Engineering Geology最新文献

{"title":"Durability of an innovative self-healing geopolymer vertical barrier under dry-wet cycles","authors":"Qin-Pei Xue ,&nbsp;Hong-Xin Chen ,&nbsp;Shi-Jin Feng ,&nbsp;Fu-Sheng Zha ,&nbsp;Xiao-Lei Zhang","doi":"10.1016/j.enggeo.2025.108155","DOIUrl":"10.1016/j.enggeo.2025.108155","url":null,"abstract":"<div><div>Microcapsules have great potential in developing more durable and reliable cutoff wall materials. This study produced two types of microcapsules, including single-walled and double-walled, using sodium silicate as the core material, which demonstrated advantageous chemical structures, thermal stability, and rheological properties. An innovative self-healing geopolymer cutoff wall backfill (SHGCWB) has been synthesized based on this. The evolution of durability was evaluated macroscopically through dry-wet cycle test. The self-healing effect and durability enhancement mechanism of microcapsules were revealed microscopically by MIP and SEM-EDS tests. The hydraulic conductivity of SHGCWB can remain below 1E-8 m/s before the 4th dry-wet cycle, and fluctuate around 1E-8 m/s in the subsequent cycles. The true determinant of permeability is the proportion of micropore (&lt; 0.05 μm), mesopore (0.05–0.1 μm), and macropore (&gt; 0.1 μm). One can promptly estimate the hydraulic conductivity of SHGCWB based on ultrasonic pulse velocity (UPV) and the first ultrasonic pulse amplitude (UPA). This study can provide a new technical solution to further improving the long-term durability and serviceability of cutoff wall.</div></div>","PeriodicalId":11567,"journal":{"name":"Engineering Geology","volume":"353 ","pages":"Article 108155"},"PeriodicalIF":6.9,"publicationDate":"2025-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144138550","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fungus-induced sand stabilization: Strength and erosion resistance properties","authors":"Leyu Gou ,&nbsp;Xianwei Zhang ,&nbsp;Haodong Gao ,&nbsp;Gang Wang ,&nbsp;Lei Yan ,&nbsp;Hualiang Zhu","doi":"10.1016/j.enggeo.2025.108156","DOIUrl":"10.1016/j.enggeo.2025.108156","url":null,"abstract":"<div><div>Climate change increases the frequency of extreme weather events, intensifying shallow flow-type landslides, soil erosion in mountainous regions, and slope failures in coastal areas. Vegetation and biopolymers are explored for ecological slope protection; however, these approaches often face limitations such as extended growth cycles and inconsistent reinforcement. This study investigates the potential of filamentous fungi and wheat bran for stabilizing loose sand. Triaxial shear tests, disintegration tests, and leachate analyses are conducted to evaluate the mechanical performance, durability, and environmental safety of fungus-treated sand. Results show that the mycelium enhances soil strength, reduces deformation, and lowers excess pore water pressure, with a more pronounced effect under undrained than drained conditions. Mycelium adheres to particle surfaces, forming a durable bond that increases cohesion and shifts the slope of the critical state line, significantly enhancing the mechanical stability of fungus-treated sand. The resulting strength parameters are comparable to those of soils reinforced with plant roots. Fungus-treated sand remains stable after 14 days of water immersion following triaxial shear tests, with no environmental risk from leachate. These findings demonstrated that fungal mycelium provides an effective and eco-friendly solution for stabilizing loose sand, mitigating shallow landslides, and reinforcing coastlines.</div></div>","PeriodicalId":11567,"journal":{"name":"Engineering Geology","volume":"354 ","pages":"Article 108156"},"PeriodicalIF":6.9,"publicationDate":"2025-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144168356","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Interpretable co-seismic landslide prediction: Unveiling the potential of multidirectional peak ground acceleration","authors":"Binghai Gao ,&nbsp;Yi Wang ,&nbsp;Xiaolong Zhang ,&nbsp;Zhice Fang","doi":"10.1016/j.enggeo.2025.108153","DOIUrl":"10.1016/j.enggeo.2025.108153","url":null,"abstract":"<div><div>Current co-seismic landslide evaluations predominantly employ machine learning methods, with peak ground acceleration (PGA) serving as the primary covariate for assessing landslide impacts. However, existing research often overlooks the effects of vertical ground motions, focusing solely on horizontal PGA, which does not reflect real-world conditions. To address this gap, we utilize actual ground shaking data to calculate a more comprehensive set of multi-directional PGA parameters and explore various combinations of these directional PGAs. To investigate their impact on co-seismic landslides, we employ a generalized additive model that captures the complex relationships between environmental factors and landslide occurrence. This model not only incorporates different directional PGAs but also considers their interactions to elucidate their effects on landslide risk. A robust suite of methods is employed to validate the model's goodness-of-fit and the interpretability of covariate effects. Our experimental results demonstrate that integrating multi-directional and interactive PGA parameters significantly enhances prediction accuracy for co-seismic landslides, with results remaining interpretable. Furthermore, we examine the generalizability of this approach across multiple machine learning methods, with consistent validation outcomes across different models. This underscores the necessity of comprehensively considering multi-directional PGA parameters and their interactions in practical co-seismic landslide predictions.