Bulletin of Engineering Geology and the Environment最新文献

The exact contribution of arbor roots to the shear strength of root-soil composites at different soil depths requires further investigation. Taking Malus halliana Koehne as the research object, the root distribution along the soil depth was obtained by the longitudinal profile method. A total of 384 samples of undisturbed root-soil composite were obtained from two profiles (0.5 m profile and 1.0 m profile) by using a self-fabricated ring knife. The large-box direct shear tests were carried out to investigate the depth-dependent variations in shear strength and its parameters (cohesion and internal friction angle) of the undisturbed root-soil composites. The results showed that the shear strength and cohesion of the undisturbed root-soil composite increases first and then decreases with the increase of soil depth, which is consistent with the results of root investigation. The soil shear strength increases by 0.3% ~ 20.7% and the cohesion increases by 4.2% ~ 32.7% with the presence of Malus halliana Koehne roots. The roots play a dominant role in strengthening the shear strength of the undisturbed root-soil composite in the depth range of 0–0.4 m. The shear strength values of the two profiles are intertwined after the comprehensive action of multiple factors in the depth range of 0.4–0.8 m. The soil depth of 0.3–0.4 m with the largest number of roots and the richest species of root diameters shows the optimal shear resistance. The research results have important theoretical insights to reveal the interaction between arbor roots and undisturbed soil under different soil depths.

The widespread presence of dispersive soils in northern Shaanxi, China, has caused significant erosion and numerous dam failures. This study uses a rheological approach to investigate the mechanisms behind these issues. The effect of water content, Na⁺, and Ca²⁺ on the structural stability and rheological properties of dispersive soils was examined through various tests, including steady-state shear, oscillatory amplitude shear, particle size analysis, and zeta potential measurements. Results indicate that higher clay content increases soil shear strength but reduces fluidity. With abundant clay particles and Na⁺ ions, the soil's fluidity at low water content is similar to that of Ca²⁺-modified soil. However, as water content exceeds 30%, shear resistance drops significantly, making the soil more prone to flow. At low shear rates/strains, sand and silt particles provide shear resistance, while clay particles dominate at high shear rates/strains. Na⁺ and Ca²⁺ affect the shear properties by altering the diffuse double layer's thickness and cohesive forces under high water content conditions. The modifying effect of Ca²⁺ is significant only for soils with a clay content of ≥ 10%. A prediction model combining dispersion values with steady-state shear properties was developed to predict the rheological properties of dispersive soils in their plastic stages. To improve structural stability, dam design and maintenance strategies should be optimized based on the clay content and ionic characteristics of local soils.

To address the issue of repetitive production system deployment in irregular mining areas under longwall mining, an innovative concept for gob-side entry retaining in retracement channel is proposed, along with an in-depth investigation of its retaining mechanism. A roof mechanical model was developed to analyze deformation and failure mechanisms in the retracement channel. Utilizing energy conservation theory, roof deformation under varying fracture positions of the main roof was calculated, revealing that roof deformation was minimized when the main roof fractured above the interface between the retracement channel and the gob. Based on these findings, a control technology for retracement channel retention was developed and validated through FLAC^3D numerical simulation. This research has formulated a novel method for gob-side entry retaining in retracement channel that transcends the conventional “roadway excavation-equipment transfer-roadway abandonment” model, achieving integrated mining-retreating-retaining process in fully mechanized coal faces. Field engineering test was finally conducted at the 1705 working face in Xinyi coal mine. Monitoring results indicated that during the retreating-retaining phase, the deformation of the retracement channel remained within controllable limits, validating the successful application of this method. The method not only effectively resolves the technical challenge associated with gob-side entry retaining in retracement channel, but also provides practical guidance for mining coal in similar irregular mining areas.

The shear deformation of rock discontinuities plays a crucial role in the stability analysis of engineering rock masses. The damage constitutive model combining statistical damage theory with damage distribution is a prevalent method for studying the shear deformation of geologic materials. However, conventional models often rely on empirically selected damage distribution, neglecting the intrinsic uncertainty associated with the distribution function. To solve this problem, a generalized distribution function based on maximum entropy theory is established. Following this, a novel shear constitutive model of rock discontinuities is proposed. Many experimental data, encompassing laboratory and in situ tests, validate its credibility. Comparative analyses with four classical models further demonstrate the model’s robustness and adaptability. Finally, the engineering application of the model and its applicability to the shear deformation of the rock-concrete interface are discussed. The findings indicate that the proposed model exhibits professional competence in simulating the shear deformation of rock discontinuities, notably characterizing the deformation traits across each stage. It displays superior accuracy against comparable models, as substantiated by superior R², RMSE, and AAREP values. This paper introduces a novel approach utilizing maximum entropy theory to investigate the shear deformation of rock discontinuities, offering geoengineers a strengthened tool for assessing the shear behavior of such discontinuities.

