Documents published in Scipedia

• Data-driven Multi-stage Sampling Strategy for Machine Learning of Underground Digital Twins Consdiering Stratigraphic Uncertainty

  C. Shi*, Y. Wang, V. Kamchoom

  ISC2024.

  
  

  Abstract

  A sound understanding of subsurface geological conditions is crucial for the digitalisation of underground infrastructure. The building and updating of underground digital twins heavily rely on sparse geotechnical measurements (e.g., boreholes) retrieved from the ground, and an efficient sampling strategy can facilitate the interpretation of subsurface heterogeneities. Geotechnical sampling design can be viewed as a constrained optimization process that aims to obtain as much geological information as possible from a limited number of sampling locations within a given site boundary. In this study, a data-driven intelligent sampling strategy is proposed to optimize borehole locations for a multi-stage site investigation of a three-dimensional (3D) geological domain. The initial sampling plan is determined using weighted centroidal Voronoi tessellation, which assigns uniform sampling densities to zones of different importance. Measurements obtained from the initial stage are combined with prior geological knowledge to build underground digital twins using an image-based stochastic modelling method. Multiple realizations of the geological domain can be developed under the framework of Monte Carlo simulation, and stratigraphic uncertainties associated with multiple random realizations can be quantified using information entropy. The location with the maximum entropy is adaptively selected as the next optimal sampling location. The proposed method is the first sampling strategy that can explicitly consider 3D subsurface stratigraphic variations. The performance of the proposed multi-stage sampling strategy is demonstrated using a simulation example. Results indicate that the proposed method can efficiently identify the optimal sampling locations while accounting for irregular site geometries and 3D subsurface stratigraphic uncertainties.

  Abstract
  A sound understanding of subsurface geological conditions is crucial for the digitalisation of underground infrastructure. The building and updating of underground digital [...]

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• Predicting Soil Behavior Types Along the Danube: An AI-Driven Approach Using CPT Data in the Szigetköz Floodplain Area

  E. Koch*, S. Alsamia

  ISC2024.

  
  

  Abstract

  The Szigetköz (Hungary) is a hotbed of sand boil formation, owing to the combination of a 100-250 m thick gravel layer beneath a relatively thin covering of poor soil with varying thickness. Soil behavior is critical for flood protection in this region. This work proposes a novel way to predict Soil Behaviour Types (SBT) based on detailed CPT data collected from 29 sites in the Szigetköz area using an artificial intelligence (AI) model. The study follows a methodically planned approach that includes data collecting, preprocessing, SBT categorization based on the SBT chart developed by Robertson et al. (1986), and AI model building. The CPT dataset contains critical metrics like cone resistance and friction ratio, which are essential in characterising soil behavior. The AI model, built with powerful machine learning algorithms, is intended to learn complicated associations within data to forecast SBT classifications. Extensive feature selection, hyperparameter tuning, and cross-validation are all necessary steps in model construction to ensure accuracy and generalizability. The results show that the model can accurately forecast SBT classifications for the Szigetköz area, shedding information on the soil's behavior near the Danube River. Spatial distribution visualizations emphasize the region's many SBT categories, giving valuable information for engineering projects, land use planning, and environmental conservation activities. The AI model's interpretability elucidates the major CPT parameters driving SBT forecasts, providing stakeholders with actionable information for decision-making. Furthermore, validation of the model with new, previously unseen CPT data confirms its applicability and robustness in real-world circumstances.

  Abstract
  The Szigetköz (Hungary) is a hotbed of sand boil formation, owing to the combination of a 100-250 m thick gravel layer beneath a relatively thin covering of poor soil with [...]

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• Artificial Intelligence in Dynamic Compaction for Geotechnical Site Characterization

  J. Park*, C. Lee

  ISC2024.

  
  

  Abstract

  We present a novel method using four artificial intelligence (AI) algorithms to anticipate the cumulative degree of soil compaction (CDSC) after dynamic compaction (DC). Four AI algorithms adopted in this study include support vector regression SVR, artificial neural network (ANN), random forest (RF), and gradient boosting machine (GBM). Input variables for AI algorithms involve the average SPT N-value before dynamic compaction, cumulative applied energy normalized with a cross-sectional area of tamper, and the number of the tamper drops. Apart from cross-validation with a testing set, additional in situ test data compiled from a different section within the studied site are used to estimate the generalized capacity of the AI models. In addition, we conduct out-of-distribution analyses for the four AI algorithms in view of parametric studies. The CDSC prediction performance for the four AI models results in high prediction metrics of accuracy with the r2 higher than 0.9 for the testing scenario while the r2 of the other AI models is more than 0.9 when out-of-sample data are considered except for the GBM. The ANN seems to be the best model as the parametric study considers out-of-distribution data and suggests a strong relationship between input variables and CDSC that is more coherent with engineering principles for DC. Finally, the ANN model can be utilized to develop a mathematical model for CDSC prediction.

  Abstract
  We present a novel method using four artificial intelligence (AI) algorithms to anticipate the cumulative degree of soil compaction (CDSC) after dynamic compaction (DC). Four [...]