</div></div>","PeriodicalId":11567,"journal":{"name":"Engineering Geology","volume":"353 ","pages":"Article 108153"},"PeriodicalIF":6.9,"publicationDate":"2025-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144147011","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Scale-dependent recursive analysis of topographical roughness: A methodology for differentiating geological and geomechanical features from point cloud data","authors":"Niloufarsadat Sadeghi,&nbsp;Jonathan D. Aubertin","doi":"10.1016/j.enggeo.2025.108152","DOIUrl":"10.1016/j.enggeo.2025.108152","url":null,"abstract":"<div><div>Exposed rock surfaces reflect diverse topographical features shaped by underlying geological and geomechanical conditions, such as mineral composition, weathering, excavation methods, and structural geology. These features directly influence the mechanical behavior of in-place materials, providing a robust basis for differentiating geological and geomechanical units in engineering. Their explicit spatial differentiation relies on time-consuming and subjective visual assessments, or the inefficient and difficult to reproduce measurement of topographical features (e.g., roughness, undulation) at arbitrary scales. This work aims to offer an objective, reproducible, and efficient topographical analysis framework to differentiate geological and geomechanical features arising from natural and man-made origins. This study introduces a scale-dependent recursive analysis method to systematically evaluate and characterize roughness conditions of exposed rock surfaces. By analyzing point clouds across multiple scales, the method derives scale-dependent trends and computes parameters that distinguish topographical features associated with specific geological and operational settings. A moving-window algorithm is applied as a second layer of analysis to capture localized trends, integrating these as an explicit scalar field within point clouds for direct differentiation of features. This methodology improves accuracy and efficiency compared to traditional roughness measurement techniques by reducing biases and subjectivity associated with visual-based assessments. The approach is demonstrated using four datasets from diverse geological and geomechanical contexts, showcasing its applicability and the insights gained. The influence of point cloud density and moving-window size on the recursive analysis is further discussed, highlighting the method's potential to provide objective and quantifiable topographical differentiation for mining, tunneling, and construction applications.</div></div>","PeriodicalId":11567,"journal":{"name":"Engineering Geology","volume":"354 ","pages":"Article 108152"},"PeriodicalIF":6.9,"publicationDate":"2025-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144239346","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Sparse data-driven knowledge discovery for interpretable prediction of permeability in tight sandstones","authors":"Lulu Xu ,&nbsp;Zhengyang Du ,&nbsp;Meifeng Cai ,&nbsp;Shangxian Yin ,&nbsp;Shuning Dong ,&nbsp;Hung Vo Thanh ,&nbsp;Kenneth C. Carroll ,&nbsp;Mohamad Reza Soltanian ,&nbsp;Zhenxue Dai","doi":"10.1016/j.enggeo.2025.108151","DOIUrl":"10.1016/j.enggeo.2025.108151","url":null,"abstract":"<div><div>Permeability (<em>k</em>) is crucial for subsurface fluid flow, but predicting <em>k</em>-values in tight sandstones remains challenging due to their complex pore structure and heterogeneity. Although machine learning (ML) has shown promise, it faces significant challenges, including limited high-quality data, high computational costs, and unclear prediction mechanisms. This study proposes a sparse data-driven knowledge discovery framework aimed at enhancing the accuracy and interpretability of <em>k</em>-value predictions in tight sandstone formations. We integrate ML models with data augmentation (ML-DA), using Extreme Gradient Boosting (XGBoost-DA) and Least Squares Support Vector Regression (LSSVR-DA), optimized through genetic algorithms (GA), particle swarm optimization (PSO), and Bayesian optimization (BO). SHapley Additive Explanations (SHAP) are employed to elucidate the interactions between key factors influencing predictions. Monte Carlo simulations demonstrate the robust performance of our ML-DA models, even under data constraints. SHAP analysis identifies key predictors, including porosity, displacement pressure, median pore throat radius, median pressure, and carbonate content. Partial dependence plots (PDPs) reveal a significant interaction between porosity and carbonate content, as well as a decrease in model stability at low carbonate content. This study presents an interpretable ML framework with data augmentation, enabling improved predictions from sparse data while exploring the interactions between key factors. The framework can be adapted to other domains facing similar challenges, enhancing the accuracy and transparency of model predictions.