Search Results (180)

In recent years, controlling the integrity of shallow-buried natural gas pipelines within surface subsidence zones caused by high-intensity underground longwall mining in the Daniudi Gas Field of China’s Ordos Basin has emerged as a critical challenge impacting both mine planning and the safe, efficient co-exploitation of coal and deep natural gas resources. This study included field measurements and an analysis of surface subsidence data from high-intensity longwall mining operations at the Xiaobaodang No. 2 Coal Mine, revealing characteristic ground movement patterns under intensive extraction conditions. The subsidence basin was systematically divided into pipeline hazard zones using three key deformation indicators: horizontal strain, tilt, and curvature. Through ABAQUS-based 3D numerical modeling of coupled pipeline–coal seam mining systems, this research elucidated the spatiotemporal evolution of pipeline Von Mises stress under varying mining parameters, including working face advance rates, mining thicknesses, and pipeline orientation angles relative to the advance direction. The simulations further uncovered non-synchronous deformation behavior between the pipeline and its surrounding sand and soil, identifying two distinct evolutionary phases and three characteristic response patterns. Based on these findings, targeted pipeline integrity preservation measures were developed, with numerical validation demonstrating that maintaining advance rates below 10 m/d, restricting mining heights to under 2.5 m within the 260 m pre-mining influence zone, and where geotechnically feasible, the maximum stress of the pipeline laid perpendicular to the propulsion direction (90°) can be controlled below 480 MPa, and the separation amount between the pipe and the sand and soil can be controlled below 8.69 mm, which can effectively reduce the interference caused by mining. These results provide significant engineering guidance for optimizing longwall mining parameters while ensuring the structural integrity of shallow-buried pipelines in high-intensity extraction environments. Full article

Small landslides, though frequent, are often overlooked despite their significant potential impact on human-affected areas. This study presents an analysis of the Bella Orxeta landslide in Alicante, Spain, a rotational landslide event that occurred in March 2017 following intense and continued rainfall. Utilizing multitemporal datasets, including LiDAR from 2009 and 2016 and drone-based photogrammetry from 2021 and 2023, we generated high-resolution digital terrain models (DTMs) to assess morphological changes, estimate displaced volumes of approximately 3500 cubic meters, and monitor slope activity. Our analysis revealed substantial mass movement between 2016 and 2021, followed by relatively minor changes between 2021 and 2023, primarily related to fluvial erosion. This study demonstrates the effectiveness of UAV and DTM differencing techniques for landslide detection, volumetric analysis, and long-term monitoring in urbanized settings. Beyond its scientific contributions, the Bella Orxeta case offers pedagogical value across academic disciplines, supporting practical training in geomorphology, geotechnical assessment, GIS, and risk planning. It also highlights policy gaps in existing territorial risk plans, particularly regarding the integration of modern monitoring tools for small-scale but recurrent geohazards. Given climate change projections indicating more frequent high-intensity rainfall events in Mediterranean areas, the paper advocates for the systematic documentation of local landslide cases to improve hazard preparedness, urban resilience, and geoscience education. Full article

The Niland Moving Mud Spring, located near the southeastern margin of the Salton Sea, represents a rare and evolving geotechnical hazard. Unlike the typically stationary mud pots of the Salton Trough, this spring is a CO₂-driven mud spring that has migrated southwestward since 2016, at times exceeding 3 m per month, posing threats to critical infrastructure including rail lines, highways, and pipelines. Emergency mitigation efforts initiated in 2018, including decompression wells, containment berms, and route realignments, have since slowed and recently almost halted its movement and growth. This study integrates hydrochemical, temperature, stable isotope, and tritium data to propose a refined conceptual model of the Moving Mud Spring’s origin and migration. Temperature data from the Moving Mud Spring (26.5 °C to 28.3 °C) and elevated but non-geothermal total dissolved solids (~18,000 mg/L) suggest a shallow, thermally buffered groundwater source influenced by interaction with saline lacustrine sediments. Stable water isotope data follow an evaporative trajectory consistent with imported Colorado River water, while tritium concentrations (~5 TU) confirm a modern recharge source. These findings rule out deep geothermal or residual floodwater origins from the great “1906 flood”, and instead implicate more recent irrigation seepage or canal leakage as the primary water source. A key external forcing may be the 4.1 m drop in Salton Sea water level between 2003 and 2025, which has modified regional groundwater hydraulic head gradients. This recession likely enhanced lateral groundwater flow from the Moving Mud Spring area, potentially facilitating the migration of upwelling geothermal gases and contributing to spring movement. No faults or structural features reportedly align with the spring’s trajectory, and most major fault systems trend perpendicular to its movement. The hydrologically driven model proposed in this paper, linked to Salton Sea water level decline and correlated with the direction, rate, and timing of the spring’s migration, offers a new empirical explanation for the observed movement of the Niland Moving Mud Spring. Full article

