Bulletin of Engineering Geology and the Environment最新文献

Water evaporation is a major process of energy and material exchange between soil and atmosphere, which is a direct cause of numerous geotechnical and environmental engineering issues. In this work, after the energy supply and water vapor transfer conditions in the evaporation process being analyzed, the characteristics that the evaporation surface constantly moves down during the evaporation process were considered, a modified model for predicting soil water evaporation rate was established by introducing the relative humidity at the evaporation surface and soil heat flux. Based on these, a large-scale environmental chamber evaporation test system was developed. The evaporation tests were conducted on aeolian sands under constant groundwater level and given atmospheric conditions. Experimental results show that the sensible heat flux within ± 50 mm of the soil surface is up to about 20 times greater than that outside this area, indicating that the exchanges of heat and moisture during the evaporation process are strongly concentrated in the region within a range of ± 50 mm from the soil surface in the environmental chamber. As evaporation progresses, position of the lowest temperature in soil moves downward from the soil surface, confirming the downward movement of the evaporation surface. Moreover, the influence of soil thermal flux on moisture evaporation transforms from a promoting to a suppressing effect. Compared to that in the existing models, the changes in energy and moisture supply during the evaporation process could be more comprehensive reproduced in the proposed model. Accordingly, the predicted results of the modified model significantly agreed with the experimental data.

The roughness of rock discontinuities is a crucial parameter that influences mechanical properties. Many scholars have proposed different roughness parameters from both 2D and 3D perspectives. 3D roughness parameters capture more detailed discontinuity surface undulations, while 2D parameters remain popular due to their easier measurement and simpler calculations. However, systematic investigations into the correlations between 2D and 3D roughness parameters using the same evaluation method are notably lacking. In this study, three natural discontinuities were selected, and their point cloud data were obtained. The roughness of each discontinuity was evaluated based on three sets of parameters (({Z^{prime}_2}) and ({Z^{prime}_{23{text{D}}}}), (theta _{{max}}^{*}/{(C+1)_{2{text{D}}}}) and (theta _{{max}}^{*}/{(C+1)_{3{text{D}}}}), PAP_2D and PAP_3D), respectively, and the correlations of each set of parameters were analyzed. Additionally, the roughness evaluation effects of them were further studied and compared with the JRC values via direct shear tests of artificial discontinuities. The results show that: (1) Certain deviations exist between the evaluation results of 2D parameters based on local profiles and those of their corresponding 3D counterparts. (2) There is a linear correlation between the 2D parameter evaluation results using uniform profiles and the corresponding 3D parameter evaluation results, and the correlation increases with reduced profile. (3) When both the profile spacing and the point cloud point spacing reach 0.3 mm, uniform profile-based 2D roughness evaluations match 3D counterpart analyses.

Physically-based model is an important method for refined assessment of landslide hazard at large scale. The traditional infinite slope model homogenizes the slope’s structure and morphology, discretizing the slope into grid units and neglecting the interactions between different parts of the slope. However, slope units constitute the fundamental elements for stability analysis of natural slopes. Moreover, landslide bodies or slopes prone to landslides exhibit significant spatial heterogeneity. In order to realize the landslide hazard assessment in slope units, this study proposes a physically-based model called representative profile model (RPM). RPM takes the slope unit as the assessment unit and couples the slope surface morphology, Quaternary deposits thickness and ground water level. In order to represent the information of the slope unit within a single cross-section, the elevation range of the slope unit is divided with a uniform interval into some elevation segments. Each segment is assigned the average grid values of its respective elements. Then, a representative profile can be generated, consisting of ground surface, sliding surface, and ground water level. RPM also integrates the slices method and the Monte Carlo method to calculate the failure probability, allowing a physically-based hazard assessment in slope unit at a large scale. This study automates the process of RPM model through secondary development of ArcGIS. RPM model were applied in Tiefeng Township, Chongqing, China. The results validated by ROC curves and field investigation represent good performances, which could provide evidence of the potential of RPM for the landslide hazard assessment at regional scale.

