Cone Penetration Test Data Research Articles

Estimating soil strength using ultra high resolution seismic: A case study from a shallow water windfarm

The increasing global reliance on offshore wind farms as a sustainable energy source necessitates precise soil assessment for foundation design, due to their high foundation costs and complex integration into marine environments. This study explores the utilization of ultra-high-resolution seismic data in conjunction with cone penetration test data for soil strength estimation in offshore wind farm development. We propose a convolutional neural network-based approach that leverages ultra-high-resolution seismic data for direct soil strength estimation, aiming to address the challenges of limited cone penetration test measurements and potential overfitting in complex geological settings. Our methodology involves preprocessing ultra-high-resolution seismic and cone penetration test data, interpreting and integrating geological unit information, and applying repeated k-fold cross-validation to evaluate model performance. The field data results demonstrate the model's efficacy in accurately predicting cone penetration test curve trends, albeit with some amplitude discrepancies. We highlight the significance of geological interpretation in predicting soil strength and identify the dependency on valid data within each geological unit as a limitation.

Numerical Study of Cone Penetration Tests in Lunar Regolith for Strength Index

The cohesive properties of lunar regolith, combined with a low-gravity environment, result in it having a distinct mechanical behavior from sandy soil on Earth. Consequently, empirical formulas derived from cone penetration tests (CPTs) for calculating the shear strength parameters of Earth’s sand cannot be directly applied to lunar regolith. This study utilized the three-dimensional discrete element method (DEM) to numerically simulate triaxial shear tests and cone penetration tests in a lunar environment. The particle contact model for lunar regolith in the discrete element method (DEM) simulation incorporated the hysteresis effect of van der Waals forces, thereby simulating the cohesive properties of lunar regolith in a lunar environment. We proposed a relationship for calculating the shear strength index of lunar regolith based on normalized cone tip resistance using the results from triaxial and CPT simulations and referencing empirical formulas derived from ground-based CPT data. The results of this study provide a valuable reference for future lunar CPTs.

UNet-like transformer for 1D soil stratification using cone penetration test and borehole data

Subsurface stratification is crucial for the construction safety of underground projects. The one-dimensional (1D) soil stratification aims at identifying segmentation points that separate soil strata. Current engineering practice mainly requires human judgement, which is time-consuming, labour-intensive, and heavily relies on domain expertise. Other probabilistic methods, such as Bayesian approaches, usually involve complex expressions. With the advent of artificial intelligence, deep learning has emerged as a powerful tool in various domains. The UNet, as a typical convolutional neural network, has been extensively utilized for its superior performance in segmentation tasks, but struggles to capture global and long-range semantic information due to the locality of convolution operations. To realize intelligent and automatic 1D soil stratification, this paper introduces a UNet-like Transformer (ULTra) that integrates multiple data sources, including cone penetration test and borehole data, to incorporate prior knowledge. The architecture features a multi-level Transformer with shifted windows in both the encoder and decoder to extract context features and restore spatial resolution, respectively. Experimental results demonstrate that the ULTra outperforms other UNet variants, particularly in detecting minor textures and local details, underscoring the benefits of integrating Transformers into a standard UNet. Case studies indicate that compared with probabilistic methods, the ULTra enables automatic 1D soil stratification using original exploration data with less human intervention, which is fast, effective, and could be continuously improved through interaction with human knowledge, thus streamlining the intelligent data analysis.

