Documents published in Scipedia

• Digitization of subsurface geological stratigraphy using machine learning and neighborhood aggregation

  Y. Hu*, Z. Wang, X. Guo

  ISC2024.

  
  

  Abstract

  In engineering geology and geotechnical engineering, subsurface soils and rocks are natural geomaterials and exhibit inherent variability in stratigraphy due to geological deposition process. Explicit knowledge of subsurface stratigraphy is a critical input for the analysis, design, and construction of geotechnical engineering systems. However, the accurate and reliable modelling of subsurface geological stratigraphy is challenging due to the limited number of available boreholes in practice and the complex nature of soil stratigraphy. This paper presents an innovative machine learning framework built upon the neighborhood aggregation technique for the prediction of digitized subsurface geological stratigraphy. To predict the stratigraphy at a given point of interest, neighborhood aggregation is first performed to intelligently consolidate the stratigraphy information from its neighboring boreholes, resulting in additional features associated with the target location. By combining the extra stratigraphy information with conventional location-specific features, the framework enhances the predictive capabilities of classical machine learning models at a finer scale. The proposed framework is implemented using common machine learning models and is validated using a simulated benchmark 3D example. The results of leave-one-out cross-validation demonstrate that the proposed framework can improve the performance of classical machine learning models, leading to more reasonable stratigraphy transition and associated uncertainty quantification.

  Abstract
  In engineering geology and geotechnical engineering, subsurface soils and rocks are natural geomaterials and exhibit inherent variability in stratigraphy due to geological [...]

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• Calibration and Test Bed Simulation of a 40 Cm2 Cptu Cone

  E. Baziw*

  ISC2024.

  
  

  Abstract

  In cone penetration testing (CPT) an electronic penetrometer is pushed at a constant rate into penetrable soils and cone bearing (qc), sleeve friction (fc) and dynamic pore pressure (u) are recorded with depth. The measured qc, fs and u values are utilized to estimate soil type and associated properties. Cone tips have areas which vary from 5cm2 to 40 cm2. The larger tips allow for the penetration of gravely soils while small cone tips are utilized for shallow soil investigations. The measured cone bearing and sleeve friction values are blurred or averaged. The measurements are also susceptible to anomalous peaks and troughs due to the relatively small diameter cone tip penetrating sandy, silty and gravelly soils. The cones with relatively smaller cone tips are significantly more susceptible to the anomalous peaks and troughs while the cones with larger cone tips are more susceptible to the smoothing of the cone tip and sleeve friction measurements. Baziw Consulting Engineers (BCE) has invested considerable resources in addressing the qc and fs measurements distortions. This paper outlines the techniques developed by BCE and integrates them so that optimal soil properties can be obtained from CPT data sets. Particular focus is put on relatively larger cone tips because they can penetrate soils with high resistance and are less susceptible to the additive measurement noise of anomalous peaks and troughs. The anomalous peaks and troughs are more challenging to remove or minimize than the qc and fs blurring effects. It is of paramount importance to first implement newly developed signal processing and optimal estimation algorithms on extensive test bed simulations prior to processing real data sets. This paper also outlines the results from processing a challenging test bed simulation of a 40 cm2 cone tip data set with BCE’s newly developed algorithms.

  Abstract
  In cone penetration testing (CPT) an electronic penetrometer is pushed at a constant rate into penetrable soils and cone bearing (qc), sleeve friction (fc) and dynamic pore [...]

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• Cone Penetration Test-Based Correlations to Forecast Critical State Parameters

  J. Gamez*, S. Olson

  ISC2024.