Rainfall is a key triggering factor for numerous geotechnical hazards. Hence, it is necessary to investigate the degradation characteristics of rock–soil strength under different water contents. The existing macro–mesoscopic analysis methods for rock–soil strength degradation neglect the intrinsic connection between water content variations caused by external rainfall and mesoscopic mechanical mechanisms. In addition, there is a lack of discrete element method (DEM) mesoscopic parameter calibration methods for rock–soil strength under the influence of external environmental factors. Hence, this study aims to perform a macro–mesoscopic analysis and develop a parameter calibration model for the degradation of rock–soil strength under different water contents. First, the mesoscopic mechanical characteristics under different water contents are investigated by analyzing particle displacement, the bond failure rate, and the anisotropy coefficient. Interrelationships among shear strength, water content, and mesoscopic parameters are qualitatively analyzed, which indicated a macro–mesoscopic synergistic mechanism. A macro–meso-environment data set is constructed. Key mesoscopic parameters are determined using Pearson correlation (Pearson) and mutual information (MI) methods. Then, the mapping relationships are established based on ordinary least squares. The model accuracy is verified by comparing the calibrated simulation results with direct shear test results. The results show that the shear strength increases with vertical pressure under a constant water content. However, as the water content varies, the strength initially increases and then decreases. The average displacement of central particles and bond failure rate both decrease initially and then increase with rising water content, while the anisotropy coefficients show the opposite trend. Normal bond strength, tangential bond strength, and friction coefficient are determined as the key parameters. The goodness-of-fit R² of the parameter calibration model exceeds 0.92. Among 45 validation working conditions, only two are found to have errors of 12.4% and 13.6%, and the remainder have errors below 5%. Full article

Constructing a probabilistic assessment method is the primary task and key step in liquefaction research. This paper presents a systematic investigation into liquefaction potential evaluation methods. Through a comparative analysis of three conventional assessment methods, we identify critical limitations in existing approaches regarding accuracy and adaptability. A probabilistic ANN model was developed using field-collected standard penetration test (SPT) data from 311 liquefaction case histories. The model demonstrates superior performance with an overall accuracy of 86.17%, achieving 83.33% and 90.00% recognition rates for liquefied and non-liquefied cases, respectively. Key metrics, including precision (91.84%), recall (83.33%), and F1-score (87.38%), indicate robust discriminative capability. Comparative studies confirm the ANN model’s advantages over traditional methods in terms of prediction reliability and operational practicality. The research outcomes offer significant value for improving current liquefaction hazard assessment protocols in geotechnical engineering practice. Full article

Soil liquefaction poses a significant risk to foundations during earthquakes, prompting the need for simple, risk-aware screening tools that go beyond single deterministic boundaries. This study creates a probability-calibrated dual-threshold screening rule using a random forest (RF) classifier trained on 208 SPT case histories with quality-based weights (A/B/C = 1.0/0.70/0.40). The model is optimized with random search and calibrated through isotonic regression. Iso-probability contours from 1000 bootstrap samples produce paired thresholds for fines-corrected, overburden-normalized blow count N_1,60,CS and normalized cyclic stress ratio CSR_7.5,1 at target liquefaction probabilities P_liq = 5%, 20%, 50%, 80%, and 95%, with 90% confidence intervals. On an independent test set (n = 42), the calibrated model achieves AUC = 0.95, F₁ = 0.92, and a better Brier score than the uncalibrated RF. The screening rule classifies a site as susceptible when N_1,60,CS is at or below and CSR_7.5,1 is at or above the probability-specific thresholds. Designed for level ground, free field, and clean-to-silty sand sites, this tool maintains the familiarity of SPT-based charts while making risk assessment transparent and auditable for different facility importance levels. Sensitivity tests show its robustness to reasonable rescaling of quality weights. The framework offers transparent thresholds with uncertainty bands for routine preliminary assessments and to guide the need for more detailed, site-specific analyses. Full article

Geotechnical engineering faces challenges related to data, especially the ones related to dynamic soil behavior (i.e., shear modulus reduction and damping ratio curves with strain), with only a few datasets in open-access format and a slow transition to a more data-driven method. This lack of data, combined with variations in data collection methods, makes it difficult to build accurate predictive models. These challenges arose while developing a model to predict the shear modulus curves, an important soil property to better understand seismic hazard from three different databases. Combining multiple databases can sometimes degrade model performance. To address this, a novel approach in geotechnics based on Shapley values computed from an XGBoostRegressor model is introduced. This game–theoretic method quantifies each database’s marginal contribution to the model’s R² across all possible combinations, making it possible to identify which databases contribute most to improving performance. As the number of available databases continues to grow, this method will become increasingly useful. For shear modulus reduction curves, two out of three databases explored have Shapley values of 0.341 and 0.339, while the last one reaches only a value of 0.320. This suggests that the first two databases contribute more to the model’s performance. Full article