A smarter approach to liquefaction risk: harnessing dynamic cone penetration test data and machine learning for safer infrastructure

This paper presents a novel approach for assessing liquefaction potential by integrating Dynamic Cone Penetration Test (DCPT) data with advanced machine learning (ML) techniques. DCPT offers a cost-effective, rapid, and adaptable method for evaluating soil resistance, making it suitable for liquefaction assessment across diverse soil conditions. This study establishes a threshold criterion based on the ratio of the penetration rate to the dynamic resistance (e/qd), where values exceeding four indicate high liquefaction susceptibility. ML models, including Support Vector Machine (SVM) optimized with Particle Swarm Optimization (PSO), Grey Wolf Optimizer (GWO), Genetic Algorithm (GA), and Firefly Algorithm (FA), were employed to predict the e/qd ratio using key geotechnical parameters, such as fine content, peak ground acceleration, reduction factor, and penetration rate. The SVM-PSO model demonstrated superior performance, with high R2 values of 0.999 and 0.989 in the training and testing phases, respectively. The proposed methodology offers a sustainable and accurate approach for liquefaction assessment, reducing the environmental impact of geotechnical investigations, while ensuring reliable predictions. This study bridges the gap between field testing and advanced computational techniques, providing a powerful tool for geotechnical engineers to assess liquefaction risks and design resilient infrastructures.

Multistep procedure for estimating non-linear soil response in low seismicity areas—a case study of Lucerne, Switzerland

SUMMARY The impact of non-linear soil behaviour on seismic hazard in low-to-moderate seismicity areas is often neglected; however, it may become relevant for long return periods. In this study, we used fully non-linear 1-D simulations to estimate the site-specific non-linear soil response in the low seismicity area, using the city of Lucerne in Switzerland as an example. The constitutive model considers the development of pore pressure excess and requires calibration of complex soil models, including the soil dilatancy parameters. In the absence of laboratory measurements, we mainly used the cone penetration test data to estimate the model variables and perform inversion for the dilatancy parameters. Our findings, using Swiss building code-compatible input ground motions, suggest a high probability of strong non-linear behaviour and the possibility of liquefaction at high ground motion levels in the case study area. While the non-linearity observations from strong-motion recordings are not available in Lucerne, the comparison with empirical data from other sites and other methods shows similarity with our predictions. Moreover, we show that the site response modelled is largely influenced by the strong pore pressure effects produced in thin sandy water-saturated layers. In addition, we demonstrate that the variability of the results due to the input motion and the soil parameters is significant, but within reasonable bounds.

Leveraging Bayesian methods for addressing multi-uncertainty in data-driven seismic liquefaction assessment

When assessing seismic liquefaction potential with data-driven models, addressing the uncertainties of establishing models, interpreting cone penetration tests (CPT) data and decision threshold is crucial for avoiding biased data selection, ameliorating overconfident models, and being flexible to varying practical objectives, especially when the training and testing data are not identically distributed. A workflow characterized by leveraging Bayesian methodology was proposed to address these issues. Employing a Multi-Layer Perceptron (MLP) as the foundational model, this approach was benchmarked against empirical methods and advanced algorithms for its efficacy in simplicity, accuracy, and resistance to overfitting. The analysis revealed that, while MLP models optimized via maximum a posteriori algorithm suffices for straightforward scenarios, Bayesian neural networks showed great potential for preventing overfitting. Additionally, integrating decision thresholds through various evaluative principles offers insights for challenging decisions. Two case studies demonstrate the framework's capacity for nuanced interpretation of in situ data, employing a model committee for a detailed evaluation of liquefaction potential via Monte Carlo simulations and basic statistics. Overall, the proposed step-by-step workflow for analyzing seismic liquefaction incorporates multifold testing and real-world data validation, showing improved robustness against overfitting and greater versatility in addressing practical challenges. This research contributes to the seismic liquefaction assessment field by providing a structured, adaptable methodology for accurate and reliable analysis.

Assessment of Driven Pile Ultimate Capacity through Artificial Neural Network Analysis of Cone Penetration Test Data

Assessment of Driven Pile Ultimate Capacity through Artificial Neural Network Analysis of Cone Penetration Test Data

Integrating Machine Learning in Geotechnical Engineering: A Novel Approach for Railway Track Layer Design Based on Cone Penetration Test Data