  
  

  Abstract

  In critical state soil mechanics, the critical state refers to the combination of effective stress and void ratio (e) at which a soil continues to shear with no change in effective stress, shear stress, and e. The phenomena can be visualized using the critical state line (CSL). The CSL represents the locus of e at critical state with effective mean stress (σ′mean). To define the CSL, the CSL slope (λ), termed “compressibility,” and CSL y-axis intercept at 1 kPa (Γ), termed “altitude,” are required. The CSL in e – σ′mean space provides a simple model of complex soil behavior that allows engineers to construct constitutive models using the state parameter (ψ), which is the mathematical difference between the in-situ e of the soil and the e of the soil at critical state. Currently, Γ can be obtained only through laboratory testing, while λ and ψ can be obtained via laboratory testing or correlation. This paper presents forthcoming correlations based on the ΔQ soil behavior index (which is obtained via the cone penetration test, CPT) to forecast Γ, λ, and ψ, and compares the ΔQ-based correlations’ performance to other CPT-based correlations as well as to data obtained from literature. To compare the correlations, the authors used data from a site investigation performed in Fraser River sand as part of the Canadian Liquefaction Experiment.

  Abstract
  In critical state soil mechanics, the critical state refers to the combination of effective stress and void ratio (e) at which a soil continues to shear with no change in [...]

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• Inferring Spatial Variation of Soil Classification Using Clustered Bayesian Analysis by Both CPT and Borehole Data

  H. Kamyab Farahbakhsh*

  ISC2024.

  
  

  Abstract

  Soil boundary delineation is an important task in geotechnical site characterization. It can be achieved by either extracting borehole samples, conducting laboratory tests, and classifying them according to a soil classification system such as the Unified Soil Classification System (USCS) or utilizing multiple cone penetration test (CPT) soundings, and identifying soil boundaries at the soundings from the Ic (soil behavior type index) profiles. However, most soil-layer delineation methods can only take a single type of test result as the input. For instance, the well-known Markov random field (MRF) method can only take soil-type data such as sand, silt, or clay at boreholes as the input. Recognizing that soil classifications and soil properties are correlated, this paper proposes a novel coupled MRF-Bayesian framework to infer the spatial variation of USCS classifications (e.g., sand, silt, and clay) as well as soil properties by integrating both CPT and borehole data. This integrated approach leverages both CPT and borehole data to address some main challenges e.g., uncertainties and multivariate soil data input in underground stratification problems by simultaneous sampling of soil properties and soil types. The new unified framework can accommodate multivariate data, hence the new framework is compatible with the geotechnical engineering practice. The uncertainties for the spatial variation of USCS classification at sounding locations are quantified through a “layer-specific” Bayesian updating i.e., updating posterior cross-correlation behaviors for different layers (such as sand, silt, and clay), independently. In this Bayesian updating, soil-type data can provide some information about the soil properties according to the unified soil classification system. Further, the soil boundaries can be identified across the entire domain by the realization of conditional random fields of soil properties once the spatial variation of USCS classification is inferred at sounding locations, followed by a 3-dimensional Markov random field process.

  Abstract
  Soil boundary delineation is an important task in geotechnical site characterization. It can be achieved by either extracting borehole samples, conducting laboratory tests, [...]

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• Data-Driven Two-Dimensional Near-Surface Seismic Imaging

  J. Vantassel*, S. Bhochhibhoya

  ISC2024.

  
  

  Abstract

  Non-invasive site characterization techniques have the potential to rapidly evaluate large subsurface volumes to guide subsequent invasive geotechnical site investigation. Among these methods, seismic full waveform inversion (FWI) stands out for its potential to recover detailed two-dimensional (2D) images of the subsurface. However, FWI’s need for substantial computational resources and sensitivity to the initial starting model has limited its utilization as a generalpurpose geotechnical site characterization tool. Addressing this, prior studies have shown data-driven methods can predict 2D subsurface structures composed of soil over rock. In the present study, we aim to generalize these findings to all nearsurface conditions. We propose a novel model generation framework that utilizes techniques from geostatistics to generate complex 2D subsurface models. The generated models include dipping soil and rock layers, soil lenses, boulders, and underground utilities; none of which have been considered previously. We use our model generation framework to simulate 100,000 2D subsurface models. We simulate field data acquisition along these 100,000 synthetic models, by numerically solving the elastic wave equation using an impulse source at the model’s center surrounded by 24 receivers (12 on either side). The data-driven predictive model, trained on 90% of the simulated data, achieved a mean absolute percent error on the testing set of 19%. Furthermore, these predictions are made within fractions of a second circumventing the computational and starting-model-related challenges associated with traditional 2D FWI. These results demonstrate that data-driven methods can predict complex images of the subsurface to enable rapid subsurface imaging for geotechnical applications.