This study aimed to compare the prediction performance of single and ensemble machine learning (ML) models in terms of landslide susceptibility mapping in areas affected by the 2017 Jiuzhaigou earthquake. The single ML models selected were the logistic regression (LR) and naïve Bayes (NB) algorithms, and the selected ensemble ML models were the C4.5 decision tree (C4.5 DT), random forest (RF), light gradient boosting machine (LightGBM), extreme gradient boosting (XGBoost), RF coupled with information value (RF‒IV), and XGBoost‒IV. In total, 2482 landslides were identified and used to create training (75%) and validation (25%) datasets. Next, 11 landslide condition factors passed the multicollinearity evaluation. The feature importance between these conditioning factors was determined by SHAP analysis. The prediction capability of the eight models was validated and compared in terms of different statistical indices. The results revealed that the XGBoost model performed best (AUC = 0.910, ACC = 0.85, AP = 0.89, and k = 0.70), followed by XGBoost‒IV (AUC = 0.908), RF‒IV (AUC = 0.902), RF (AUC = 0.899), NB (AUC = 0.899), LightGBM (AUC = 0.897), LR (AUC = 0.875), and C4.5 DT (AUC = 0.838). These findings indicate that the statistically coupled model may negatively affect the accuracy and stability of single ML models. SHAP identified peak ground acceleration, lithology, and precipitation as key drivers of landslides, revealing a nonlinear relationship between feature variables and landslide prediction. This study provides a reference for predicting potential landslide hazard-prone areas and for explainable artificial intelligence research based on ML algorithms.

Using the steam equilibrium method of saturated salt solutions, this study investigated the evolution of the soil-water characteristic curve (SWCC) for Gaomiaozi (GMZ) bentonite under multifactorial conditions (dry density, temperature, and salinity). Results revealed that water retention capacity positively correlated with dry density. Elevated temperatures reduced water retention by shifting SWCC downward, while increased salinity enhanced water retention capacity, particularly under low suction. Dry density exerted greater regulatory influence on SWCC than temperature, whereas salinity surpassed dry density in impact intensity. Based on the vG model, by introducing different influencing factors, a mathematical model of the SWCC that includes the coupling term of dry density, temperature and salinity was constructed. Further, based on Bayesian regularization neural networks, two different forms of SWCC prediction models have been established through machine learning training on a large amount of SWCC test data influenced by multiple factors. One is a data-driven model that takes matric suction as an input variable to predict the volumetric water content under different suction conditions. The other is a data-physics fusion-driven model that uses the proposed SWCC model as a physical constraint to predict model parameters. Both models enabled accurate SWCC prediction in complex environments using basic inputs (physical properties and environmental parameters). Experimental validation confirmed their effectiveness, demonstrating reliable predictive performance for SWCC behavior influenced by multiple factors.

Deterioration caused by water transport commonly poses a large threat to culturally-important rock-hewn caves (often called grottoes), affecting the state of wall paintings, sculptures, and cliffs. Even grottoes located in extremely arid areas face these issues. In this paper, we focused on Cave 6 of Yulin Grottoes located in northwest China, which despite being sheltered from direct rainfall ingress still suffers from damp problems. Through multi-scale monitoring and experiments, we identified that air pressure potential and thermal potential are dominant drivers of moisture transport in the surrounding rock. Monitoring at the site scale of 10¹ meters indicated that the water content in the cave ceiling increases during more than 80% of rainfall events, especially during temperature rise and atmospheric pressure fluctuations after rainfall. Large-scale experiments of 10⁰ meters exhibited pore air pressure ascent during rainfall and subsequent temperature rise. Small-scale experiments of 10^− 1 meters demonstrated that pore air pressure has a positive correlation with atmospheric pressure and loading temperature. The process was initiated by rainwater infiltration during rainfall, which triggered the rebalancing of pore air pressure and water content, and was further influenced by post-rainfall fluctuations of atmospheric pressure and temperature rise. We expect these results to support the conservation of Yulin Grottoes, and provide a reference for the conservation of similar heritage sites.

To investigate the degradation of the load-bearing capacity of the surrounding rock in tunnels under fire conditions, this work employed the surrounding rock of the Baishuishan Tunnel as the geological background. A nano-alumina-modified cement-based similar material was developed to reproduce the thermal response of the prototype rock, ensuring that its thermal damage and degradation characteristics were consistent with those of the natural rock. Based on this material, a scaled physical model of the prototype tunnel was constructed and subjected to mechanical loading after simulated fire exposure of varying durations. The temperature diffusion and evolution within the surrounding rock during fire exposure were quantitatively analyzed, and the effects of fire duration on the post-fire mechanical response of the surrounding rock structure were evaluated using acoustic emission (AE) monitoring and digital image correlation (DIC) techniques. The main findings are as follows: when the fire duration reached 60 min, the temperature at the tunnel crown exceeded 300 °C. With the extension of fire duration to 240 min, the high-temperature region expanded, although the rate of temperature increase diminished. Both the ultimate bearing capacity and the generalized elastic modulus decreased linearly with increasing fire duration, with reductions of 36.7% and 40.0%, respectively, observed at 480 min. When the fire duration was ≤ 240 min, the predominant failure mode was shear failure, whereas at durations was ≥ 360 min, a mixed tensile-shear crack network developed. The sustained and active AE signals provided a quantitative measure of the progressive damage within the surrounding rock.