The cone penetration test (CPT) has emerged as a cost-effective and time-efficient method for assessing soil conditions relevant to railway track infrastructure. The geotechnical data obtained from the CPT serve as crucial input for asset managers in designing optimal sublayers and form layers for track renewal works. To properly assess the condition of soil layers, various soil behavior type charts and machine learning models based on CPT data have been published to help engineers classify soils into groups with similar properties. By understanding the properties of the soils, an optimal substructure can be designed to minimize extensive maintenance and reduce the risk of derailment. However, when analyzing multiple CPTs, the diversity and non-uniformity of subsoil characteristics pose challenges in designing a new optimal trackbed. This study presents an automated approach for recommending thicknesses of sublayers and form layers in railway tracks based on CPT data, employing machine learning algorithms. The proposed approach was tested using CPT data from the Belgian railway network and showed very good agreement with results from traditional soil investigation interpretations and layer design. A Random Forest classifier, fine-tuned through Bayesian optimization with a cross-validation technique and trained on 80% of the datasets, achieved an overall accuracy of 83% on the remaining 20%. Based on these results, we can conclude that the proposed model is highly effective at accurately designing sub-ballast layers using CPT data.

Assessment and development of empirical correlations between flat plate dilatometer and cone penetration tests for Auckland soils

ABSTRACT Based on a database of over 2900 cone penetration test (CPT) and flat plate dilatometer test (DMT) data pairs obtained from Auckland, New Zealand, this paper assesses the applicability of several empirical CPT-DMT correlations and presents newly developed regional correlations for use in geotechnical engineering applications. Empirical correlations using the DMT-based material index are assessed. As both the DMT-based material index (ID ) and the soil behaviour type index (Ic ) from CPT data are often used to estimate soil behaviour characteristics when no samples are retrieved, these were used to develop subset databases. In general, the correlation used to estimate the material index (ID ) tended to slightly overestimate the measured values for Auckland soils, while better performance could be observed after removing data points where Ic and ID soil classifications were incompatible. In contrast, the correlations used to estimate horizontal stress index (KD ), and dilatometer modulus (ED ) underestimated the measured values for the database used herein, with the largest underestimates for low cone tip resistance values. This paper presents new correlations for estimating KD and ED based on CPT parameters for Auckland soils, providing improved estimates, with stable bias values across the range of input parameters considered.

Integration of Fuzzy Sets, AHP, and TOPSIS Methods for Estimation of Liquefaction Potential Zones in Wates Groundwater Basin, Kulon Progo, Special Region of Yogyakarta

Abstract In regional development planning, geological, environmental, and socio-economic conditions must be considered. Among the crucial things is the potential for disasters in the region. The Wates Groundwater Basin area has great regional development potential, starting from the establishment of the New Yogyakarta International Airport (NYIA) to the Southern Causeway (JJLS) construction. Various developments are predicted to continue to grow along with the development of areas targeted by the government for this area so the mapping of potential disasters is necessary as a means of achieving Sustainable Development Goals (SDGs). One of the potential disasters in CAT Wates is a liquefaction disaster, which is supported by geological, hydrogeological, and seismic data in this area which is prone to liquefaction. To confirm this potential, this research was conducted using geological and hydrogeological data from previous studies. The result of this research is a more detailed and objective zoning map of the liquefaction potential even though it uses minimal data. This is done by integrating the Fuzzy Sets, AHP, and TOPSIS methods, while simultaneously validating the result using the Cone Penetration Test (CPT) data. The results show that the eastern and southern regions of the basin which are composed of river deposits and coastal deposits, respectively, have a high to very high relative vulnerability to liquefaction, while the central region of the basin has a low to moderate relative vulnerability, and the northern region of the basin tends to have relatively very low vulnerability. That result is aligned with the total thickness of the liquefiable layer from CPT data. It indicates that the combined method is proven to be reliable for liquefaction susceptibility spatial mapping. Thus, the result hopefully can be used as a reference for land use planning in the Wates Groundwater Basin area.

