Documents published in Scipedia

• 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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• Effect of Precast Pile Driving on Liquefaction Potential Mitigation of Sandy Silts Based on CPTu

  K. Fakharian*, F. Kaviani-Hamedani, M. Bahrami, H. Vaezian, I. Attar, M. Hashemi, A. Osouli, T. Bahrami

  ISC2024.

  
  

  Abstract

  Precast concrete piles are adopted as a foundation solution in liquefiable silty sand and sandy silt layers of north of Oman Sea shorelines for large diameter liquid tanks. The ground water table is about 6 m deep, and a highly potential liquefiable layer is identified from 7 m and continues to about 12 m deep. This liquefiable layer not only reduces the pile shaft skin friction, but also could have caused damage to slender precast piles as a result of kinematic and inertia shear forces and bending moments, in particular at the intersection of liquefiable and non-liquefiable cohesive layers underneath. The main objective of the paper is to evaluate the effect of densification of sandy silt deposits attributed to pile installation and the possibility of liquefaction mitigation effects due to radial compaction of the soil. CPTu tests were carried out prior to and after the installation of piles. It is noticed that both qc and fs were increased depending on the center-to-center spacing of piles. Liquefaction analysis is carried out on CPTu results before and after piling installation and it is observed that the sandy silt layers are significantly strengthened against liquefaction and the safety factor notably rose above unity after the pile driving operation. The results are compared with triaxial cyclic tests on samples taken from a comparable depth for further investigation, indicating that the mitigation has occurred simply with a 10 percent increase in the relative density of the liquefiable sandy silt layer

  Abstract
  Precast concrete piles are adopted as a foundation solution in liquefiable silty sand and sandy silt layers of north of Oman Sea shorelines for large diameter liquid tanks. [...]

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• Characterization of the Consolidation Coefficient Behavior from Piezocone and Piezoball Tests in a Brazilian Soft Soil

  J. Sosnoski*, A. Meier, G. Dienstmann, H. Nierwinski, E. Odebrecht, F. Mantaras

  ISC2024.

  
  

  Abstract

  The present paper aims to validate the applicability of a piezoball test equipped with pressure transducers at the probe’s tip, middle, and equator faces to estimate the coefficient consolidation behavior of a soft soil deposit. The proposals of Mahmoodzadeh et al. (2015) and Liu et al. (2023), derived from numerical solutions, can be adopted to estimate horizontal coefficients of consolidation (ch) through the piezoball dissipation measurements. The dissipations tests were performed at depths of 4, 6, 8, and 10 m and were conducted up to at least about 70% of dissipation of the excess pressure generated during the penetration, except for the test at a depth of 4m, done at 85%. Results were directly compared with piezocone, and the estimated values for the consolidation coefficient were similar for all methodologies applied, both at the face and equator positions.

  Abstract
  The present paper aims to validate the applicability of a piezoball test equipped with pressure transducers at the probe’s tip, middle, and equator faces to estimate the [...]

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• The Use Of Large-Scale 3D Numerical Modeling to Identify Areas of Increased Seismic Risk in Polish Underground Copper Mines.

  K. Fuławka*, L. Stolecki, I. Jaśkiewicz-Proć, R. Kołodziej

  ISC2024.

  
  

  Abstract

  Seismic activity and related rockbursts are currently one of the most dangerous threats negatively affecting work safety and continuity of operation in Polish underground copper mines. Taking into account the experience to date, it can be clearly stated that current technical and organizational tools do not make it possible to eliminate mining tremors, however, by taking appropriate actions, it is possible to minimize the threat and partially control it, e.g. by active de-stressing the rock mass. However, developing an appropriate schedule of preventive activities is still a burning issue. This is due to the random nature of mining-induced seismic phenomena which makes it impossible to accurately predict the place, energy and time of the seismic event. A breakthrough could be the use of stage-based large-scale numerical modelling based on which it would be possible to track stress changes with ongoing exploitation and locate areas of increased seismic risk. This study presents the results of large-scale three-dimensional FEM-based numerical modelling enabling tracking of changes in stress distribution with the progress of exploitation. Then stress distribution and areas identified as prone to instability occurrence were correlated with the areas of high-energy seismic tremors manifestation. Models were prepared at monthly intervals, and validated with the use of measurements obtained with underground geomechanical monitoring systems. As preliminary analyses show, well-validated numerical models can be the basis for estimating seismic risk and may be useful at the stage of designing methods for preventing active rockbursts and seismicity prevention.

  Abstract
  Seismic activity and related rockbursts are currently one of the most dangerous threats negatively affecting work safety and continuity of operation in Polish underground [...]