  Abstract
  Non-invasive site characterization techniques have the potential to rapidly evaluate large subsurface volumes to guide subsequent invasive geotechnical site investigation. [...]

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• Geological and geotechnical study of a construction site in Porto Romano area-Albania

  A. Malaj*, S. Allkja, J. Belliu, I. Kero

  ISC2024.

  
  

  Abstract

  Porto Romano port complex new facilities are part of the expansion plans of Durres port and its modernisation. However, the relocation site presented many significant spatial and geotechnical challenges to be considered and mitigated. Soil conditions and the country’s high seismic activity meant the project required extensive feasibility and technical studies to find a safe and sustainable approach. For a detailed geological and geotechnical investigation of the area, various geotechnical, geophysical tests were carried out such as: borings, SPT test, CPTU test, Seismic refraction, MASW, Downhole and HVSR allowing to obtain the fundamental resonance frequency of the ground. Field recordings per each layer were than compared and calibrated to the results and tests performed in the laboratory. The construction site displayed a variety of soils from soft to firm silty Clays, to loose to medium dense silty Sands and layers with high organic content. During execution of SPT tests, sandy layers gave more satisfactory results, meanwhile, for silty CLAY layers, the results of CPTU testing were considered in analysis. This detailed soil investigation and characterization served to properly design the new port facilities, identify, and protect from the liquefaction phenomena at this specific site.

  Abstract
  Porto Romano port complex new facilities are part of the expansion plans of Durres port and its modernisation. However, the relocation site presented many significant spatial [...]

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• Open-source implementation of the new unified CPT-based axial method in sands

  C. Sastre Jurado, B. Stuyts*

  ISC2024.

  
  

  Abstract

  This paper presents the implementation into the open-source finite element simulation framework OpenSees of the new Unified CPT-based method for driven piles in sands. The formulation incorporates the maximum skin friction and endbearing CPT values based on the new ISO-19901-4 under development, within the non-linear load-transfer curves calibrated against the measured responses from the static pile tests in the unified database. Special attention was paid to maintaining an open-source philosophy during the implementation. The important EURIPIDES research tests in highly dense sands are considered as a worked example to illustrate the application of the implemented method. The numerical benchmark shows good agreement between the model and full-scale measurements.

  Abstract
  This paper presents the implementation into the open-source finite element simulation framework OpenSees of the new Unified CPT-based method for driven piles in sands. The [...]

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• Comparison of Different Prediction Methods to Derive Synthetic CPT Profiles - An Offshore Wind Farm Case Study from the German North Sea

  L. Siemann*, P. Masoudi, R. Maraka, R. Opris, Y. Pande, N. Römer-Stange, N. Morales, T. Mörz

  ISC2024.

  
  

  Abstract

  The further development of offshore windfarm areas in various countries plays a key role in the transition of energy production towards renewable sources. As offshore windfarm areas tend to expand and the amount of ground truth data is limited, the estimation of geotechnical parameters at unknown locations integrating other site investigation data becomes a necessary tool. This is especially relevant for cost efficient area wide site characterization. Here, the proper integration and correlation of geotechnical and geophysical data is a key factor for reliable ground model building. This study investigates different prediction methods, while presenting a modelling framework which incorporates geological, geotechnical, and geophysical information to derive synthetic Cone Penetration Testing (CPT) profiles using offshore windfarm site investigation data from the German North Sea. We combine geological interpretation, CPT data and 2D ultra high-resolution seismic reflection data. The geophysical and geological information are used to guide geotechnical parameter prediction. Additionally, seismic horizons constrain the prediction as structural information. For evaluation, we test and compare several prediction techniques, with different level of complexity, from geostatistical methods to machine learning. Seismic attributes are used as auxiliary information to improve CPT parameter prediction. To validate the results, CPT parameters are predicted onto a representative 2D seismic line and a leave-one-out cross-validation (blindtest) is performed. Though all methods struggle to replicate local extremes, results indicate a reduction of prediction uncertainty when implementing seismic attributes.

