Search Results (2,565)

With the continuous increase in mining activities, effective tailings management has become a critical concern in geotechnical and environmental engineering. This study systematically investigates the microstructural characteristics and 3D reconstruction behavior of copper tailings with different particle sizes using X-ray computed tomography (micro-CT), digital image processing, and 3D modeling techniques. Two particle size groups (fine: 0.075–0.15 mm; coarse: 0.15–0.3 mm) were analyzed to quantify differences in particle morphology, pore structure, and orientation anisotropy. Binary images and reconstructed models revealed that coarse particles tend to have more irregular and angular shapes, while fine particles exhibit more complex pore networks with higher fractal dimensions. The apparent porosity derived from CT data was consistently lower than laboratory measurements, likely due to internal agglomeration effects. Orientation analysis indicated that particle alignment and anisotropy vary systematically with section angle relative to the principal stress direction. These findings offer new insights into the particle-scale mechanisms affecting the packing, porosity, and anisotropy of tailings, providing a scientific basis for enhancing the structural evaluation and sustainable management of tailings storage facilities. Full article

Shallow foundations are widely used in both terrestrial and marine environments, supporting critical structures such as buildings, offshore wind turbines, subsea platforms, and infrastructure in coastal zones, including piers, seawalls, and coastal defense systems. Accurately determining the soil bearing capacity for shallow foundations presents a significant challenge, as it necessitates considerable resources in terms of materials and testing equipment, as well as a substantial amount of time to perform the necessary evaluations. Consequently, our research was designed to approximate the forecasting of soil bearing capacity for shallow foundations using machine learning algorithms. In our research, four ensemble machine learning algorithms were employed for the prediction process, benefiting from previous experimental tests. Those four models were AdaBoost, Extreme Gradient Boosting (XGBoost), Gradient Boosting Regression Trees (GBRTs), and Light Gradient Boosting Machine (LightGBM). To enhance the model’s efficacy and identify the optimal hyperparameters, grid search was conducted in conjunction with k-fold cross-validation for each model. The models were evaluated using the R² value, MAE, and RMSE. After evaluation, the R² values were between 0.817 and 0.849, where the GBRT model predicted more accurately than other models in training, testing, and combined datasets. Moreover, variable importance was analyzed to check which parameter is more important. Foundation width was the most important parameter affecting the shallow foundation bearing capacity. The findings obtained from the refined machine learning approach were compared with the well-known empirical and modern machine learning equations. In the end, the study designed a web application that helps geotechnical engineers from all over the world determine the ultimate bearing capacity of shallow foundations. Full article

Soil void ratio is a key parameter in geotechnical engineering design and geological hazard prevention. However, existing methods for determining void ratio are plagued by issues such as difficulty in sampling, susceptibility of samples to disturbance, and heavy experimental workload. The cone penetration test, with its advantages of simple operation, high survey efficiency, and high accuracy, has gradually become a commonly used in situ testing method in engineering investigations. Based on data from the Yellow River Delta, this paper evaluates the applicability of several models related to void ratio. Combined with the Robertson density prediction model, a semi-empirical model for predicting void ratio based on the piezocone penetration test (CPTu), in situ testing is proposed, which enables efficient evaluation by establishing a conversion mechanism between soil density and void ratio. Verification using a database built from six types of nearly saturated sedimentary soil data shows that underestimation of predicted density will amplify the error of soil void ratio. The prediction accuracy is significantly improved after coefficient correction. Finally, a simple model for predicting void ratio that only requires CPTu data is developed, providing a sampling-free evaluation tool for estuarine and marine sedimentary areas. Full article