Multi-fidelity fusion for soil classification via LSTM and multi-head self-attention CNN model

An effective soil classification method is essential for soil stratigraphy interpretation. Traditional methods rely on classification systems in design codes or empirical formulae based on single source data, which may not be accurate or suitable for every site. Recent advancements in data fusion techniques and deep learning have shown promising results in a wide range of practical domains, which provides motivation to explore its potential application in soil classification. To that end, this paper introduces a novel machine learning architecture using boosting long-short term memory (LSTM) machine to map low fidelity cone penetration test (CPT) data to high fidelity laboratory test (LT) data which are then fused and fed into a multi-head self-attention convolutional neural network (MSCNN) for soil classification. A rigorous loss function is developed to train a LSTM-MSCNN model in an end-to-end workflow. The MSCNN classifier is benchmarked against traditional machine learning methods to demonstrate its superior outperformance, with ablation analyses exploring the related merits of the attention mechanism and the influence of attention heads on classification results. The results reveal a significant improvement in the prediction of true ground profiles using the proposed LSTM-MSCNN model over conventional methods.

A Critical Review of Cone Penetration Test-Based Correlations for Estimating Small-Strain Shear Modulus in North Sea Soils

The geotechnical characterisation of offshore wind farm sites requires measurement or estimation of the small-strain shear stiffness Gmax of the subsoil. This parameter can be derived from shear wave velocity Vs measurements if the bulk density of the soil is known. Since direct measurements of Vs are generally not available at all foundation locations in a wind farm, correlations with cone penetration test (CPT) results are often used to determine location-specific stiffness parameters for foundation design. Existing correlations have mostly been calibrated to onshore datasets which may not contain the same soil types and stress conditions found in the North Sea. The distinct geological history of the North Sea necessitates a critical review of these existing CPT-based correlations. They are evaluated against an extensive database of in situ Vs measurements in the southern North Sea. The importance of modelling the stress-dependent nature of Vs is highlighted, and a novel stress-dependent model for Vs from CPT data, which leads to an improved fit, is presented. As the small-strain stiffness is used as an input to foundation response calculations, the model uncertainty of the correlation can introduce significant uncertainty into the resulting foundation response. This transformation uncertainty is quantified for each of the correlations evaluated in this study and shows important variations.

Evaluating the Characteristics of Spatial Variability of Soil in Vertical Direction Highly Heterogeneous Region Based on Cone Penetration Test

Abstract Key parameters describing the spatial variability of soil properties based on the random field theory are the scale of fluctuation (SOF) and coefficient of variation (COV). To characterize the spatial variability of soil properties, reducing the impact of these errors and uncertainties is necessary. To accomplish this, for the five main layers of soil, we collected 18 cone penetration test (CPT) data from a highly heterogeneous region in Lianyungang New Airport, Jiangsu Province, China, and used the control variable method to analyze the influence of the estimation method of tendency, its function type and outliers. The results show that, compared with the ordinary least square method (OLSM), the least absolute deviation method (LADM) can more truly reflect the trend component of CPT parameters in the vertical direction, and the influence of other factors on SOF and COV is also studied, such as outliers and estimation functions of trend components. On this basis, a reasonable calculation process of SOF and COV is summarized, which provides a reference for the calculation of SOF and COV in vertical direction in the future. By comparing the SOF calculated by different models, the results show that the squared exponential (SQX) model has the highest SOF in 68.3% of the evaluation, and the single exponential (SNX) model has the lowest SOF in 64.4% of the evaluation. Moreover, we compared the SOF and COV of cone tip resistance (qc) and sleeve friction (fs), which showed that SOF of qc and COV of qc is lower than that of fs in 54.4 and 73.3% of all evaluations, respectively.

