Geotechnical Site Investigation Research Articles

Influence of Geotechnical Site Investigation in Horizontal Plane on Slope Reliability

Influence of Geotechnical Site Investigation in Horizontal Plane on Slope Reliability

Non-parametric construction of site-specific non-Gaussian multivariate joint probability distribution from sparse measurements

Construction of a joint probability distribution for correlated geotechnical properties is often needed in geotechnical reliability-based analysis and design. Geotechnical properties vary across sites and follow site-specific and non-Gaussian probability distribution, because of different geological processes that soils and rocks in different sites have undergone. In addition, site-specific measurements on geotechnical properties are usually sparse and correlated in geotechnical practice, leading to the difficulty in estimation of meaningful joint probability distribution when using conventional parametric statistical methods. To address this issue, this paper develops a non-parametric and data-driven approach for characterizing the site-specific non-Gaussian multivariate joint probability distribution without pre-selection of the marginal probability distribution type. Two key components for constructing a multivariate joint probability distribution (i.e., marginal probability distribution for each variable and correlation matrix for all variables) are estimated separately using Bayesian Gaussian mixture and Bayesian compressive sampling (BCS) and Karhunen-Loève (KL) expansion. The proposed method is illustrated using both simulated and real data in geotechnical site investigation. The results show that the proposed method performs well for both examples. The proposed method does not model spatial variability explicitly but is based on the assumption of independent measurement data along depth, and thus measurement records are required to be sufficiently spaced apart in depth (e.g., more than 1 scale of fluctuation) to satisfy this assumption.

Infinite slope stability model and steady-state hydrology-based shallow landslide susceptibility evaluations: The Guneysu catchment area (Rize, Turkey)

The main purpose of this study is to investigate shallow landslide susceptibility by considering the infinite slope stability model and steady-state hydrological conditions. The prediction performance of the Stability Index Mapping (SINMAP) carried out by considering the mechanical and hydrological parameters and soil thickness values for different residual soils derived from different geological formations was evaluated. For this purpose, comprehensive geotechnical site investigations were conducted in the Guneysu catchment area located at the east of Rize in the Eastern Black Sea region of Turkey, where shallow landslides are frequently observed within the residual levels developed as a result of decomposition of magmatic rocks. The research was performed in four stages: (i) The general characteristics of the region were examined; (ii) detailed investigations were carried out for the preparation of shallow landslide inventory of the Guneysu catchment area; (iii) to reveal the mechanical and hydrological characteristics of the residual soils, disturb and undisturbed samplings were carried out, and geophysical investigations were performed; (iv) as the last stage, shallow landslide susceptibility was assessed by implementing the SINMAP mathematical model. As a result, the average accuracy value of the models produced to predict shallow landslide initiation in the region was obtained to be 96.7%. Considering the statistics achieved in this research, it is realized that the differentiation of soil material increases the model prediction capacity. Additionally, pre-evaluations of the mechanical and hydrological characteristics of the residual soils also increase the estimation performance.

Practical Aspects of Routine Geotechnical Site Investigations

It's likely that on any given day there are hundreds of geotechnical site investigations in progress in North America alone. While many of these investigations are carried out in support of large p...

Evaluation of Seismic Site Amplification Using 1D Site Response Analyses at Ba Dinh Square Area, Vietnam

This study presents a case study on ground response analysis of one of the important cultural heritages in Hanoi, Vietnam. One‐dimensional nonlinear and equivalent linear site response analyses which are commonly applied to solve the problem of seismic stress wave propagation are performed at the Ba Dinh square area. A measured in‐situ shear wave velocity profile and corresponding geotechnical site investigation and laboratory test data are utilized to develop the site model for site‐specific ground response analysis. A suite of earthquake records compatible with Vietnamese Design Code TCVN 9386: 2012 rock design spectrum is used as input ground motions at the bedrock. A few concerns associated with site‐specific ground response evaluation are analyzed for both nonlinear and equivalent linear procedures, including shear strains, mobilized shear strength, and peak ground acceleration along with the depth. The results show that the mean maximum shear strains at any soil layer are less than 0.2% in the study area. A deamplification portion within the soil profile is observed at the layer interface with shear wave velocity reversal. The maximum peak ground acceleration (PGA) at the surface is about 0.2 g for equivalent linear analysis and 0.16 g for nonlinear analysis. The ground motions are amplified near the site natural period 0.72 s. The soil factors calculated in this study are 1.95 and 2.07 for nonlinear and equivalent linear analyses, respectively. These values are much different from the current value of 1.15 for site class C in TCVN 9386: 2012. A comparison of calculated response spectra and amplification factors with the local standard code of practice revealed significant discrepancies. It is demonstrated that the TCVN 9386: 2012 soil design spectrum is unable to capture the calculated site amplification in the study area.