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• Integrating laboratory and geophysical data considering measurement errors

  J. Xie, J. Huang*, S. Huang

  ISC2024.

  
  

  Abstract

  Laboratory and geophysical tests are commonly used in site characterization. Combining these data sets based on empirical relationships can essentially enhance data interpretation. While in traditional approaches, the uncertainties in the relationship between these data sets are ignored. The Bayesian updating method is used to consider these uncertainties. Besides, the uncertainties due to measurement errors in the laboratory tests, particularly for preconsolidation pressure, are considered based on the kriging fitting method. The outcomes of kriging fitting are utilized to establish the prior distribution, and these outcomes are then compared against the baseline established by the trend fitting method. The Markov chain Monte Carlo (MCMC) algorithm is applied to incorporate the shear wave velocity measurements from a seismic dilatometer test to derive the posterior distribution. Bayesian updating of parameters considering measurement errors is able to get a more convincing design profile.

  Abstract
  Laboratory and geophysical tests are commonly used in site characterization. Combining these data sets based on empirical relationships can essentially enhance data interpretation. [...]

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• Application of handheld mobile terrestrial laser scanning for cultural heritage documentation.

  A. Llabani*, N. Kopliku

  ISC2024.

  
  

  Abstract

  The preservation and documentation of cultural heritage sites are fundamental to safeguarding our shared history and identity. This study explores the innovative application of the viDoc RTK rover for cultural heritage documentation, presenting a forward-looking approach to capturing high-precision spatial data in the preservation and analysis of Brari bridge located in Tirana, Albania. Handheld mobile terrestrial laser scanning (HMTLS) offers versatility and mobility, enabling rapid and non-invasive data acquisition in complex and challenging environments. The methodology encompasses equipment selection, data acquisition techniques, and data processing workflows tailored for HMTLS technology. The results demonstrate the potential of HMTLS to produce highly accurate 3D models, showcasing intricate architectural details and capturing fine surface textures with an accuracy of 3 cm. Furthermore, the portability of handheld devices allows for documentation in areas where traditional scanning methods may be impractical. This study underscores the transformative impact of HMTLS on cultural heritage preservation, offering a cost-effective, efficient, and accessible means of creating digital archives. The adoption of this technology contributes to the long-term conservation, research, and education associated with our cultural heritage, ensuring that these invaluable assets continue to inspire and inform future generations.

  Abstract
  The preservation and documentation of cultural heritage sites are fundamental to safeguarding our shared history and identity. This study explores the innovative application [...]

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• A combined approach to Automated Parameter Determination (APD)

  I. Marzouk*, F. Tschuchnigg

  ISC2024.

  
  

  Abstract

  The success of numerical analysis relies on several factors, with one crucial aspect being the accurate determination of constitutive model parameters. Extracting these parameters directly from in-situ tests has several advantages, such as costeffectiveness and minimal soil disturbance. However, obtaining soil parameters directly from in-situ tests is not feasible, as empirical correlations are used to interpret them. An ongoing research project aims to create an automated parameter determination (APD) framework using a graph-based approach to determine constitutive model parameters from in-situ tests. The process involves using two spreadsheets as input: the first defines the parameters, while the second specifies the correlations used to compute them. The system then generates connections between the parameters and computes values for each one. The paper discusses the validation of the correlations database used by the system, which includes over 100 correlations for deriving parameters for various soil types. The framework determines parameters based on cone penetration tests (CPT), dilatometer tests (DMT), and in-situ shear wave velocity measurements. The system's output is compared to values interpreted from laboratory tests. To collect data for this validation, a web-based application "Datamap" was employed, which stores and categorizes geotechnical data. The validation process utilized data from the Norwegian GeoTest Sites (NGTS), specifically the NGTS-silt project. The parameters were calculated based on CPT, DMT, and in-situ shear wave velocity measurements. Ongoing research aims to evaluate the accuracy of the derived parameters and expand the system's capabilities to include additional in-situ tests

  Abstract
  The success of numerical analysis relies on several factors, with one crucial aspect being the accurate determination of constitutive model parameters. Extracting these parameters [...]

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• The stratigraphic reconstruction of longitudinal tunnel based on improved coupled Markov chains

  Q. Jiang*, J. Zhang, D. Zhang

  ISC2024.

  
  

  Abstract

  The distribution of natural strata is uncertain due to tectonic movements and sedimentation. Capturing geological uncertainty is a challenge for traditional deterministic models. In this study, an improved three-dimensional coupled Markov chains method for probabilistic stratigraphic reconstruction was developed. This method considers the correlation between the field borehole data. On this basis, an inversion analysis method for horizontal transition probability matrix estimation is proposed. This method makes the predictions more suitable for possible stratigraphic distributions. The accuracy of the method was further verified by different borehole schemes from the Mawan Tunnel in Shenzhen. The results show that the proposed method can still have high accuracy when the number of boreholes is sparse. This method can reflect the asymmetry, continuity and anisotropy of three-dimensional strata.

  Abstract
  The distribution of natural strata is uncertain due to tectonic movements and sedimentation. Capturing geological uncertainty is a challenge for traditional deterministic [...]

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• 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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