Machine learning-guided predictive models are attractive in rock modeling for different scholars to obtain continuous profiles of rock compressive strength in rock engineering. The major objectives of the study are to assess the implications of machine learning (ML)-based connectionist models to obtain the unconfined compressive strength (UCS) of rock, to perform parametric sensitivity analysis on petrophysical parameters, and to develop an improved correlation for UCS prediction. The least-squares support vector machine (LSSVM) is applied to develop data-driven models for the prediction of UCS. Additionally, the random forest (RF) algorithm is applied to verify the effectiveness of predictive models. A database containing well-logging data is processed and utilized to construct connectionist models to obtain UCS. For the efficacy of predictive models, statistical performance indicators such as the coefficient of determination (CC), average percentage relative error, and maximum average percentage error are utilized in the study. It is revealed that the RF- and LSSVM-based models for predicting UCS perform excellently with high precision. Considering the parametric sensitivity analysis in the predictive models for UCS, the formation compressional wave velocity and formation gamma-ray are the most strongly contributing predictor variables rather than other input variables such as the modulus of elasticity, acoustic shear wave velocity, and rock bulk density. The improved correlation for predicting UCS shows high precision, achieving a CC of 96% and root mean squared error of 0.54 MPa. This systematic research workflow is significant and can be utilized for connectionist robust model development and variable selections in the petroleum and mining fields, such as predicting reservoir properties, the drilling rate of penetration, sanding potentiality of hydrocarbon reservoir rocks, and for the practical implications of boring and geotechnical engineering projects. Full article

Accurate and efficient rock mass characterization is essential in geotechnical engineering, yet traditional tunnel face mapping remains time consuming, subjective, and potentially hazardous. Recent advances in digital technologies and AI offer automation opportunities, but many existing solutions are hindered by slow 3D scanning, computationally intensive processing, and limited integration flexibility. This paper presents Tunnel Rapid AI Classification (TRaiC), an open-source MATLAB-based platform for rapid and automated tunnel face mapping. TRaiC integrates single-shot 360° panoramic photography, AI-powered discontinuity detection, 3D textured digital twin generation, rock mass discontinuity characterization, and Retrieval-Augmented Generation with Large Language Models (RAG-LLM) for automated geological interpretation and standardized reporting. The modular eight-stage workflow includes simplified 3D modeling, trace segmentation, 3D joint network analysis, and rock mass classification using RMR, with outputs optimized for Geo-BIM integration. Initial evaluations indicate substantial reductions in processing time and expert assessment workload. Producing a lightweight yet high-fidelity digital twin, TRaiC enables computational efficiency, transparency, and reproducibility, serving as a foundation for future AI-assisted geotechnical engineering research. Its graphical user interface and well-structured open-source code make it accessible to users ranging from beginners to advanced researchers. Full article

Seepage erosion around sheet pile walls represents a critical failure mechanism in geotechnical engineering, yet the underlying mechanisms governing the onset of erosion remain poorly understood. This study presents a comprehensive multi-scale investigation employing a coupled computational fluid dynamics (CFD)-discrete element method (DEM) to elucidate the onset mechanisms of seepage erosion in gap-graded soils with varying the fines content under different hydraulic gradients. The results demonstrate that increasing the fines content enhances the overall erosion resistance, as evidenced by reduced particle mobilization and eroded mass ratio. Particle tracking analysis reveals that the fines content fundamentally influences the spatial distribution of the erosion. Specimens with low fines content exhibit distributed erosion throughout the domain, while specimens with higher fines content show concentrated erosion around the sheet pile wall and downstream regions. Micromechanical analysis of local contact fabric and contact forces indicates that this spatial heterogeneity stems from the mechanical coordination number and mechanical redundancy, characterized by the reduced magnitudes of these parameters for the region with lower erosion resistance. These findings establish that the fines content governs both global erosion resistance and spatial erosion patterns, providing essential insights for optimizing soil gradation design and advancing fundamental understanding of seepage erosion mechanisms. Full article