Development of Consolidated Sarawak Geotechnical Site Investigation for Sarawak Soil

The development of spatial or non-spatial digitized Consolidated Sarawak Geotechnical Site Investigation (CoGSI) database aims to enable information to be stored in a digital form with the efficient search and fast retrieving data. The CoGSI database for Sarawak Soil is a function of a database management system, which consists of the site information; includes the project name, locations coordinates (division), borehole logs, field and laboratory test results. The main objectives of the project are to collect, store & digitized all verified & approved Geotechnical SI data and the input gathered from the local authority, consultants and contractors. The digitized database would be enormous value for future planning of infrastructure developments; making preliminary design estimates for earthwork/foundation assessment; and future decision making with early identification of potential areas for construction purposes, which will produce an economic and safe design. The SI databases are important resources where the insufficient ground conditions information, will cause either a significantly over-designed or an under-designed solution, which lead to potential failures. In addition, the project is also intended to realize a web-based application to allow users to search borehole, view bore-logs and provides digital downloadable boreholes data of the available ground information in a standard format for analysis. At present most of the SI data are available in the form of hardcopy reports, which is time consuming and often frustrating, especially when the required report or data cannot be found. In summary, the Consolidated Sarawak Geotechnical Site Investigation (CoGSI) database system, which stored in a digital format are deployed as a web-based system. This platform provides exploration of the digital databases, which then can be accessed anywhere and anytime through the desktop & portable computer and smart phones with internet access facility.

Data-driven determination of sample number and efficient sampling locations for geotechnical site investigation of a cross-section using Voronoi diagram and Bayesian compressive sampling

Geotechnical analyses and designs in practice are often performed using a two-dimensional (2D) cross-section, information of which is obtained from site investigation. The quality of site investigation results depends greatly on the number and locations of sampling during site investigation. However, increasing the sample number requires additional expenditure, human resources, and time. In addition, geotechnical site investigation is a multi-stage process, and the measurements at preliminary stage are often sparse and limited, hence additional samples might be needed in later stages. This study develops a smart sampling strategy for planning of multistage geotechnical site investigation of a cross-section using Voronoi diagram, Bayesian compressive sampling (BCS), and information entropy. The proposed method is non-parametric and data-driven, and it can determine both the necessary sample number and their corresponding optimal sampling locations. The proposed smart sampling strategy applies Voronoi diagram to determine the efficient sampling locations of measurements at preliminary stage of site investigation, and uses BCS and information entropy to automatically decide whether or not additional samples are needed and their efficient sampling locations in a self-adaptive and data-driven manner. The proposed method is illustrated using real soil data and showed to perform well and robustly.

Measuring while drilling in Florida limestone for geotechnical site investigation

Florida limestone can be challenging to recover during coring operations, as the rock generally is soft, porous, and often highly weathered. Low recoveries coupled with poor rock quality are common in Florida, which limits the data available for geotechnical design. However, the low recoveries and poor rock quality may be attributable to coring techniques and not the rock’s in situ condition. This paper explores integrating measuring while drilling (MWD) into standard coring procedures to provide in situ strength assessment and optimize core recoveries and rock quality to improve rock mass characterization. Six drilling parameters were monitored during the research, and a controlled drilling environment was developed to investigate each monitored parameter’s effects. Variable drill bit configurations were also explored to investigate the effects of bit geometry. Interdependent relationships between the drilling parameters were discovered and a new concept of operating within optimal drilling parameter ranges based on these relationships is introduced. The coring practices developed in the controlled environment were then tested in natural Florida limestone. It was concluded that operating within the optimal ranges allows in situ strength assessment and improves core recoveries and rock quality.

A Geostatistical Solution to Estimating Groundwater Inflows in Deep Rock Tunnels with Validation Through Case Studies