  Abstract
  The further development of offshore windfarm areas in various countries plays a key role in the transition of energy production towards renewable sources. As offshore windfarm [...]

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• AI-Based Digitization of Legacy Ground Information

  J. Llopart*, P. Behjati, I. Aliguer

  ISC2024.

  
  

  Abstract

  Geotechnical characterization of site materials is of paramount importance in the construction and mining industry. The analysis of large volumes of geotechnical information from multiple sources leads to data-driven decisions that help to minimize uncertainty. For this purpose, a unified digital information platform becomes handy to have a global perspective and improve the analysis of available ground information data. Access to historic ground investigation data from previous projects during the project planning stage might increase efficiency. However, accessing and processing legacy data from companies’ databases is time and resources consuming. In the recent years, software tools that are capable of extracting data in a digital format from images have become popular, but still require human-supervised interpretation. A novel tool combining Optical Character Recognition (OCR), digital data extraction technologies and AI-based data interpretation system is presented herein. The state-of-the-art OCR technology is capable of accurately recognizing and extracting text from various document types, such as scanned documents, images, and PDFs. It utilizes advanced machine learning algorithms to process text, even in challenging conditions, ensuring data is extracted accurately and reliably. Then, a data interpretation system has been trained to identify the type of site characterization data and its structure while retrieving all the content in a digital format. All components work seamlessly together to provide a comprehensive solution for automating the interpretation and extraction of site characterization data, streamlining data management and analysis processes. The capability of gathering data from multiple sources in a unique ground information system provides valuable information for planning and design stages while decreasing costs, time and uncertainties. In addition, all these data are then available within DAARWIN platform to feed the ground model workflow.

  Abstract
  Geotechnical characterization of site materials is of paramount importance in the construction and mining industry. The analysis of large volumes of geotechnical information [...]

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• Direct-Push Profiling Technologies for Sustainable Investigation of Contaminated Sites

  J. García-Rincón*

  ISC2024.

  
  

  Abstract

  Most contaminated site investigations still rely on conventional characterisation approaches based on collecting a limited number of soil samples and installing long-screened wells. However, it is widely recognised that these methods cannot adequately capture the subsurface heterogeneity largely governing the fate and transport of contaminants. Following the example of cone penetration testing (CPT), multiple direct-push profiling tools have been developed over the years to investigate and manage contaminated sites in a more efficient and sustainable way. The objective of this work is to present well-established and emerging direct sensing technologies for contaminated site investigation and demonstrate how their application does not just result in a reduction of uncertainties but also in improved sustainability outcomes. The assessed technologies included the Hydraulic Profiling Tool (HPT), laser-induced fluorescence (LIF), Membrane-Interface Probe (MIP), and nuclear magnetic resonance (NMR). Direct-push profiling techniques were found to be valuable throughout the project lifecycle, from initial site screening phases to remedial design and closure. The high-density data collected helped to delineate contaminant source zones, preferential migration pathways and low-permeability zones. This information complemented the analysis of a reduced number of physical samples to optimise remedial designs and monitoring networks. Additional benefits related to sustainability concepts included the production of minimal investigation-derived waste, the need for less field campaigns and the little impact caused to site owners and their activities. High-resolution site characterisation approaches are paramount to conduct informed risk assessments and effectively achieve remediation goals.

  Abstract
  Most contaminated site investigations still rely on conventional characterisation approaches based on collecting a limited number of soil samples and installing long-screened [...]

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