Understanding the internal structure and geometry of large-scale gravitational slope instabilities is crucial for hazard assessment and risk mitigation in mountainous regions. This study presents a high-resolution 2D and 3D seismic first-arrival traveltime tomography analysis of the Cuolm da Vi (CdV) slope instability, one of the largest active mass movements in the Alps. To achieve this, we conducted an extensive seismic survey, deploying over 1000 autonomous nodes across a 0.7 km 2 area and acquiring data from 144 controlled-source shots. Our resulting 2D and 3D tomographic models reveal significant subsurface heterogeneities, including extensive low-velocity zones up to depths of 200 meters, indicative of severe rock mass disintegration. Additionally, strong lateral velocity variations persist throughout the unstable zone, further corroborating its structural complexity. Our findings align with previous studies that suggest toppling as the dominant deformation mechanism. The comparison between 2D and 3D velocity models highlights the critical role of out-of-plane effects, such as observed lateral ray bending, emphasizing the importance of 3D imaging for accurate characterization of complex instability structures. The 2D and 3D velocity models provide important constraints for estimating the total unstable rock volume and serve as a foundation for future geotechnical analyses and hazard assessments. This study also demonstrates the feasibility and effectiveness of large-scale nodal seismic deployments in alpine terrains, paving the way for further applications in monitoring and characterizing deep-seated slope instabilities. • Large-scale 3D seismic tomography with > 1000 receivers at the Cuolm da Vi instability. • Subsurface imaging reveals extensive rock disintegration zones up to 150 - 200 m depth. • Significant lateral velocity variations highlight the internal structural complexity. • Comparison of 2D and 3D models emphasizes the significance of out-of-plane effects.

ChatGPT has been recently applied to many engineering fields including geotechnical engineering. This study investigates the chance of using ChatGPT for seepage-induced slope stability problems based on the grid and radius method. The Python code for solving seepage and slope analysis was first generated, followed by the coupling of those two analyses was performed using ChatGPT. In addition, the grid search method and three scenarios of optimization methods were applied to determine the best optimization method for the computational efficiency of the developed framework. The comparable factor of safety (maximum error of 1.86%) shown in this study compared to those obtained by commercial software demonstrated the feasibility of using ChatGPT-generated code for seepage-induced slope stability analysis. Furthermore, the use of optimization techniques enabled up to a 70% reduction in calculation time, and the relatively easy implementation of optimization methods using ChatGPT implies that the appropriate prompts in ChatGPT can provide a wide range of applications in slope stability analysis.

A novel physics–data hybrid approach for slope stability assessment considering future rainfall patterns

Sustainable bio-cementation based on calcium lignosulfonate-assisted enzyme-induced carbonate precipitation: synergistic mechanism for road loess slope stabilization under rainfall

In recent years, microbial induced calcium carbonate precipitation (MICP) and vegetation slope protection technology have been proven to be feasible. However, since the process of MICP-improved vegetation slope protection is affected by factors such as vegetation type, the research on the adaptability and stability of roots in the root-soil complex is still not in-depth. Therefore, this paper carried out plant adaptability and erosion resistance tests and triaxial tests on MICP-solidified root-soil complexes to explore the changing laws of vegetation germination adaptability, erosion resistance and soil mechanical properties under the action of MICP solidification. The results showed that: (1) Tall fescue germination potential declined from 72.3 to 40.7% with increasing reaction solution concentration, and more sharply from 66 to 26.5% as the number of spray applications increased. Paspalum notatum showed a similar trend, decreasing from 11.4 to 2.2% (concentration) and from 8.5 to 1.2% (spray applications). Overall, the number of spray applications exerted a greater inhibitory effect than concentration. (2) Microorganisms can enhance the ability to resist erosion. After 6 sprayings, the erosion rate is only 1.5%. Microorganisms combined with plants can significantly inhibit continuous rainfall; (3) The stress-strain curve of MICP-reinforced root-soil composite shows an upward trend and is a strain hardening type. Roots can promote the formation of calcium carbonate, cement the soil and fill the pores, so that the c and φ values of the MICP-reinforced root-soil complex are positively correlated with the calcium carbonate and root content, and the C value increases more significantly; (4) MICP technology has a great influence on the root The strength increase ratio of soil composite strength is very important, and its MICP strength increase ratio is as high as 80% under the optimal root content. MICP can effectively improve the adaptability of vegetation slope protection technology and improve the stability of slopes. Therefore, it can be considered that MICP has important significance for improving the stability of slopes by improving vegetation slope protection technology.