Liquefaction hazard of Wellington reclamations based on conventional analysis

Severe liquefaction-induced damage occurred in reclamation fills at the port of Wellington (CentrePort) in the 2016 Kaikōura earthquake, but little or no damage was reported in areas of older and shallower reclamations in central Wellington. Recent studies have therefore primarily focused on understanding the liquefaction hazard of the port, while little is still understood with regards to the fill characteristics and liquefaction potential of the Wellington reclamations outside CentrePort. This study utilizes data from comprehensive field investigations, including 58 new cone penetration tests (CPTs) performed both within and outside the port in the Wellington waterfront area, supplemented with over 100 CPTs from our previous studies at CentrePort, to characterize the liquefaction resistance of the reclaimed fills in Wellington. The geotechnical data is first used to define simplified schematic soil profiles and to determine characteristic CPT parameter values (25th–50th–75th percentiles) for fills encountered in different reclamation areas. These analyses highlight differences in the soil profiles, and the relative similarity in the estimate of liquefaction resistance based on conventional CPT-based assessment, of fills encountered in different reclamation areas despite differences in the age, techniques, and materials employed in the construction of these reclamations. Conventional liquefaction assessments of reclamation fills based on CPT data are then performed over the wider waterfront area for a range of earthquake scenarios and ground motion intensities relevant for Wellington. For recent, past earthquakes, correspondence between predicted and observed severity of the manifestations of liquefaction vary depending on the earthquake event and area of observations. Likelihood of liquefaction occurrence and severity of the effects of liquefaction are then discussed for characteristic return periods, in the context of the seismic hazard of Wellington.

Explainable AI models for predicting liquefaction-induced lateral spreading

Introduction: Earthquake-induced liquefaction can cause substantial lateral spreading, posing threats to infrastructure. Machine learning (ML) can improve lateral spreading prediction models by capturing complex soil characteristics and site conditions. However, the “black box” nature of ML models can hinder their adoption in critical decision-making.Method: This study addresses this limitation by using SHapley Additive exPlanations (SHAP) to interpret an eXtreme Gradient Boosting (XGB) model for lateral spreading prediction, trained on data from the 2011 Christchurch Earthquake.Result: SHAP analysis reveals the factors driving the model's predictions, enhancing transparency and allowing for comparison with established engineering knowledge. Notably, the SHAP values expose an unexpected behavior in the PGA feature. Moreover, the results demonstrate that the XGB model successfully identifies the importance of soil characteristics derived from Cone Penetration Test (CPT) data in predicting lateral spreading, validating its alignment with domain understanding.Discussion: This work highlights the value of explainable machine learning for reliable and informed decision-making in geotechnical engineering and hazard assessment.

Utilizing Machine Learning for Cone Penetration Test-Based Soil Classification

The cone penetration test (CPT) is widely used in geotechnical engineering to assess soil properties. Traditional methods of interpreting CPT data and classifying soils have limitations and are time-consuming. Machine learning (ML) algorithms offer a data-driven approach to automate and improve soil classification based on CPT data. In this study, the applicability of ML techniques was investigated to measure the reliability of soil classification prediction using raw CPT data. A dataset comprising raw CPT data and corresponding soil classifications derived from the adjacent boreholes was prepared for training and testing the selected ML techniques. Five ML algorithms, namely logistic regression, the support vector machine, the random forest (RF), K-nearest neighbors (KNN), and extreme gradient boosting (XGBoost), were applied. The results showed that the RF algorithm outperformed other ML methods, achieving an F1-score of 0.896. Comparing the performance of different algorithms, the RF consistently showed the best results, followed by XGBoost and KNN. These findings highlight the potential of ML algorithms, particularly the RF, in accurately predicting soil classification based on CPT data, thus improving the efficiency and reliability of geotechnical engineering applications.

Distributed acoustic sensing for seismic surface wave data acquisition in an intertidal environment