</div></div>","PeriodicalId":11567,"journal":{"name":"Engineering Geology","volume":"353 ","pages":"Article 108151"},"PeriodicalIF":6.9,"publicationDate":"2025-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144138549","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessment of water inrush hazards in tunnels using the inversion method of full-decay induced polarization with physical law constraints","authors":"Lichao Nie ,&nbsp;Shixun Jia ,&nbsp;Wei Zhou ,&nbsp;Zhiqiang Li ,&nbsp;Pengyu Jing ,&nbsp;Shuo Zhang","doi":"10.1016/j.enggeo.2025.108115","DOIUrl":"10.1016/j.enggeo.2025.108115","url":null,"abstract":"<div><div>Water hazards pose significant risks in complex geological conditions encountered during tunnel construction. Therefore, it is necessary to assess and detect water hazards in tunnels with greater precision. This paper proposes an inversion method of full-decay induced polarization (FDIP) with physical law constraints for assessing water inrush hazards. This method innovatively adds physical law constraints to the conventional FDIP inversion method based on equivalent resistivity. The physical law constraints comprise time-smoothing, the Weibull growth model, and spatial smoothing three constraints. Initially, the study presents two constraints for calculating equivalent resistivity, time-smoothing, and Weibull growth model constraints, to ensure smooth and monotonical resistivity changes over time. Additionally, the study incorporates a spatial smoothing constraint when deriving FDIP multi-parameters which guarantees gradual transitions of subsurface properties to avoid abrupt changes. The efficacy of the inversion method of FDIP with physical law constraints was confirmed by numerical simulations and a water diverting tunnel in Southwest China.</div></div>","PeriodicalId":11567,"journal":{"name":"Engineering Geology","volume":"354 ","pages":"Article 108115"},"PeriodicalIF":6.9,"publicationDate":"2025-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144177954","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fracture image classification study of clay hydraulic fracturing based on non-destructive testing and machine learning methods","authors":"Jia-He Zhang ,&nbsp;Shi-Jin Feng ,&nbsp;Qi-Teng Zheng ,&nbsp;Xiao-Lei Zhang","doi":"10.1016/j.enggeo.2025.108149","DOIUrl":"10.1016/j.enggeo.2025.108149","url":null,"abstract":"<div><div>High-frequency ground penetrating radar (GPR) offers a precise and non-destructive method for assessing the distribution of internal soil fractures. This research develops a non-destructive fracture GPR testing platform for low-permeability contaminated soil to acquire GPR B-scan images under authentic environmental conditions. The reliable dataset of soil GPR image is collected. Furthermore, an Improved ResNet50 Version 3 (IRV3) network, featuring embedded self-attention modules and an enhanced bottleneck design, is presented and applied to real hydraulic fracturing laboratory testing using GPR. Comparisons of GPR images before and after fracturing revealed significant alterations in fracture distribution. Under the complex conditions of fracturing, the IRV3 network achieved a classification accuracy of 86.3 %. These results validate the reliability of the GPR testing platform constructed for simulating soil internal fractures and demonstrate the IRV3 network's applicability in experimental fracturing scenarios.</div></div>","PeriodicalId":11567,"journal":{"name":"Engineering Geology","volume":"354 ","pages":"Article 108149"},"PeriodicalIF":6.9,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144168609","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Shaking table tests on the effect of transverse rib thickness on the stability of geosynthetic-reinforced soil walls","authors":"Wei-xiang Zeng ,&nbsp;Fei-yu Liu ,&nbsp;Meng-jie Ying ,&nbsp;Shi-xun Zhang ,&nbsp;Chen-bo Gao","doi":"10.1016/j.enggeo.2025.108135","DOIUrl":"10.1016/j.enggeo.2025.108135","url":null,"abstract":"<div><div>Geosynthetic-reinforced soil (GRS) structures are extensively employed in infrastructure for mitigating geological disasters and facilitating restoration. Despite their widespread use, the seismic design of GRS structures requires further refinement. This study investigates the potential of three-dimensional modifications to geosynthetics for enhancing geosynthetic–soil interaction strength, extending shear bands range and reducing reinforcement length, backfill volume, and construction costs. Shaking table tests were conducted to evaluate the dynamic response of planar and stereoscopic geogrid-reinforced soil retaining walls. Using the Hilbert-Huang transform, time–frequency domain analysis examined the effects of varying transverse rib thickness, reinforcement spacing, and reinforcement length on acceleration response, panel displacement, settlement, geogrid strain, and spectrum amplitude evolution. The results reveal that stereoscopic geogrids with thickened transverse ribs improve the dynamic stability of GRS walls. Both wall types exhibited a combination failure mode involving arc-shaped and sliding failure mechanisms under