The long-term safety and operational reliability of zoned earth dams depend on the structural integrity of their internal components, including core, filters, and shell zones. This is particularly relevant for old dams which have been operational for a long period of time. Such existing infrastructure systems are exposed to various loading types over time, including environmental, seepage-related, extreme event, and climate change effects. As a result, even when they look intact externally, changes might affect their internal structure, composition, and possibly functionality. Thus, it is important to delineate a comprehensive and cost-effective strategy to identify potential issues and derive the health status of existing earth dams. This paper outlines a systematic approach for conducting a comprehensive health check of these structures through the implementation of a multi-epoch geotechnical approach based on a variety of standard measured and monitored quantities. The goal is to compare current properties with baseline data obtained during pre-, during-, and post-construction site investigation and laboratory tests. Guidance is provided on how to judge such multi-epoch comparisons, identifying potential outcomes and scenarios. The proposed approach is tested on a well-documented case study in Southern Italy, an area prone to climate change and subjected to very high seismic hazard. The case study demonstrates how the integration of historical and contemporary geotechnical data allows for the identification of critical zones requiring attention, the validation of numerical models, and the proactive formulation of targeted maintenance and rehabilitation strategies. This comprehensive, multi-epoch-based approach provides a robust and reliable assessment of dams’ health, enabling better-informed decision-making workflows and processes for asset management and risk mitigation strategies. Full article

Despite its potential danger, site amplification effects are often neglected in seismic hazard analysis. Appropriate amplification factors can be determined from shear wave velocity, but impracticality in in situ measurements leads to reliance on regional correlation with geotechnical parameters such as SPT N-value. Modified power law and logarithmic equations were derived from past correlation studies to determine V_s30 values for each borehole location in the City of Manila. V_s30 profiles were spatially interpolated using the inverse-distance weighted and thin-spline methods to approximate the variation in shear wave velocities and add more detail to the existing contour map for soil profile classification across Metro Manila. Statistical analysis of the interpolated models indicates percentage differences ranging from 0 to 10% with a normalized root mean square error of nearly 5%. Generated equations and geospatial models in the study may be used as a basis for a seismic microzonation model for Metro Manila, considering other geological and geophysical layers. Full article

Blast-induced ground vibrations present substantial safety and environmental hazards in surface mining operations. This study proposes and evaluates the Sparrow Search Algorithm-optimized ANN (SSA-ANN) against artificial neural network (ANN), Genetic Algorithm-optimized ANN (GA-ANN), and empirical formula (USBM) to estimate peak particle velocity (PPV). In addition, the input parameters include key blasting design parameters and rock mass features (GSI and UCS). The SSA-ANN demonstrated superior prediction accuracy, attaining an average R² of 0.51 using bootstrap validation, surpassing GA-ANN (0.41) and standard ANN (0.26). Furthermore, the incorporation of GSI enhanced the model’s geotechnical sensitivity. These results illustrate that the application of SSA-ANN alongside comprehensive rock mass characteristics can substantially decrease uncertainty in PPV prediction, therefore enhancing safety within the blast area and improving vibration control methods in blasting operations. 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

Earthquake hazards, such as strong ground motion, liquefaction, and landslides, pose significant threats to structures built on seismically vulnerable, loose, and saturated sandy soils. Therefore, a structural failure evaluation method that accounts for site-specific seismic responses is essential for developing effective and appropriate earthquake hazard mitigation strategies. In this study, a real-time assessment framework for structural seismic susceptibility is developed. To evaluate structural susceptibility to earthquakes, seismic fragility functions are employed as thresholds for structural failure and are linked to a geotechnical spatial grid that incorporates correlation equations for seismic load determination. The real-time assessment consists of the following procedures. First, the geotechnical spatial grid is constructed based on the geostatistical method to estimate the site-specific site response to be correlated with the earthquake hazard potential. Second, the peak ground accelerations are determined from seismic load correlation and assigned to the geotechnical spatial grid. Third, the damage grade of structure is determined by calculating the failure probabilities of defined damage levels and integrating the geotechnical spatial grids for the target structure in real time. The proposed assessment was simulated at Incheon Port, South Korea, using both an actual earthquake event (the 2017 Pohang Earthquake) and a hypothetical earthquake scenario. 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

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