Estimating groundwater inflows in rock deep tunnels is critical to the safety of personnel constructing the tunnel and the cost of construction. This paper presents two case studies for estimating steady-state groundwater inflows into deep rock tunnels. The case studies included the Chattahoochee and Nancy Creek tunnels. In this paper, statistical methods were applied to packer test data obtained from exploratory borings during geotechnical site investigations. A semi-empirical procedure was utilized for estimating groundwater inflows into these deep rock tunnels. It is the most widely used method/procedure in the tunnel industry. The Chattahoochee and Nancy Creek tunnels are deep rock tunnels which exhibit radial flow conditions. For the radial flow condition, a statistical analysis presented in this paper indicates that the semi-empirical procedure works well; however, variations in the procedure and/or an insufficient amount of testing could lead to underestimations of the inflow quantities. Packer test data plotted in histograms were observed in all cases to be log-normally distributed for the radial flow condition. Modeling using Monte Carlo simulations was observed to be an effective tool for removing irregularities in the distribution of the data and incorporating high-end permeability data to derive reasonable groundwater inflow estimates. This paper is based on the PhD thesis entitled “Improvements to the Current State of the Practice in Steady-State, Groundwater Inflow Estimates into Rock Tunnels based on the Application of Statistical Analysis Methods of Packer Test Data obtained from Exploratory Borings During Geotechnical Site Investigations.”

An innovative application of borehole acoustic image and amplitude logs for geotechnical site investigation

Down-hole acoustic televiewer (ATV logging) is a geophysical well-logging device known to be able to reveal underground in-depth image. However, reviewing the current developments in the area of geotechnical engineering, ATV logging data were predominantly used for the purpose of visualizing and calculating the specific structural features in an open borehole, and nothing more. Noticeably, to the majority of engineering applications, it appears that ATV logging played a minor role compared to other geophysical borehole logging tools. Our study does not take it for granted. A systematic study is presented of the significance and consequent applications of ATV logging. The significance of correlation between acoustic amplitude and different geotechnical parameters is discussed and found to be consistent with previous findings. The result tells that ATV logging is actually able to provide timely and ground-truth information during site characterization, if properly used. Additionally, in our study, a simple rock mass evaluation index, Aco-RQD, is proposed, which is applicable to all aspects of geotechnical engineering as long as the continuous ATV logging data are available.

Optimal Testing Locations in Geotechnical Site Investigations through the Application of a Genetic Algorithm

Geotechnical site investigations are an essential prerequisite for reliable foundation designs. However, there is relatively little quantitative guidance for planning optimal investigations, including the choice of testing location. This study uses a genetic algorithm to find the ideal testing locations of various numbers of boreholes with respect to pile foundation performance. The optimization has been done separately for single-layer and multi-layer soils, which infer what is best for obtaining soil material properties and delineating layer boundaries, respectively. A sensitivity analysis was conducted to find the genetic algorithm parameters that result in high quality solutions within a reasonable timeframe. While boreholes arranged in a regular grid pattern provide good performance in many cases, there are instances where optimized locations provide a cost saving of A$2 million, or 4.2% of the construction cost. A set of recommended testing guidelines is provided.

Seismic hazard microzonation of ground response parameters in Bengkulu City, Indonesia

This paper presents the seismic hazard microzonation of ground motion parameters on the basis of earthquakes events in Bengkulu City. Geotechnical site investigation and geophysical survey were performed in Bengkulu City. Furthermore, data were used to observe the geological condition. The local seismic design code for soft soil was used to generate spectral acceleration, which was later used as the scaling factor to generate ground motion for earthquake simulation. One-dimensional seismic ground response analysis was conducted to simulate earthquake wave propagating through soil layers. Several parameters, such as peak ground acceleration (PGA), spectral accelerations (SA) at 0.2 s and 1 s, and seismic wave amplification factor were observed. Those parameters were also depicted in seismic microzonation maps and addressed to identify potential impact during the earthquake. The results showed that the possibility of structural building responses along coastal area of Bengkulu City is relatively high. Therefore, a local seismic design code is important to propose as the alternative for reliable seismic design code. In general, the results of this study could give a recommendation to local government to consider seismic resistance design for buildings in Bengkulu City.

Smart sampling strategy for investigating spatial distribution of subsurface shallow gas pressure in Hangzhou Bay area of China

Subsurface shallow gas has been widely discovered in coastal areas across continents at buried depths of approximately 10–100 m. Its presence poses a significant hazard and risk during underground construction, e.g., risk of explosion and fire when using tunnel boring machines for subway construction. Therefore, it is important to determine the spatial distribution of the shallow gas pressure during site investigation for subsequent hazard analysis and mitigation. In engineering practice, the gas pressure is often sparsely measured at a limited number of locations using modified cone penetration tests (CPTs) owing to time, budget, resource, and technical constraints. There are no rational or quantitative methods available for the planning of site investigation of gas pressure. Therefore, it is important to determine ways to properly and efficiently plan a site investigation of the shallow gas pressure during geotechnical site investigation (e.g., how to determine the required number of modified CPTs (i.e., the sample size) and their corresponding optimal locations). A smart sampling strategy is developed for planning of site investigation in this study that uses innovative data analytic methods (e.g., Bayesian compressive sampling, BCS, and information entropy) to automatically determine sample size and optimal sampling location for gas pressure characterization. The proposed smart sampling strategy is illustrated using numerical examples. The results show that the proposed smart sampling strategy performs reasonably well.

