Xin Zhou

To investigate the potential of generative artificial intelligence (Gen AI) in sustainable engineering education and laboratory instruction, this study used the direct shear test in soil mechanics as a case study. A total of 112 third-year undergraduate students majoring in hydraulic engineering were assigned to traditional and AI-assisted groups. Their performance in experimental operation, data processing, report writing, presentation of results and problem solving was compared using grade statistics, classroom observations and interview data. The results showed that the AI group generally outperformed the traditional group in Experimental operation, data analysis, report completeness, presentation and defense, and overall scores. The analysis showed that Gen AI can function as a form of cognitive scaffolding in conceptual explanation, data processing, report structuring, and error analysis. Specifically, it can provide stage-specific prompts for understanding, procedural support, and feedback during the learning process, thereby helping students complete experimental learning tasks. However, the qualitative corpus included documented cases of overreliance on Gen AI, including uncritical acceptance of generated outputs and alteration of discrepant data without adequate verification; these behaviours were not tabulated at the pair level, so their prevalence cannot be estimated. These findings suggest that the integration of Gen AI into soil mechanics laboratory instruction should be grounded in teacher guidance, disciplinary knowledge support, data verification awareness, and standardized tool-use practices. These findings also offer implications for the responsible use of Gen AI in sustainable engineering education and for the development of students’ AI literacy, awareness of data authenticity, and responsible technology use competencies.

The estimation of the area susceptible to rock failure and the prediction of its movement process are pivotal for hazard mitigation, yet they are also challenging. In this study, we proposed a novel integrated method combining field investigation, remote sensing, and three-dimensional discrete element method (DEM) simulation to achieve our goal. The field investigation and remote sensing analysis are used for the purpose of ascertaining the deformation phenomenon and the structure of the rock slope, identifying the potential failure position and area of the slope. Subsequently, a three-dimensional DEM simulation is employed to quantitatively assess the potential rock failure-affected area and movement process, based on the above potential failure information. The simulation results demonstrate that potential rock failure persists for approximately 30 s, and its movement process can be categorized into two distinct stages: acceleration and deceleration. The initial acceleration stage is characterized by a duration of 10 s, culminating in a peak average velocity of 13 m/s. The subsequent deceleration stage extends for a duration of 20 s. Notably, the maximum attainable velocity for the segment of rock mass under consideration is estimated to be 50 m/s. Furthermore, the model demonstrates the variation in fracture energy, friction energy, and kinetic energy over time. The potential affected area is 140,000 m², and approximately 8000 m² of residential construction will be destroyed if a rock failure occurs. It is imperative to implement measures aimed at the prevention of rock failure in order to mitigate the risk of such an occurrence.

Landslide susceptibility mapping (LSM) faces persistent challenges in defining representative stable samples as conventional random selection often includes unstable areas, introducing spatial bias and compromising model accuracy. To address this, we redefine the certainty factor (CF) method—traditionally for factor weighting—as a spatial screening tool for stable zone delineation and apply it to the tectonically active upper Jinsha River (937 km², southeastern Tibetan Plateau). Our approach first generates a preliminary susceptibility map via CF, using the natural breaks method to define low- and very low-susceptibility zones (CF < 0.1) as statistically stable regions. Non-landslide samples are exclusively selected from these zones for support vector machine (SVM) modeling with five-fold cross-validation. Key results: CF-guided sampling achieves training/testing AUC of 0.924/0.920, surpassing random sampling (0.882/0.878) by 4.8% and reducing ROC standard deviation by 32%. The final map shows 88.49% of known landslides concentrated in 25.70% of high/very high-susceptibility areas, aligning with geological controls (e.g., 92% of high-susceptibility units in soft lithologies within 500 m of faults). Despite using a simpler SVM, our framework outperforms advanced models (ANN: AUC, 0.890; RF: AUC, 0.870) in the same region, proving physical heuristic sample curation supersedes algorithmic complexity. This transferable framework embeds geological prior knowledge into machine learning, offering high-precision risk zoning for disaster mitigation in data-scarce mountainous regions.

To clearly realize the dynamic process as well as the dynamic fragmentation behavior of a long run-out rockslide, a novel numerical method for landslide simulation of the coupled finite-discrete element method (FDEM) was applied and the Jichang rockslide was used as a case. The calibrated simulation result of the FDEM in a rockslide deposit corresponds well with the real rockslide deposit. The main run-out process of the rockslide lasts for 75 s and can be divided into acceleration and deceleration stages, which last for 33 s and 42 s, respectively. The maximum overall rockslide movement speed is 35 m/s while the partial sliding mass reaches 45 m/s. The fracturing, fragmentation, and disintegration processes of the sliding mass can be clearly observed from the dynamic scenarios. Fracture energy generated by rock fracturing constantly increases with time in a non-linear form. Of the total fracture energy, 54% is released in the initial 5 s because of fracturing, and 39% of the total fracture energy is released because of fragmentation and disintegration in the last 35 s. The accumulated friction energy increases in the whole run-out process, and its magnitude is much greater than the kinetic energy and fracture energy of the sliding mass.

