Documents published in Scipedia

• 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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• Advances in the Characterisation of Sites Contaminated with Petroleum Hydrocarbons: Insights from a Collaborative Effort

  J. García-Rincón*, E. Atekwana, E. Gatsios, R. Lenhard, R. Naidu

  ISC2024.

  
  

  Abstract

  Petroleum hydrocarbons (PHCs) such as gasoline and diesel are among the most common and widespread contaminants in urban and industrial environments. The authors were engaged by the imprint Springer to complete a book giving visibility to technologies overcoming limitations associated with conventional characterisation approaches, as well as pertinent concepts and methods that may still be underutilised by the industry internationally. The open access book entitled ‘Advances in the characterisation and remediation of sites contaminated with petroleum hydrocarbons’ was prepared as a contributed volume involving the participation of more than 100 global experts from academia, government agencies, and the private sector. An analysis of the book contents yielded general insights into the state of the art regarding the investigation of PHC-impacted sites. As highlighted in several chapters, fate and transport of fuel products are controlled by multiphase flow mechanics and constitutive relations intrinsically linked to biological phenomena and the spatial and temporal variability of multiple subsurface properties. Most site characterization methods presented in the book (from direct-push vertical profiling and biogeophysics to sequence stratigraphy or molecular biological tools) address aspects often overlooked in conventional site investigation projects, including: (i) the entrapment of fuel products due to capillary forces, (ii) the essential role played by microbial activity, and (iii) subsurface heterogeneity effects. Further research and adoption of up-to-date concepts and methods are encouraged to ensure best practices are implemented and PHC risks are managed sustainably and responsibly

  Abstract
  Petroleum hydrocarbons (PHCs) such as gasoline and diesel are among the most common and widespread contaminants in urban and industrial environments. The authors were engaged [...]

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• Towards An Integrated And Automated Digital Workflow In Geotechnical Engineering

  R. Brinkgreve*, A. Zekri, A. Laera

  ISC2024.

  
  

  Abstract

  The use of soil data is essential in geotechnical design, but in a preliminary project phase such data are usually limited to that inferred from field tests, like CPT, SPT or DMT. In previous publications by the authors and co-workers, it was shown how such data can be automatically processed into soil profiles and parameter sets for geotechnical finite element analysis. Another publication demonstrated the automated processing and creation of geological models as an intermediate step to more advanced 3D geotechnical modelling in a BIM / Digital Twin environment, which facilitates the link with other disciplines and stakeholders in a project. The major challenge of connecting layers across multiple 1D boreholes to form 3D soil layers is overcome by using a Machine Learning clustering algorithm. As a next step, the previously introduced Automated Parameter Determination (APD) method (connecting correlations using Graph theory) is applied based on averaged CPT parameters from all contributing layer sections. The result is an automated system that creates a complete 2D or 3D finite element model, including constitutive model parameters, for geotechnical analysis purposes. An automated system may be very efficient when exploring different design alternatives in an early stage of a project. However, it is important to emphasize the role and responsibilities of the geotechnical engineer in the design process, which requires the system to be transparent, verifiable, and adaptable. This paper describes the state-of-the-art of this ongoing research project.

  Abstract
  The use of soil data is essential in geotechnical design, but in a preliminary project phase such data are usually limited to that inferred from field tests, like CPT, SPT [...]

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• Data-driven site characterization - Focus on small-strain stiffness

  H. Felic, I. Marzouk, F. Tschuchnigg, T. Peterstorfer

  ISC2024.

  
  

  Abstract

  Non-linear soil behaviour adds complexity in accurate parameter selection for numerical modelling. One of these parameters is the small-strain shear stiffness. This parameter depends strongly on the soil mass density and the shear wave velocity; the latter can be determined through in-situ tests or laboratory tests. The paper focuses on training various machine learning models to predict shear wave velocity estimates based on raw data from cone penetration test soundings. Three decision tree algorithms are considered for the analysis: XGBRegressor, HistGradientRegressor, and RandomForest. Various data preprocessing approaches are investigated, including noise removal and outlier identification, to assess their impact on the model performance. The results indicate that different data preprocessing approaches yield significant differences in the model performances. When applied to unseen raw data from a sand site of the Norwegian GeoTest Site, the model demonstrates promising predictive capabilities and is in a good agreement with well-known correlations. This study underlines the importance of data quality and preprocessing for reliable machine learning models. To enhance transparency and reproducibility, a GitHub repository with all the used files is made available online.

  Abstract
  Non-linear soil behaviour adds complexity in accurate parameter selection for numerical modelling. One of these parameters is the small-strain shear stiffness. This parameter [...]

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