Ensuring the safe landing of deep-sea mining vehicles (DSMVs) on soft seabed sediments is critical for the stability and operational reliability of subsea mineral extraction. However, deep-sea sediments, particularly in polymetallic nodule regions, are characterized by low shear strength, high compressibility, and rate-dependent behavior, posing significant challenges for full-scale experimental investigation and predictive modeling. To address these limitations, this study develops a high-fidelity finite element simulation framework based on the Coupled Eulerian–Lagrangian (CEL) method to model the landing and penetration process of full-scale DSMVs under various geotechnical conditions. To overcome the high computational cost of FEM simulations, a data-driven surrogate model using the random forest algorithm is constructed to predict the normalized penetration depth based on key soil and operational parameters. The proposed hybrid FEM–ML approach enables efficient multiparameter analysis and provides actionable insights into the complex soil–structure interactions involved in DSMV landings. This methodology offers a practical foundation for engineering design, safety assessment, and descent planning in deep-sea mining operations. Full article

This study introduces an innovative deflection-controlled design method (DCM) for evaluating the bearing capacity of offshore mono-bucket foundations (MBFs) in clay, integrating advanced numerical simulations using FLAC3D with the modified cam clay (MCC) soil model. Departing from conventional ultimate bearing capacity approaches, the proposed method prioritizes serviceability limits by constraining foundation deflections to ensure optimal structural performance and turbine efficiency. A systematic investigation revealed that the MBF performance is predominantly governed by eccentricity ratios and soil–structure interaction, with vertical loads exhibiting a minimal impact in a serviceability limit state. Key findings include the following: (1) the rotation center (RC) stabilizes at approximately 0.8 times the skirt length (L) under loading; (2) thin, deep MBFs (aspect ratio > 1.0) exhibit up to a 30% higher bearing capacity compared to wide, shallow configurations; (3) increasing eccentricity ratios (ε = 0.31–1.54) enhance the moment capacity but reduce the allowable horizontal force by 15–20%; (4) compressive vertical loads (υ = −0.30) slightly reduce the normalized bending moments (ω) by 5–10% at low eccentricities (ε < 0.5). The numerical framework was rigorously validated against centrifuge test data, demonstrating high accuracy (error < 3%) in predicting foundation behavior. By bridging geotechnical mechanics with practical engineering requirements, this study provides a robust and efficient design framework for MBFs, offering significant improvements in reliability and cost-effectiveness for offshore wind turbine applications. The proposed DCM successfully guided the design of an MBF in southeastern China, demonstrating its efficacy for use with homogeneous clay. Full article

The aim of this study is to search for and verify regression models of selected geotechnical parameters of organic soils that are useful in engineering practices. Various machine learning methodologies were employed, including decision tree, ensembles of trees, support vector regression, Gaussian process, and neural networks. The work was based on two qualitatively different examples of estimating the unit weight of soil (γ_t). In the first example, the results of cone penetration test (CPT) probing (cone resistance q_c and friction resistance f_s) were used. In the second example, the results of laboratory tests of other physical properties of these soils (content of organic parts LOI_T and moisture content w) were used. The task was completed for 135 sets of test results, which were carried out at the Rzeszów training ground in Poland with in situ tests using the CPT probe and laboratory tests. A statistical analysis was carried out to initially determine the relationships between the variables. This work presents the results of a comparison of multiple linear regression models with regression models obtained using the machine learning (ML) method. The studies obtained ML models with mean absolute percentage errors (MAPE) that were smaller than those of statistical models. Consequently, for the CPT sounding data, the MAPE changed from 13.57% to 7.37%, and, for the second data set, from 7.87% to 1.25%. Software STATISTICA version 13.3 and the Regression Learner TM library from MATLAB R2024b were used to analyze the soil data. Full article