Karst-related hazards threaten the stability of transportation infrastructure. Single geophysical methods are often limited by interpretive non-uniqueness. This work developed an integrated exploration framework that combines electrical resistivity tomography (ERT), multi-channel analysis of surface waves (MASW), borehole drilling, and water pressure tests to evaluate the adverse geology and grouting quality underlying roadbeds in karst regions, as applied to a section of the Weilai High-Speed Railway. Results show that while MASW offered limited resolution, ERT provided higher accuracy yet remains ambiguous in distinguishing lithological variations from karst cavities. A synergistic strategy (anchored by ERT, supplemented by MASW, and validated by drilling) was proposed, significantly improving detection reliability and clarifying the distribution of adverse geology. Grouting effectiveness was assessed through an integrated qualitative and quantitative evaluation combining geophysical data, rock quality designation, and permeability, confirming that grouting enhances both rock mass integrity and impermeability. This work established a practical “detection–evaluation” technical workflow for roadbed engineering in karst environments.

Rock mass classification systems are fundamental to the design of rock structures and are widely applied in rock engineering. Among these systems, the geological strength index (GSI) is particularly significant as a commonly used classification method. GSI comprises four primary parameters: the uniaxial compressive strength of intact rock (σ_ci), discontinuity spacing (S_d), rock quality designation (RQD), and the condition of discontinuities (C_d). Notably, RQD is inherently linked to discontinuity spacing, with both parameters collectively accounting for approximately 42% of the GSI ratings. Evaluating S_d and RQD through conventional methods is challenging due to the variation in measured S_d and RQD across different scanline orientations and the lack of a standardized criterion for their determination. Rock masses consist of heterogeneous elements of various sizes and shapes, interlocking like a three-dimensional puzzle, which complicates the assessment of block size distribution. Image analysis techniques offer an effective solution to capture the roles of both S_d and RQD within the GSI framework. In this study, the parameters σ_ci, RQD, S_d, C_d, and the median block size (F₅₀) were measured across 96 scan lines in various mines, trenches, and tunnels. Analysis of the results revealed a good correlation between GSI and the combination of the discontinuity condition rating (R_cd) and F₅₀. Consequently, a new relation was developed, linking GSI to these parameters, thereby simplifying the GSI assessment process.

Sulfide-rich soils (also called potential acid sulfate soil), with their sulfur content, and potential to generate acidity, pose challenges for construction due to low bearing capacity, shear strength, and their acidification potential. This study compares the effectiveness of Portland cement and Multicem binders in stabilizing these soils by analyzing unconfined compressive strength (UCS), porosity, pH changes, and strain behavior. Four soil samples with varying state of oxidation, pH, sulfur content, and water content were treated with binders at different dosages and analyzed after 28 days of curing. The results demonstrate that binder type and dosage, initial pH, and soil composition together significantly influence stabilization performance. Portland cement achieved higher UCS values across all samples, particularly in soils with low pH, due to its high alkalinity and facilitation of hydration reactions. In contrast, Multicem achieved acceptable UCS in soils with moderate initial pH, as its slower hydration limited its effectiveness in highly acidic conditions. Porosity reduction in stabilized samples correlated with increased binder content. Strain behavior exhibited an inverse relationship with UCS. Higher UCS samples showed reduced strain, indicating increased stiffness. However, in oxidized and highly acidic sulfate soils, such as Sunderbyn, UCS gains were limited, suggesting that extreme pH conditions inhibit binder effectiveness. The study offers practical guidance for optimizing binder selection and application strategies, especially for the treatment of acid sulfate-rich soils in geotechnical engineering. Future research should prioritize the assessment of durability, stability and performance under varied environmental stresses to ensure sustainable solutions.