Parametric assessment of rainfall-related slope stability through SRM modeling and orthogonal experimental design: insights from the Zhuquedong slope, China.

Kenya faces significant seismic and landslide risks due to its location along the East African Rift System. Historical earthquakes, such as the 1928 Subukia event (magnitude 6.9), have caused widespread damage, and recent deadly landslides, including the 2019 West Pokot disaster, have resulted in over 70 fatalities and the displacement of thousands. The study employs a comprehensive analytical framework encompassing vulnerability assessment, risk assessment, preparedness measures, mitigation strategies, response mechanisms, and rehabilitation protocols. A vulnerability matrix identifies residential buildings, informal settlements, rural hill communities, and vulnerable populations (children, elderly, persons with disabilities) as high-risk elements, while a risk assessment matrix reveals that landslides present more frequent and immediate threats compared to earthquakes, which though less frequent, carry potential for devastating impacts. The findings indicate that effective preparedness requires integrating early warning systems, public education, emergency drills, stockpiling of relief supplies, and evacuation planning. Mitigation strategies include hazard mapping, enforcement of building codes, slope stabilization, reforestation, and land-use planning regulations. The study highlights the stark implementation divide between developed nations with institutionalized preparedness and developing countries like Kenya facing challenges of limited resources, weak enforcement, fragmented coordination, and systemic vulnerabilities. The paper concludes that multi-faceted, collaborative, and continuous preparedness planning, incorporating scientific data with community-based approaches, is essential for reducing disaster impacts, saving lives, and minimizing economic losses. Recommendations include strengthening institutional capacity, enforcing building regulations, investing in early warning technologies, promoting community awareness, and mainstreaming disaster risk reduction into national and county development plans.

ABSTRACT Landslides are devastating natural disasters, resulting in loss of life and property. We can reduce the impact of landslides by identifying the susceptible areas and regularly monitoring slope instability. This article presents a novel stacked ensemble machine learning (ML) model for landslide susceptibility mapping in Meghalaya, India. The proposed stacking model consists of the base layer and the meta layer. The base layer employed Logistic Regression, XGBoost, Random Forest, LightGBM, Support Vector Classifier, Extra Trees, k-Nearest Neighbours, Decision Tree, Gaussian Naive Bayes, and Quadratic Discriminant Analysis as the base learners. A neural network meta learner then processes the intermediate features (meta-features) to predict the probability of landslide occurrence. Recursive feature elimination with cross validation and the variance inflation factor were employed to determine the optimum features. Sixteen landslide causative factors, along with 2070 landslide and non-landslide points, were utilised to train and evaluate the proposed model. The proposed model's performance was assessed using accuracy, AUC, F1-score, precision, recall, and Kappa. It outperformed the individual base learners and advanced ML methods, including deep learning and hybrid approaches. Finally, the proposed model was employed for generating a landslide susceptibility map of Meghalaya.

The long-term stability of jointed rock slopes subjected to freeze–thaw (F–T) cycling is a key scientific issue in landslide risk assessment in cold regions, yet the influence of F–T cycling on creep behavior and associated damage mechanisms in jointed rock masses remains unclear. To address this, slate specimens with varying joint angles were collected from landslide deposits in Wet-Freeze Zone II of China. A series of creep tests under F–T cycling was performed to investigate the effects of F–T cycles and joint angle on the creep properties of slate. Based on the experimental observations, a novel anisotropic creep model accounting for F–T damage and joint effect was developed. Model validation against creep test data confirms its ability to reproduce the entire creep deformation process and to capture the influence of joint angle and F–T cycling on creep behavior. The proposed model was further integrated with strength reduction method and applied to evaluate the long-term stability of slopes. The results indicate that the potential landslide direction is directly related to the joint angle, and with increasing F–T cycles, the factor of safety decreases while the plastic zone progressively develop upward along the pre-existing joint planes, extending further with creep duration. These findings enhance understanding of the instability mechanisms for geotechnical slope in cold regions.