Rare earth elements (REEs) and trace elements, due to their relative stability during sedimentary processes, are effective geochemical proxies for sediment provenance. In the Dongdaohaizi Depression of the eastern Junggar Basin, the provenance of the Middle Jurassic Sangonghe Formation remains contentious. In this study, representative sandstone samples were systematically collected from all three members of the Sangonghe Formation in both the Dongdaohaizi Depression and its western margin. Through comprehensive petrographic and geochemical analyses, we obtained the following results. The Sangonghe Formation is primarily composed of feldspathic lithic sandstones, lithic sandstones, and minor lithic–feldspathic sandstones. The heavy mineral assemblage includes zircon, garnet, chromite, and rutile, suggesting source rocks of intermediate to acidic igneous, metamorphic, and mafic lithologies. The total REE contents range from 101.84 to 192.68 μg/g, with an average of 161.80 μg/g. The ∑LREE/∑HREE ratios vary from 6.59 to 13.25 (average 10.96), and the average δEu values are close to 1. The δCe value ranges from 1.09 to 1.13 (average 1.11). Trace element discrimination diagrams, including La-Th-Sc, Th-Co-Zr/10, Th-Sc-Zr/10, and La/Y-Sc/Cr ternary plots, indicate that most samples fall within the continental island arc domain, with a few plotting in the passive continental margin field. Comparison with potential surrounding source regions reveals dual provenances: an eastern source from the Kalamaili Mountains and a western source from the Zhayier Mountains. During the Early Jurassic, these two orogenic belts acted as distinct sediment sources. The Zhayier Mountains provided stronger input, with fluvial and tidal processes transporting sediments into the basin, establishing the primary subsidence center in the west of the depression. By the Middle Jurassic, continued thrusting of surrounding fold belts caused a migration of the lake center and the main depocenter to the western edge of the Dongdaohaizi Depression, while the former depocenter gradually diminished. Furthermore, sustained erosion and denudation of the Mosowan Uplift during the Early–Middle Jurassic reduced its function as a structural barrier, thereby promoting increased mixing between eastern and western sediment sources. The study not only refines existing paleogeographic models of the Junggar Basin, but also demonstrates the utility of REE–trace geochemistry in deciphering complex provenance systems in tectonically active basins. Full article

Measuring-while-drilling (MWD) techniques have great potential for use in geotechnical engineering research. This study first addresses the current use of MWD, which consists of recording data using sensors in a drilling machine operating on site. It then addresses the currently unsolved problems of quality control in drilled piles and assessments of their interaction with the soil under load. Next, an original method of drilling displacement piles using a special EGP auger (Electro-Geo-Probe) is presented. The innovation of this new drilling system lies in the placement of the sensors inside the EGP auger in the soil. These innovative sensors form an integrated measurement system, enabling improved real-time control during pile drilling. The most original idea is the use of a Cone Penetration Test (CPT) probe that can be periodically and remotely inserted at a specific depth below the pile base being drilled. This new MWD-EGP system with cutting-edge sensors to monitor the soil’s impact on piles during drilling revolutionizes pile drilling quality control. Furthermore, implementing this in-auger sensor system is a step towards the development of digital drilling rigs, which will provide better pile quality thanks to solutions based on the results of real-time, on-site soil testing. Finally, examples of measurements taken with the new sensor-equipped auger and a preliminary interpretation of the results in non-cohesive soils are presented. The obtained data confirm the usefulness of the new drilling system for improving the quality of piles and advancing research in geotechnical engineering. Full article