This study investigates the durability of Urfa stone, which has shaped the architectural heritage of Şanlıurfa, Türkiye, including its iconic use at Göbeklitepe. Although widely used in historic and contemporary structures, its long-term performance under varying environmental conditions remains unclear. To investigate this uncertainty, an extensive experimental campaign was conducted on more than one hundred specimens. Petrographic and geochemical results indicate that Urfa stone is a biomicritic limestone composed mainly of calcite. The physico-mechanical tests show that the stone’s compressive strength (16.9 MPa dry and 10.6 MPa saturated), together with its moderate sonic velocity (3418 m per s), reflects the characteristics of a relatively low-strength limestone with a moderately cemented micritic matrix. In addition, its elevated porosity (19.9%) and high capillary water absorption (3.9 kg per m²√h) facilitate rapid moisture uptake, suggesting that the stone is highly susceptible to salt crystallization. This response aligns with its moderate resistance to freeze-thaw cycling and its pronounced vulnerability to salt weathering. The compressive strength decreases of 21% after 50 freeze–thaw cycles and 42.7% after 25 Na₂SO₄ cycles support this pattern and indicate that salt-related damage is the primary driver of mechanical weakening. Analyses further demonstrate that the visible wavy patterns on the stone surface intensify through salt accumulation, oxidation, and mineral precipitation, eventually developing into structural failure zones. By assessing the durability of Urfa stone, this study provides practical guidance for predicting its long-term performance and supporting its sustainable use across different architectural contexts.

There is a substantial gap in global research concerning the temporal prediction of landslides. Advances in machine learning within the geosciences present an opportunity for disaster-prone countries, such as the Philippines, to leverage limited resources in developing effective landslide prediction models. In this study, we present a landslide prediction system for a mountain highway in Benguet, Philippines, that relies on a minimal dataset consisting of rainfall measurements and documented landslide occurrences. To enhance the accuracy of the prediction models, a spatial component was incorporated by subdividing the landslide inventory into datasets based on three distinct lithologic domains. These datasets were balanced and input into five machine learning algorithms: Gaussian Naïve Bayes (GNB), Logistic Regression (LR), Support Vector Machine (SVM), Decision Tree (DT), and Random Forest (RF). Among these, the RF models demonstrated the highest performance, with the best model achieving an Area Under the Receiver Operating Characteristic Curve (AUROC) of 82.7%, a True Positive Rate (TPR) of 85.4%, and a True Negative Rate (TNR) of 80%. Although all RF models are reliable (AUROC ≥ 0.7, TPR ≥ 0.7, and TNR ≥ 0.7), certain models trained using lithologically- constrained datasets show better performance than those trained on the unconstrained dataset. This finding suggests that grouping landslide events based on lithologic domains prior to model development can enhance temporal landslide prediction models. This approach can be incorporated into early warning systems, as it enables spatially-informed temporal prediction of landslides.

Addressing the challenges of difficult deformation signal detection and low spatial positioning accuracy in identifying potential landslide hazards in mountainous regions, this paper proposes a multi-tiered and multi-model collaborative identification approach. Firstly, surface deformation time-series data were acquired using SBAS-InSAR technology. Anomalous deformation areas are identified through spatial autocorrelation analysis (Local Moran’s I), and these deformation data are spatially constrained by integrating susceptibility evaluation results generated from a Random Forest model and the Information Value method. This enabled preliminary screening of potential hazard zones via kernel density estimation. Subsequently, a U-Net model was employed to perform semantic segmentation on Sentinel-2 imagery. A secondary identification model, constructed based on geomechanical evolutionary principles, further refines the initial screening results to ultimately determine the distribution of potential landslide areas.Field validation results demonstrate that the collaborative multi-model identification outcomes are highly consistent with GNSS monitoring data in terms of deformation trends, maintaining an error confidence interval within 10 mm. The identified potential landslides show high spatial correlations: an 84.6% spatial concordance with Quaternary cover layers, an 81.8% spatial concordance with dip slope structures, and a strong alignment with historical landslide distributions. Furthermore, the U-Net model achieved an AUC of 0.88 on the test set and an AUC of 0.81 on the validation set. This study effectively enhances the accuracy and reliability of landslide hazard identification by synergizing three modules—InSAR time-series analysis, machine learning classification, and deep learning segmentation—thereby providing a novel technical pathway for landslide disaster prevention in mountainous regions.