Probabilistic soil classification and stratification in a vertical cross-section from limited cone penetration tests using random field and Monte Carlo simulation

Classification and stratification (or zonation) of subsurface soils are important tasks in geotechnical site investigation. Due to the limit of time, budget, or access to subsurface soils, subsurface soil information obtained from investigation points (e.g., boreholes, cone penetration tests (CPTs)) in a specific site is often limited (e.g., a few boreholes or CPT soundings), resulting in great challenge in interpretation of the site investigation data obtained and significant uncertainty in the inferred subsurface soil classification and stratification. A novel probabilistic method is developed in this paper for properly accounting for the uncertainty associated with CPT-based subsurface soil classification and stratification. The method properly classifies and stratifies subsurface soils in a 2D vertical cross-section from limited CPT soundings. A limited number of 1D CPT sounding data is firstly interpolated to produce a 2D vertical cross-section, and the associated interpolation uncertainty is modelled explicitly using random field theory. Probabilistic soil classification model is also developed to account for the uncertainty associated with the empirical soil behavior type classification model. Then, the interpolation uncertainty and soil classification model uncertainty are considered simultaneously in a Monte Carlo simulation framework. Both simulated and real data examples are used to illustrate the proposed method. The results indicate that the proposed method well predicts subsurface soil classification and stratification in a 2D vertical cross-section from limited CPT soundings, and properly quantifies the associated uncertainty. In addition, sensitivity studies on interpolation uncertainty and soil classification model uncertainty are performed.

Probabilistic outlier detection for sparse multivariate geotechnical site investigation data using Bayesian learning

Various uncertainties arising during acquisition process of geoscience data may result in anomalous data instances (i.e., outliers) that do not conform with the expected pattern of regular data instances. With sparse multivariate data obtained from geotechnical site investigation, it is impossible to identify outliers with certainty due to the distortion of statistics of geotechnical parameters caused by outliers and their associated statistical uncertainty resulted from data sparsity. This paper develops a probabilistic outlier detection method for sparse multivariate data obtained from geotechnical site investigation. The proposed approach quantifies the outlying probability of each data instance based on Mahalanobis distance and determines outliers as those data instances with outlying probabilities greater than 0.5. It tackles the distortion issue of statistics estimated from the dataset with outliers by a re-sampling technique and accounts, rationally, for the statistical uncertainty by Bayesian machine learning. Moreover, the proposed approach also suggests an exclusive method to determine outlying components of each outlier. The proposed approach is illustrated and verified using simulated and real-life dataset. It showed that the proposed approach properly identifies outliers among sparse multivariate data and their corresponding outlying components in a probabilistic manner. It can significantly reduce the masking effect (i.e., missing some actual outliers due to the distortion of statistics by the outliers and statistical uncertainty). It also found that outliers among sparse multivariate data instances affect significantly the construction of multivariate distribution of geotechnical parameters for uncertainty quantification. This emphasizes the necessity of data cleaning process (e.g., outlier detection) for uncertainty quantification based on geoscience data.

Railway critical speed assessment: A simple experimental-analytical approach

When constructing a new railway line, its long length means there are significant financial implications associated with determining the geodynamic ground properties. Therefore, this paper presents recommendations to optimize the efficiency and depth of such a geotechnical site investigation. Firstly, a numerical analysis is performed to investigate the effect of soil layering, soil stiffness and track bending stiffness on critical velocity. It is shown that each of these variables play an important role, however for most practical cases, only the top 8 m of soil is influential. Track dynamics are rarely affected by soil properties at depths below this, meaning this is the maximum required depth of soil investigation. Using this knowledge, a hybrid experimental-analytical methodology is presented, based on a geophysical Spectral Analysis of Surface Waves (SASW) experimental setup to compute the ground dispersion curve and an analytical model to compute the track dispersion curve. The experimental and analytical results are combined directly, to accurately compute the critical velocity. This approach is attractive because: 1) SASW tests are typically accurate to ≈8 m (when using a mobile exciter) thus matching the required depth needed for critical velocity computation, 2) soil property uncertainties are inherently accounted for, 3) the uncertainties associated with SASW inversion are avoided. The approach is attractive when constructing new railway lines and upgrading the speed of existing lines because it can potentially yield site investigation cost savings. In-situ field work is performed to show the practical application of the technique.