Understanding the intrinsic relationship between microscopic structures and macroscopic mechanical properties of rock under freeze–thaw (F-T) conditions is essential for ensuring the safety and stability of geotechnical engineering in cold regions. In this study, a series of F-T cycle tests, nuclear magnetic resonance (NMR) measurements, and uniaxial compression tests were conducted on sandstone samples. The mechanisms by which F-T cycles influence pore structure and mechanical behavior were analyzed, revealing their internal correlation. A degradation model for peak strength was developed using mesopore porosity as the key influencing parameter. The results showed that with increasing F-T cycles, the total porosity and mesopore and macropore porosities all exhibited increasing trends, whereas the micropore and different fractal dimensions decreased. The compaction stage in the stress–strain curves became increasingly prominent with more F-T cycles. Meanwhile, the peak strength and secant modulus decreased, while the peak strain increased. When the frost heave pressure induced by water–ice phase transitions exceeded the ultimate bearing capacity of pore walls, smaller pores progressively evolved into larger ones, leading to an increase in the mesopores and macropores. Notably, mesopores and macropores demonstrated significant fractal characteristics. The transformation in pore size disrupted the power-law distribution of pore radii and reduced fractal dimensions. A strong correlation was observed between peak strength and both the mesopore and mesopore fractal dimensions. The increase in mesopores and macropores enhanced the compaction stage of the stress–strain curve. Moreover, the expansion and interconnection of mesopores under loading conditions degraded the deformation resistance and load-bearing capacity, thereby reducing both the secant modulus and peak strength. The degradation model for peak strength, developed based on changes in mesopore ratio, proved effective for evaluating the mechanical strength when subjected to different numbers of F-T cycles. Full article

Accurately characterizing the energy evolution during rock failure is crucial in understanding instability mechanisms and enabling the real-time monitoring and early warning of geological hazards in mining and geotechnical engineering. However, the energy evolution characteristics and correlations of multi-physics signals like acoustic emission (AE) and infrared radiation (IR) require further investigation. This study specifically investigated the energy evolution of AE and IR and their correlation during the uniaxial compression failure process of phosphate rock. Tests were performed on specimens under different loading rates to analyze energy dissipation and damage progression. Based on damage mechanics theory, damage evolution models were developed to describe the relationship between the cumulative AE energy, IR radiation variations (specifically the change in the average infrared radiation temperature, ΔAIRT), and strain under varying loading conditions. The results indicate that the loading rate significantly influences the energy release mechanism, with higher rates intensifying rock damage. The peak AE energy rate coincides with the inflection point of the cumulative energy curve, marking substantial internal energy release at failure. Additionally, as the loading rate increases, high-temperature regions in IR thermograms appear earlier, while the variation in ΔAIRT follows a decreasing trend. From an energy perspective, the correlation between AE ringing counts and the average IR temperature was analyzed at both the precursor and failure stages, revealing a strong relationship between AE activity and thermal energy dissipation. Furthermore, mathematical expressions for rock damage variables and coupled relationship equations were derived and validated using experimental data, yielding correlation coefficients (R²) exceeding 0.92. These findings provide a theoretical and methodological foundation for the development of enhanced real-time rock monitoring and early warning systems, contributing to improved safety in geological and mining engineering. Full article

Synchronous grouting slurry is widely used in shield tunnel construction to fill the gaps between stratum and shield tail segments. However, as grout is nearly liquid in the initial stages, the tunnel lining segments recently separated from the shield tail are easily affected by the upward buoyancy generated by grout, causing issues such as longitudinal misalignment and opening of ring joints. Therefore, studying the upward buoyancy characteristics of synchronous grout is crucial. In this study, floating characterisation parameters of grout were investigated using buoyancy model tests, orthogonal tests, and comprehensive tests. The floating characterisation parameters are affected by distribution ratio and types of each grout component. The relationship between the floating characterisation parameters of grout and buoyancy was established. The results show that density, flow index, and shear strength can be used as the floating characterisation parameters. Binder–sand and water–binder ratios have the largest impact on the density. The bentonite–water ratio exerts a primary influence on the flow index, while the water–binder ratio contributes a secondary effect. In addition, bentonite–water and binder–sand ratios have the greatest effect on the shear strength. Furthermore, the particle size of sand and type of bentonite considerably influence the flow index and shear strength. A high-shear grout using well-graded fine sand and a high mesh of sodium bentonite was considered in this study. When the content of bentonite exceeds 7% (P2.2), Archimedes’ law is not applicable for calculating the upward buoyancy of grout. Buoyancy supply rate exhibits gradual enhancement with flow index elevation, yet with diminishing growth rates. Full article