ABSTRACT To address the challenges in slope-failure precursor identification and landslide risk monitoring, this study proposes an analytical method that utilises the dual dominant frequency mechanism in microseismic (MS) signals across laboratory and field scales. Experimental tensile and shear tests revealed dual frequencies in rocks (high: 260–300 kHz; low: 50–120 kHz), with a characteristic frequency ratio shift preceding rupture. The field MS signals from the Baige landslide exhibited analogous dual frequencies (high: 210–250 Hz; low: 20–70 Hz), where a significant ratio shift prior to failure served as a validated precursor during the third landslide event. These precursor characteristics were employed to evaluate slope stability, integrated with microseismic moment tensor inversion for precise risk zone identification. A comprehensive analysis confirmed the consistency among the multi-source field data, moment tensor-inferred damage modes, dual-frequency characteristics and field investigations. The dual-frequency-based methodology demonstrates broad applicability for landslide stability assessment and offers a novel approach for early warning of geotechnical disasters.

Abstract Studying deep-seated catastrophic landslides is challenging due to their complex geological conditions, slow and imperceptible movements, limited real-time monitoring, and difficulties in accurately assessing sliding behavior. We examine slope instability in a deep-seated landslide-prone area located in Lantai, Taiwan, by analyzing the temporal evolutions of slope movement and rainfall. Using a non-invasive automatic monitoring system that integrates the Global Navigation Satellite System, seismic velocity changes derived from ambient noise interferometry, as well as rainfall and groundwater-level records, we investigate rainfall-induced accelerated slope deformation in 2015–2024. Our findings show that acceleration events are triggered when rainfall lasts over 30 h and cumulative rainfall exceeds 200 mm at the study site. Multiple sliding events show a consistent temporal sequence, in which rainfall triggers an immediate dv/v decrease of 2–4%, followed by GNSS horizontal displacement typically in the range of ~10–30 mm within 1–2 days, and a delayed groundwater-level rise of about ~3 m occurring 4–5 days after peak GNSS displacement rate. This time lag suggests the initial slope acceleration is associated with pore-pressure increases within the landslide mass. The one-dimensional hydrological model successfully simulates rainfall infiltration into the groundwater system, enabling prediction of delayed groundwater responses. This integrated monitoring-modeling framework improves hazard assessment and early warning for rainfall-induced deep-seated landslides and is broadly applicable worldwide.

In seasonal frozen zones, reinforced soil (RS) slopes are susceptible to uneven settlement or potential slope instability due to prolonged freeze-thaw (FT) cycles. To investigate the effects of cycle number, soil moisture content, and reinforcement type on the performance of RS slopes under FT conditions, five model tests on RS slopes were conducted. The test results indicated that as the number of FT cycles increased, the amplitude of temperature fluctuations within the slope gradually decreased, moisture gradually accumulated near the slope surface and bottom, and slope deformation and reinforcement strain progressively accumulated. With an increase in the initial soil moisture content, the temperature changes within the slope slowed, the amplitude of temperature fluctuations reduced, internal moisture migration gradually intensified, and slope deformation and reinforcement strain increased progressively. Differences in stiffness, permeability, and soil-reinforcement interface behavior among reinforcement types led to variations in the performance of reinforced soil slopes with different reinforcement materials. Additionally, based on theoretical analysis, the influence of FT cycles on the thermal conductivity coefficient of RS slopes was studied. The results indicated that as the number of FT cycles increased, the soil thermal conductivity coefficient gradually rose under the combined effects of FT cycles and external loads.