increased spacing or reduced reinforcement length, but transverse rib thickness had a particularly significant effect on structural performance and deformation behavior. The failure surface in the backfill was visually observed using marked sand tracking, which showed that thickened transverse ribs reduced the dislocation height difference of soil markers by 60 %. In contrast, planar geogrids experienced a 39.8 % increase in average strain increment, greater horizontal panel displacement, and more pronounced sliding failure. During vibration, stereoscopic geogrid reinforcement reduced the increase in high-frequency amplitude propagation from the base to the top of the model, lowering it from 83.0 % to 16.1 %. These findings provide valuable insights for optimizing seismic design parameters in GRS walls, contributing to improved dynamic stability and cost efficiency.</div></div>","PeriodicalId":11567,"journal":{"name":"Engineering Geology","volume":"353 ","pages":"Article 108135"},"PeriodicalIF":6.9,"publicationDate":"2025-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144116187","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Quantitative risk assessment of road posed by levee slope failure: A novel framework integrating Monte Carlo simulation and material point method","authors":"Yanhao Zheng ,&nbsp;Junru Li ,&nbsp;Jinhui Li ,&nbsp;Yintang Wang","doi":"10.1016/j.enggeo.2025.108148","DOIUrl":"10.1016/j.enggeo.2025.108148","url":null,"abstract":"<div><div>The frequent occurrence of extreme rainfall events often triggers levee slope failure (LSF), which, due to the “levee effect”, significantly damages the roads behind the levee. This paper presents a novel framework for the quantitative risk assessment of roads posed by LSF. Within the framework, the innovative integration of Monte Carlo simulation (MCS) and Material point method (MPM) provides a unique solution for simulating the complicated dynamic relationship between LSF and road destruction. MCS generates precise failure scenarios for MPM simulations, overcoming the limitations of traditional approaches in addressing uncertainty in complex scenario systems. With its technical superiority in capturing post-failure deformations, MPM offers critical insights for assessing road exposure and vulnerability. The framework also accounts for indirect losses from road disruptions, which have long been overlooked. The application of the framework to the risk assessment of the road behind the Shijiao Levee in the Pearl River Basin fully demonstrates its practicality and robustness. Compared to traditional risk assessment methods, the proposed framework provides a more refined dynamic evaluation, facilitating the formulation of more effective disaster mitigation strategies.</div></div>","PeriodicalId":11567,"journal":{"name":"Engineering Geology","volume":"353 ","pages":"Article 108148"},"PeriodicalIF":6.9,"publicationDate":"2025-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144130784","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A GLPI framework for gravelly soil liquefaction hazard assessment based on fuzzy mathematics","authors":"Jing Wang ,&nbsp;Jilei Hu","doi":"10.1016/j.enggeo.2025.108134","DOIUrl":"10.1016/j.enggeo.2025.108134","url":null,"abstract":"<div><div>The Liquefaction Potential Index (LPI) is commonly used to evaluate liquefaction hazards in sandy soils, but is not applicable to gravelly soils. In addition, the choice of liquefaction prediction methods and the form of depth weighting functions may greatly affect the result of the LPI calculation. Meanwhile, the quartile thresholding method, for classifying the LPI values into hazard levels, suffers from boundary uncertainty, which can also affect the disaster assessment accuracy. Therefore, this study investigates the effects of different liquefaction prediction methods (simplified procedural method and its improvements, Bayesian network models) and different depth weighting functions (linear, logarithmic, exponential, hyperbolic, and power functions) on LPI results and disaster prediction accuracy. In addition, a new liquefaction potential index for gravelly soil (GLPI) evaluation is proposed, and the fuzzy mathematical method is used to handle the boundary uncertainty. The results show that the proposed GLPI hazard assessment method improves the prediction accuracy by 55 % over the LPI model. For the GLPI computational framework, the use of the Bayesian network method instead of the simplified procedure for liquefaction prediction was the most effective method for improving the accuracy of hazard assessment, with an improvement of 33 %; the depth weighting function has a relatively smaller effect, and the hyperbolic function has an improvement of 8 % compared with the linear function. In the hazard assessment based on GLPI values, fuzzy mathematics improved by 9 % over the quartile threshold method. Finally, the effectiveness of GLPI method was validated in new historical liquefied sites.</div></div>","PeriodicalId":11567,"journal":{"name":"Engineering Geology","volume":"353 ","pages":"Article 108134"},"PeriodicalIF":6.9,"publicationDate":"2025-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144099766","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}