Combined with visible light remote sensing technology and InSAR technology, this study employed the fundamental principles of the frequency ratio model, information content model, and analytic hierarchy process to assess the susceptibility of the study area. Nine susceptibility assessment factors such as elevation, slope, aspect, water system, vegetation coverage, geological structure, stratum lithology, rainfall, and human activities were selected, and the factor correlation degree was calculated by using the relative area density value of the landslide. The frequency ratio model and information content model were selected to carry out landslide susceptibility zoning, and the accuracy of the two models was verified by the ROC curve and density method. The results indicate that the information content model performed relatively well. Therefore, the information model, combined with the analytic hierarchy process and fuzzy superposition method using the landslide point density map, was chosen to evaluate landslide susceptibility. The study area was divided into five levels of landslide hazard, ranging from low to high, using the natural discontinuity point method. The results show that the area of each hazard zoning is 197.48, 455.72, 408.21, 152.66, and 16.22 km² from low to high, and the proportion of landslides in the corresponding area is 0.17%, 1.60%, 3.88%, 8.41%, and 16.65%, respectively. It can be seen that with the increase in the hazard level, the proportion of landslides also increases significantly, which verifies the accuracy of the hazard results. Additionally, four representative landslides in the study area were selected for analysis to understand their characteristics and underlying mechanisms. The results revealed that these landslides were notably influenced by the density of the Jinsha River and the surrounding roads. The susceptibility assessment outcomes for geological disasters align well with the current situation of landslide occurrences in the Tuoding river section, demonstrating high accuracy. This study provides a scientific foundation for effective prevention and control measures against local landslide disasters.

In the analysis of rock slope stability and rock mass hydraulics, the dominant partitioning of discontinuities of rock masses is a very important concept, and it is still a key for establishing the three-dimensional (3-D) network model of random discontinuities. The traditional graphical analysis method is inadequate and greatly influenced by subjective experience. A new method using density-based spatial clustering of applications with noise (DBSCAN) algorithm is proposed for the dominant partitioning of discontinuities of rock mass. In the proposed method, we do not need to determine the centers of every cluster before clustering, and the acnodes or outliers can be eliminated effectively after clustering. Firstly, the spatial coordinate transformation of the discontinuity occurrence is carried out and the objective function is established by using the sine value of the angle of the unit normal vector as the similarity measure standard. The DBSCAN algorithm is used to establish the optimal clustering centers by searching the global optimal solution of the objective function, and the fuzzy C-means clustering algorithm is optimized and the mathematical model of the advantage grouping of rock discontinuities is established. The new method and the fuzzy C-means method are compared and verified by using the artificially randomly generated discontinuity occurrence data. The proposed method is a better method than the fuzzy C-means method in general cases, and it can provide more accurate results by eliminating the acnodes or outliers. Finally, the proposed method is applied to discontinuity orientation partition data at Maji dam site, Nujiang River, and there is good agreement with the in situ measurement.

A structural domain represents a volume of a rock mass with similar mechanical and hydrological properties. To demarcate structural domains (or statistically homogeneous regions) in fractured rock masses, this study proposes a three‐parameter simultaneous analysis method (3PSAM) that simultaneously considers rock fracture orientation, trace length, and aperture to evaluate statistical homogeneity between two regions. First, a 102‐patch three‐dimensional Schmidt net, which represents a new comprehensive classification system, is established to characterize rock fractures based on their orientation and aperture. Two populations of rock fractures can then be projected to the corresponding patches. Second, the Wald–Wolfowitz runs test is used to measure the similarity between the two populations by considering the fracture trace lengths. The results obtained by applying the 3PSAM to seven simulated fracture populations show that the homogeneity is influenced by both the distributions of the fracture parameters and the sequences of the fracture parameters. The influence of a specific combination sequence makes it impractical to analyze the rock fracture parameters individually. Combined with previous methods, the 3PSAM provides reasonable and accurate results when it is applied to a fractured rock slope engineering case study in Dalian, China. The results show that each fracture population should be identified as an independent structural domain when using the 3PSAM. Only the 3PSAM identifies the west exploratory trench 2 and the east exploratory trench as being nonhomogeneous because the difference in the aperture of the two fracture populations is considered. The benefit of the 3PSAM is that it simultaneously considers three parameters in the demarcation of structural domains.