The application of distributed acoustic sensing (DAS) for shallow marine seismic investigations is assessed, in particular with respect to the collection of seismic surface wave data in an intertidal setting. Appropriate selection and directional sensitivity of fiber-optic cables is considered and the measured data is validated with respect to conventional seismic data acquisition approaches, using geophones and hydrophones, along with independent borehole and seismic cone penetration test (SCPT) data. In terms of cable selection, a reduction in amplitude and frequency response of an armored cable is observed, when compared with an unarmored cable. For seismic surface wave surveys in an offshore environment where the cable would need to withstand significant stresses, the use of the armored variant with limited loss in frequency response may be acceptable from a practical perspective. The DAS approach also has indicated good consistency with conventional means of surface wave data acquisition, and the inverted [Formula: see text] also is very consistent with downhole SCPT data. Observed differences in phase velocity between high tide (Scholte wave propagation) and low tide (Rayleigh wave propagation) are not thought to be related to the particular type of interface wave due to shallow water depth. These differences are more likely to be related to the development of capillary forces in the partially saturated granular medium at low tide. Overall, this study demonstrates that our novel approach of DAS using seabed fiber-optic cables in the intertidal environment is capable of rapidly providing near-surface S-wave velocity data across considerable spatial scales (multikilometer) at high resolution, which is beneficial for the design of subsea cables routes and landfall locations. The associated reduction in deployment and survey duration, when compared with conventional approaches, is particularly important when working in the marine environment due to potentially short weather windows and expensive downtime.

Soil classification based on unsupervised learning using cone penetration test data

Borehole data obtained during geological surveys are the most essential source for understanding soil stratification. It is a prerequisite to know the soil classes up to some depths prior to any construction. However, the direct method to identify the soil classes by drilling boreholes and testing soil samples is costly. A cost-effective alternative is the Cone Penetration Testing (CPT), which is one of the most popular soil investigation methods. This paper explores the intelligent classification of soil layers based on CPT data using three unsupervised machine learning methods: K-means, Gaussian Mixture Process, and BIRCH. The research investigates the classification performance of different models in scenarios with 2 combinations, 3 combinations, 4 combinations, and 5 combinations. The results indicate that the Gaussian Mixture Process method exhibits the best classification performance, followed by the BIRCH method, while K-means performs relatively poorly. Using unsupervised learning for intelligent soil layer classification offers a fast and clear process, but the accuracy still requires further improvement. This study provides a valuable reference for future soil classification studies.

Similarity Solutions for Spherical Cavity Expansion in Strain-Softening Soils and its Application in Cone Penetration Test

The cavity expansion theory is commonly employed to predict the grouting pressure and cone penetration resistance. However, existing theoretical solutions for cohesive-frictional soils with dilation fail to simultaneously consider both the elastic deformation in the plastic zone and strain-softening behaviour of soils. To address this limitation, a similarity solution was derived for the expansion of spherical cavities from a zero initial radius with an initial confining pressure in strain-softening Mohr–Coulomb materials. The results demonstrate good agreement with previous theories, predicting an ultimate pressure higher than that obtained by neglecting elastic deformation in the plastic zone but lower than that calculated by neglecting softening characteristics. Moreover, several influential factors affecting the radius of the plastic zone and ultimate pressure were meticulously examined. On-site cone penetration test data and the theoretical results showed notable agreement. This indicates that the proposed solution exhibits a better conformance with the field data than do the previous solutions.

Penyelidikan Tanah Menggunakan Metode Uji Sondir Di Universitas 17 Agustus 1945 Surabaya

Cone Penetration Test (CPT) is a method designed to determine and test the strength of soil layers quickly and is one of the most widely used testing methods. The aim of this research is to determine the resulting bearing capacity and determine the hard soil layers on the Padhang-Padhang campus of West Sulawesi University using the Cone Penetration Test (CPT) or Sondir field testing tool. The method used in this research is the literature study method and direct testing in the field. From the results of this test, the Cone Penetration Test (CPT) data from 1 test point was processed and analyzed using Microsoft Excel 2019 software. The results of data processing and analysis were the maximum cone pressure (qc) value and the maximum number of sticking resistances (JHP), namely 650 kg/cm at a depth of 12.60 meters and 2496.00 kg/cm² at a depth of 15.00 meters. The maximum friction ratio (Rf) of 0.11% is at a depth of 14.00 meters, while the maximum Sticking Resistance Pressure (fs) value of 110 kg/cm² is at a depth of 14.00 meters.