Although slope failures are inherently three-dimensional (3D) and soils exhibit spatial variability, studies that consider both factors remain limited. To address this issue, an efficient framework is proposed in this work for probabilistic evaluation of 3D slope stability with spatially variable soil properties. The framework couples a 3D convolutional neural network (CNN) with Monte Carlo simulation (MCS) for reliability assessment. Spatial variability in soil strength is modeled using random fields, with realizations obtained through the fast Fourier transform method. A deterministic solver based on discretized limit analysis (DLA) is implemented to evaluate 3D slope stability in spatially heterogeneous soils. A limited number of random field samples is generated, and the associated slope stability responses are evaluated with the DLA-based deterministic solver. The resulting paired input-output data constitute the training set for a 3D CNN, which can then estimate slope stability for new random field realizations. The trained CNN surrogate enables large-scale MCS for probabilistic slope stability analysis with high computational efficiency. Model performance is assessed at both the deterministic solver level and the CNN level. Overall, the proposed DLA-CNN coupling provides an efficient approach for 3D slope reliability analysis, enabling accurate probabilistic evaluation with markedly reduced computational cost

The Dlingo area in Bantul Regency, Yogyakarta, is characterized by steep slopes and volcanic deposits that contribute to high landslide susceptibility, as evidenced by destructive events in 2021. This study aims to characterize subsurface conditions controlling slope instability using electrical resistivity modeling. Geoelectric data were acquired using the Wenner configuration along four survey lines, consisting of three slope-parallel profiles and one slope-perpendicular profile, each 225 m long with 15 m electrode spacing. Apparent resistivity data were processed and inverted using Res2Dinv to generate two-dimensional (2D) resistivity sections. The results consistently reveal three principal resistivity zones across all profiles: (1) a high-resistivity unit (approximately 33,9–642 Ωm) interpreted as relatively compact to slightly weathered tuff, (2) a moderate-resistivity layer (1.79–33.9 Ωm) corresponding to weathered volcanic deposits, and (3) a low-resistivity zone (<1.79 Ωm) interpreted as water-saturated or highly conductive material developed along the lower slope. The recurrent resistivity contrast between the weathered layer and the underlying conductive zone suggests a hydrogeological boundary that may facilitate pore-water accumulation and contribute to slope instability. These findings provide a geophysically constrained conceptual model of subsurface controls on slope instability in volcanic terrains.

To address the stability issues of bridge piers on high expansive soil slopes in the Yangtze-Huaihe River Water Transfer Project and reveal the slope-bridge structure interaction mechanism, this study performed 100 g geotechnical centrifuge model tests. Slope failure modes under rainfall-bridge load coupling are investigated, with bridge pier deformation, earth pressure, and pile bending moment evolution analyzed. Results show that rainfall-induced failure causes shallow slope sliding with negligible pier displacement, keeping the structure safe. Conversely, under bridge working and ultimate loads, the slope will experience a mid-deep landslide with a sliding depth of 13–20 m, leading to slope instability and bridge overturning. The influence range of shallow landslides is 1–2 m, and the earth pressure at the pile cap is 132 kPa, which is a critical factor affecting bridge stability. In contrast, the bearing performance of pile foundations plays a dominant controlling role in deep-seated landslides. With the increase in landslide depth, the inflection point of the pile gradually moves downward. Numerical simulations further indicate that shallow landslides feature superficial slip–shear failure, and deep-seated landslides follow a progressive slip tensile cracking mechanism.