To explore the triggering mechanism and disaster process of loess landslides under intermittent rainfall, this study focuses on a loess landslide in the Ili River Valley in Central Asia and employs physical model test and microscopic testing methods to investigate the destabilization mechanism of intermittent rainfall-induced loess landslides. The research findings reveal the following: The deformation evolution of loess landslides triggered by intermittent rainfall primarily unfolds in three stages: the expansion of surface fissures, followed by surface runoff and foot-of-slope erosion, culminating in sudden landslides precipitated by a sharp rise in pore water pressure. As rainfall infiltrates, fine particles in the upper soil layer migrate downward through pores, coalesce, and form pore throat blockages. This leads to a sustained increase in pore water pressure within the slope that cannot dissipate, generating excess pore water pressure and thereby reducing effective stress. Ultimately, as the eroded zone at the slope toe continues to expand, the stress imbalance culminates in sudden landslide initiation. By comparing physical model monitoring data with field observations, it is evident that intermittent rainfall triggers a sequence in earthen slopes: fluctuating moisture content increases - cumulative rise in pore water pressure - non-linear acceleration of landslide deformation. This process diminishes slope stability, precipitating landslide disasters. Moreover, the deformation exhibits hysteresis, significantly influenced by the time required for moisture infiltration. This study provides a theoretical foundation and scientific basis for the monitoring, early warning and prevention of loess landslide disasters triggered by intermittent rainfall.

In room-and-pillar mining, pillar stability depends on both the mechanical properties of the rock mass and the excavation geometry. While empirical methods typically assume homogeneous and isotropic conditions, many stratiform ore deposits are intersected by igneous intrusions, introducing heterogeneity. This study presents a comprehensive parametric analysis, implemented in COMSOL Multiphysics, to quantify the effect of dyke geometry, stiffness contrast, and contact strength on the stress distribution within rock pillars. Using the Arqueros Mine in Chile as a case study, we demonstrate that lithological contacts act as stress raisers and can locally increase the maximum compressive stress by up to 58% when the contact is relatively week, and by up to 29% when the contact is strong, relative to homogeneous conditions. Based on these results, we propose a set of correction factors to adjust safety factor estimations derived from traditional models. These correction factors provide a practical and replicable approach for evaluating the geomechanical performance of pillars affected by subvertical intrusions. Although stiffness parameters for host and intrusive rocks can be measured in the laboratory, contact stiffness remains difficult to quantify directly. We therefore recommend that future studies integrate numerical modelling with laboratory and field data to refine the representation of lithological contacts and improve design reliability in heterogeneous mining environments.

This study aimed to predict the quality of aggregates to be produced from the Lower Eocene limestone of septentrional Tunisia and to identify suitable industrial applications. A series of sampling from two different deposits (Jebel Soumeur and Kef Chegagga) in Bizerte region was conducted: thirteen limestone lithotypes were subjected to a comprehensive characterization of their mineralogical and geochemical composition, petrophysical (bulk density (ρ_bulk), water absorption (%W) and P-wave velocity (Vp)) and mechanical (Los Angeles (LA) and Micro-Deval (MDE)) properties. Statistical assessment of the dataset by the Principal Components Analysis (PCA) that allowed the combination of destructive and non-destructive analysis showed a significant distinction between the limestone deposits according to their mineralogical composition (85% < Calcite < 99% − 1% < Quartz < 15%) and their microstructural properties that affect the material durability. These results highlighted that the most compact limestone samples (Vp ≥ 4000 m/s) with the highest purity degree (CaCO₃ ≥ 95%) are the most resistant to wear and weathering conditions (LA ≤ 20%, MDE ≤ 15%) and are the most compatible for aggregate manufacturing in accordance with standards requirements.