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• A Data-Driven Approach to Predict Shear Wave Velocity from CPTu Measurements: An Update

  I. Entezari*, J. Sharp, P. Mayne

  ISC2024.

  
  

  Abstract

  This paper is an extension of our previous research investigating the potential of machine learning models to estimate shear wave velocity (Vs) from piezocone penetration test (CPTu) measurements. The aim of this update is to examine the effect of incorporating geographical information, namely latitude and longitude, as input parameters to the machine learning models. New models are developed by incorporating both CPTu parameters and spatial coordinates as input features and are compared to models developed with only CPTu parameters. Furthermore, SHAP (SHapley Additive exPlanations) analysis is employed to assess the importance of different features and variables in the developed machine learning models. The results show improvement in prediction performance when adding geographical data, indicating the influence of geological variations on Vs. The paper shows the potential of using geospatial information to improve the data-driven approach for estimating soil properties from CPTu tests when large worldwide datasets are available.

  Abstract
  This paper is an extension of our previous research investigating the potential of machine learning models to estimate shear wave velocity (Vs) from piezocone penetration [...]

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• A Data Driven CPTu-PINNs Method to Predict Soil Settlement

  H. Li, Z. Ouyang*

  ISC2024.

  
  

  Abstract

  Obtaining soil parameters through laboratory tests and solving the governing equations that describe soil settlement can be time-consuming, making immediate on-site predictions of soil settlement challenging. In-situ testing provides a more efficient approach to obtain soil parameters than laboratory tests. Data from the Piezocone penetration test (CPTu) can be used for on-the-spot interpretation of soil mechanical parameters, which can then be incorporated into the governing equations for soil settlement calculation. Physics Informed Neural Networks (PINNs) algorithm uses automatic differentiation method to directly embed partial differential equations (PDEs) into a deep learning neural network and provides solution for these PDEs in a cost-effective manner compared to traditional numerical methods. In this paper, a framework integrating data from CPTu and PINNs to predict soil settlement is proposed and evaluated through comparison with numerical simulations from Finite Element Methods (FEMs). Results show that the framework gave a reasonably good agreement with the FEMs benchmark while substantially reduced the computation time. This method allows for immediate on-site prediction of soil settlement during site investigations, thus better guiding surveying and construction activities.

  Abstract
  Obtaining soil parameters through laboratory tests and solving the governing equations that describe soil settlement can be time-consuming, making immediate on-site predictions [...]

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• Can Seabed Spatial Uncertainty Be Quantified Using Advanced Statistical Approaches?

  J. Valderrama*, M. O'Neill, F. Bransby, P. Watson, M. Bertolacci, A. Zammit-Mangion

  ISC2024.

  
  

  Abstract

  Knowledge of seabed properties away from investigated locations is often required, for example, when geotechnical surveys are sparse or when the field layout changes between investigation and construction phases. In such cases, design lines that appropriately incorporate the uncertainty of the seabed properties must be defined to ensure reliable (yet not overly costly) design. This paper explores how two different approaches, traditional engineering judgement and advanced statistical methods, fare at quantifying the uncertainty of a real offshore site. This is achieved by ‘hiding’ different data and ‘scoring’ the predictive performance of the methods against a range of criteria. The work reveals that complex geological sites are significantly more challenging to represent than the stationary random fields often examined in research and suggests that more advanced approaches incorporating broader data sets are required to reduce uncertainty.

  Abstract
  Knowledge of seabed properties away from investigated locations is often required, for example, when geotechnical surveys are sparse or when the field layout changes between [...]

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• Soil Variability From High-Resolution S-Wave Full-Waveform Inversion: Deriving Reliable Cone-Tip Resistance From Vs for Geotechnical Evaluations

  E. Revelo-Obando*, R. Ghose, M. Hicks

  ISC2024.

  
  

  Abstract

  Capturing the spatial variability in soil is crucial for ground response analyses in the context of seismic hazard mitigation. The lateral variability in thickness and properties of the different soil layers is one of the main factors that determines the variability of the ground motion spectrum from one location to another. The absence of such lateral variability information in the subsoil in between the locations of Cone Penetration Tests (CPTs) may be compensated by the use of more densely sampled seismic data. In this research we aim to derive a shear-wave velocity field through seismic full-waveform inversion that yields a model resolution approaching that of high-resolution seismic CPT surveys. Following this, a datadriven correlation between geophysical and geotechnical information is attempted through the application of new machine-learning-based approaches.

  Abstract
  Capturing the spatial variability in soil is crucial for ground response analyses in the context of seismic hazard mitigation. The lateral variability in thickness and properties [...]

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