Abstract A fundamental understanding of landslide evolution requires characterizing how deformation localizes within the sliding mass, as these non-homogeneous zones provide crucial insights into how destabilization initiates, failure surfaces develop, and the overall kinematic behavior evolves. While traditional analysis often assumes uniform movement, this study presents a methodology to quantify intricate patterns of surface deformation at a fine scale, allowing for the direct analysis of localization behavior. By applying strain tensor analysis to high-resolution displacement fields derived from multi-temporal Uncrewed Aerial Vehicle-Light Detection and Ranging (UAV-lidar) and Structure from Motion (SfM) surveys, we compute the divergence, gradient, and curl fields for two distinct landslides: one translational and one rotational. This approach quantifies volumetric changes, translational strain, and rotational components, revealing unique kinematic signatures for each landslide type. The translational slide is characterized by alternating expansion-contraction patterns along its dip-line, whereas the rotational slide exhibits clear, separate bands of head subsidence and toe expansion, coupled with non-uniform rotation along the strike. This detailed characterization of strain localization provides direct observational evidence of the fundamental mechanisms governing landslide behavior. It offers a more nuanced, mechanistic understanding that advances the interpretation of slope instability, providing a stronger physical basis for hazard assessment and risk management.

Landslides occurring on reservoir banks in steep, high-gradient canyon areas pose a significant risk of surge disasters when they slide into the water. This can endanger the lives and property of downstream residents and damage coastal infrastructure. Therefore, researching the formation mechanisms, disaster evolution, and risk assessment of the landslide-surge disaster chain in such areas is essential. This paper takes the Laowuchang landslide in the Shuibuya Reservoir area of the Qingjiang River, China, as its research object. Using GeoStudio 2018 software, it evaluates the landslide’s stability under varying reservoir water levels and rainfall conditions. For potential unstable scenarios identified, a full-chain numerical simulation of the landslide–tsunami disaster was conducted based on the Tsunami Squares method, with a focus on analyzing the wave characteristics during generation, propagation, and run-up processes. Furthermore, the paper assesses the risk of landslide–tsunami disasters in the Laowuchang landslide area. The research findings indicate that: (1) Under the long-term continuous river incision, limestone of the Triassic Daye Formation slides along weak interlayers, inducing large-scale collapses. Subsequently, part of the landslide mass is transported by water, while most accumulates in the near-shore area of the Qingjiang River, ultimately shaping the present morphology of the landslide. (2) The Laowuchang landslide is stable under static water levels of 375 m and 400 m, with corresponding safety factors of 1.137 and 1.167, respectively. Under combined static water level and heavy rainfall conditions, the slope stability decreases significantly, with safety factors of 1.034 and 1.064, respectively. Under reservoir drawdown conditions, the slope tends to be unstable, with a safety factor of 1.047. (3) Numerical simulation results indicate that if the Laowuchang landslide fails into water by the speed of 12 m/s and with a volume of 2 million m3, the maximum initial wave height can reach 15.9 m. The tsunami’s affected range spans 10 km upstream and downstream from the landslide mass, with four houses and one substation within a 2 km up and downstream falling into high-risk areas. If abnormal increases in landslide displacement occur, relocation and risk avoidance measures should be implemented. The findings of this study provide a scientific basis for the prevention and response to landslide–tsunami disasters in similar high and steep canyon terrains.

Summary Waste management is one of the major environmental challenges of the twenty-first century. This Perspective examines how vegetation dynamics at composting facilities and landfills both reflect and influence anthropogenic environmental change. We define our use of the Anthropocene as a human-dominated epoch that is functionally and stratigraphically distinct from the Holocene, and we argue that waste-derived ecosystems constitute model systems for detecting its signals through technogenic substrates and synanthropic succession. Although composting reduces pressure on landfills, incomplete processing of biowaste can disseminate propagules of invasive plant species. Landfills, shaped by disturbance and altered edaphic regimes, support synanthropic plant assemblages dominated by neophytes that act as bioindicators of leachate stress and other pressures. At the same time, spontaneous vegetation provides functional benefits, including slope stabilization, organic matter accumulation and habitat provision during early successional stages. We bring together information on risks and functions, link ecological criteria to permitting, monitoring and post-closure management pathways, and outline practical considerations for integrating plant-based indicators with geochemical screening. These steps enable ecologically sensitive strategies to be implemented that mitigate biodiversity risks while leveraging succession to improve the resilience of waste-derived landscapes.

A Case Study of Ground Anchor Effectiveness on Slope Stability in Red Basaltic Soil

Organic amendments for slope bioengineering: Enhanced Melastoma malabathricum establishment and growth.