A modified seismic reflection approach for engineering geology investigation in fractured rock zones

Delineating soil/rock interface is critical for geotechnical and tunnel site investigations in engineering geology. In Singapore, we also need to draw attention to local faults and fractured rock zones because the regional geology of Peninsular Malaysia and Singapore contains some minor, steep and brittle faults propagating at some areas in the island city. In addition to drilling boreholes, seismic reflection survey is a viable option to mitigate the uncertainties in areas without boreholes. However, the current practice of seismic reflection still follows the methods used in the petroleum exploration community. This investigation reviews and modifies the seismic reflection approach specifically customized for geotechnical and tunnel site investigation, which is partly different from petroleum exploration. We demonstrate the modified approach through a field testing in Singapore. The resultant image from the field test fairly captures the soil/rock interface, and it is validated based on the vertically drilled boreholes. A local fault or a fracture rock zone is also identified in the migrated image. The extent and location match with the record of horizontal directional coring (HDC). This addresses the concern of fracture rock zone raised from the regional and local geology and provides useful information for geotechnical and tunnel engineers. In addition, we further discuss the general challenges of using the modified seismic reflection approach. This work aims to help the geotechnical and tunnel community better understand the non-invasive seismic reflection survey.

Bayesian Supervised Learning of Site-Specific Geotechnical Spatial Variability from Sparse Measurements

Although the properties of geomaterials vary spatially, geotechnical site investigations often take sparse measurements from a limited number of locations. To estimate geotechnical properties at unsampled locations, interpolation is often needed. This paper presents a Bayesian supervised learning method for interpolation of site-specific geotechnical data from sparse measurements. The interpolation is considered as a supervised learning problem and is solved under a Bayesian framework. Numerical examples are used to evaluate performance of the proposed method and to provide a comparative study with ordinary kriging, a popular interpolation method in geosciences applications. Results show that when the available measurement points are sparse and limited, the Bayesian supervised learning method performs better than kriging. When the number of measurement points is large, results from the proposed method and kriging are almost identical. In addition, the proposed method is data-driven and nonparametric. It does not require a detrending process when dealing with nonstationary data, and it bypasses estimation of a parametric form of autocorrelation structure (e.g., semivariogram in conventional kriging interpolation). A well-known challenge in kriging is the selection of a suitable semivariogram function form or a suitable trend function form for detrending, given sparse geotechnical data. The proposed Bayesian supervised learning method bypasses these challenges and is particularly suitable for nonstationary geotechnical data. Standard preprocessing steps such as outlier removal and noise reduction apply to Bayesian supervised learning.

Next-generation liquefaction database

The Next-Generation Liquefaction database is a resource for the geotechnical hazard community. It is publicly available online under the following digital object identifier (DOI): 10.21222/C2J040. The database organizes objective liquefaction data into tables and fields (columns of information), with the relationships among the tables and fields described by a schema. The data are organized into tables pertaining to (1) sites, including geotechnical and geophysical site investigation data, surface geology information, and laboratory test data; (2) earthquake events, including source and ground motion information; and (3) observations of sites following events. The schema was vetted through community outreach efforts involving multiple workshops and meetings. Users can view the data, download existing data, and upload new data through a geographic information system (GIS)-based graphical user interface. Information uploaded to the database is reviewed by a database working group to verify consistency between uploaded data and source documents. The database is replicated in DesignSafe where users can interact with the data using Python scripts in Jupyter notebooks, view point cloud data using Potree, and interact with geospatial data using QGIS.

A Geotechnical Database for Utah (GeoDU) enabling quantification of geotechnical properties of surficial geologic units for geohazard assessments

Geotechnical borehole information is often used for liquefaction hazard mapping, but can be highly variable in terms of quantity and quality. In addition, geotechnical borehole logs are often provided as images in reports rather than delivered in a structured, queryable database, which makes the logs and supplementary information difficult to organize particularly across a large geographic area. In contrast, surficial geologic mapping is generally available and often accessible in geographic information systems (GIS) format. This article’s objective is to describe the compilation of a geotechnical database for regional mapping purposes and to demonstrate the value of documenting geotechnical data into a consistent data format. Specifically, this article describes the development of three geotechnical borehole databases compiled in Utah, which has been coined the Geotechnical Database for Utah (GeoDU). The database is used to quantify geotechnical properties for subsequent liquefaction evaluations of surficial geologic units comprising similar depositional environment and age. The resulting GeoDU is an important resource for future efforts with many applications including community data sharing and planning for preliminary geotechnical site investigations.