusion sampling strategy that automatically develops an assessment model of the spatial distribution of soil liquefaction potential from spatially sparse geotechnical data, performs monitoring of liquefaction-induced settlement, and integrates spatiotemporally unconstrained geophysical data to update the model systematically and quantitatively. The proposed strategy is illustrated using real data, and the results indicate that the proposed strategy overcomes the difficulty of generating high-resolution spatial distributions of liquefaction potential from sparse geotechnical data, enables more accurate judgment of settlement variations in local areas, and is an effective tool for site liquefaction hazard analysis.

Optimal tip shape for minimum drag and lift during horizontal penetration in granular media

AbstractHorizontal penetration in granular media is ubiquitous, from tunneling and geotechnical site investigation, to root growth and the locomotion of burrowing animals in nature. This contribution couples the discrete element method (DEM) with ant colony optimisation, a heuristic optimisation algorithm, to find the optimal tip shapes to minimise drag and lift forces during horizontal penetration. The tip which minimizes drag has a slender profile with a low tip curvature, to give a drag force that is $$15.6\%$$ 15.6 % lower compared to a conventional CPT intruder, however this shape induces a downwards force that increases with intruder depth. Conversely, the tip that minimizes lift is blunt, with a high tip curvature and short width, and reduces the drag and lift forces by $$4.5\%$$ 4.5 % and $$30.8\%$$ 30.8 % (respectively) compared to the CPT. The lift and drag forces are competing optimisation objectives, thus the tip shape with the optimal trade-off between drag and lift forces can be established using Pareto optimality. The Pareto optimal tip shape reduces the drag and lift forces by $$10.7\%$$ 10.7 % and $$19.4\%$$ 19.4 % respectively, and is strikingly similar to the profile of a sandfish. This contribution shows that when a common goal exists, bio-inspired solutions can offer an optimal solution to engineering applications. We also show the potential to integrate DEM simulations within an optimization framework to develop innovative design solutions.

Identification of Soil Layers and Properties by Vibration Measurements during Dynamic Penetration Testing

Abstract A common geotechnical site investigation method in Sweden is soil-rock sounding. A steel rod is driven into the ground by a percussion drill and different drilling parameters are recorded. The penetration speed and pushing force are used to determine the soil layer profile, the presence of boulders, and the depth to bedrock. This article describes a novel concept where the ground vibrations generated by the drill bit are measured by a geophone at the ground surface to gain more information about the penetrated material. The results of vibration measurements are analyzed in terms of frequency spectra, spectrograms, and two new parameters called “spectral concentration” and “overtone ratio.” These results show that the method can identify the depth of the groundwater table, distinguish boulders from penetration into bedrock, and possibly identify silt, sand, and gravel layers. More data are needed in different soil types to verify the reliability of the concept.

Bayesian Framework for Assessing Effectiveness of Geotechnical Site Investigation Programs

Site investigation data can play an important role in not only site characterization but also the improvement of engineering design. It is tempting to predict the potential benefit of a site investigation before it is actually conducted. In this study, a Bayesian framework to assess the effectiveness of a site investigation program is proposed. The expected reduced variance (ERV) is employed to measure the uncertainty reduction owing to site investigation data. The uncertain outcomes of all possible site investigation data are considered through their probabilistic distributions. Then a framework to calculate the ERV is established. Monte Carlo simulation is adopted and the statistical uncertainty can be analyzed through the bootstrap method. Two examples of a shallow foundation on sand and an undrained slope are illustrated in this paper. Based on the examples studied, the optimal sampling layout is also discussed. The proposed method is promising to be applied in optimization of the sampling layout for various geotechnical practices.

An approach to estimate influence region of field investigation sampling points, investigation zone in civil infrastructure projects and brief review of existing field investigation process

Geotechnical investigation is a cyclic process. The interdependency between different steps involved in whole process, i.e., from planning, performing investigation, transporting samples and analysis of data obtained from field or lab, present opportunities for improvements and further discussion.For estimating number of investigation sampling points required for a particular site and selection of investigation methods, today we mostly relay on information from previously conducted site investigations, local experience from that area or site. In geotechnical investigation getting field samples which represents actual ground conditions for the whole site is well known challenge and has been highlighted in many research papers.In this paper we are presenting concept of influence region of geotechnical investigation points and an approach for estimation of the same. We believe the influence region concept; its estimation approach will provide a mathematical model and will help in deciding number of sampling points at the geotechnical investigation sites. In author’s opinion the presented concept will further improve the quality of geotechnical ground investigation process and will provide help in getting representative, quality field samples for laboratory analysis.The presented concept and estimation approach in this paper is based on author’s field experience and exposure gained from working in Swedish infrastructure projects. We think the influence region concept will further improve the existing geotechnical ground investigation process and will open opportunities for its validation through focused scientific research.A brief overview of the existing ground investigation processes has been covered in the paper, along with examples from geotechnical site investigations from Trafikverket (The Swedish Transport Administration) projects in Sweden.

One-dimensional Site Response Analysis and Liquefaction Evaluation of Can Tho City, Vietnam

Can Tho is the biggest city in the Mekong River Delta, one of the five national central cities in Vietnam. However, it has not been studied regarding seismic hazard estimation. For this purpose, one-dimensional nonlinear site response analysis of this city was performed in this paper. The measured in-situ profiles and corresponding geotechnical site investigation and laboratory test data were utilized to develop the site model for site-specific ground response analysis. A suite of earthquake records compatible with the Vietnam rock design spectrum (TCVN 9386:2012) was used as input ground motions at the bedrock. The results show that Peak Ground Acceleration (PGA) increases from the bedrock to the surface. Maximum PGA is 0.083g for O Mon district (P1) and 0.073g for Cai Rang district (P2). The maximum shear strain is reported to be 0.35% for P1 and 0.45% for P2. The recommended amplification factors are 1.7 for P1 and 1.9 for P2. Even though Can Tho city is composed of soft layers, liquefaction is unlikely to occur.