Stability of Slopes Reinforced with a Combined Waste Tyre and Vetiver System

ABSTRACT Permafrost degradation on the Qinghai–Tibet Plateau, driven by climate warming, has increased slope instability and landslide hazards, threatening key infrastructure such as the Qinghai–Tibet Railway and Highway. This study addresses landslide detection in the challenging Qinghai–Tibet Plateau environment, introduces a landslide identification method integrates InSAR with slope unit-based clustering. Ascending and descending Sentinel-1A SAR images were processed using time-series InSAR to generate LOS deformation velocity maps over an 8.4-year period. A 10 km-wide buffer zone along both sides of the railway and highway was designated as the study area. By integrating optical imagery, field investigation, and DBSCAN clustering with slope unit segmentation, 104 landslides including retrogressive thaw slumps were identified. Spatial distribution patterns were analyzed, and a SHAP-based interpretation of dominant factors and their interactions was conducted. Results show that elevation is the decisive factor in landslide occurrence, while precipitation is the dominant controlling variable. Lithology and fault proximity also have significant impacts. Retrogressive thaw slump development is primarily controlled by fault activity and active-layer thickness in permafrost regions. This study provides a robust technical framework and essential data to support landslide early detection and infrastructure protection assessment in high-altitude permafrost environments.

Affected by continuous construction disturbance, the stability coefficient of a slope is calculated to be 0.95, which does not meet the requirements of slope stability; in order to prevent slope destabilization and damage, two kinds of reinforcement schemes are formulated, scheme 1 is “circular anti-slip pile + anchor + cut-off drainage”, scheme 2 is “square anti-slip pile + cut-off drainage”. Numerical analysis of the stress field, displacement field and stability coefficient changes of the two reinforcement schemes; the study shows that:1) Scheme 1 and 2 both meet the requirements for slope stabilization, improving the slope stability coefficients by 45.26% and 35.79%, respectively, relative to the pre-stabilization period; 2) The displacement and deformation of the slope corresponding to the two reinforcement schemes have decreased significantly relative to the pre-reinforcement period, i.e., the coarse reinforcement measures in the schemes have produced effective reinforcement effects; 3) Considering comprehensively, Scheme 1 has more long-term stability benefits, and Scheme 2 has more economic benefits.

Urban flooding has emerged as a critical challenge in rapidly urbanizing cities due to increased surface imperviousness, loss of natural drainage systems, and the rising frequency of extreme rainfall events. Conventional flood-management approaches continue to rely heavily on centralized grey infrastructure, which often proves inadequate under climate-induced hydrological stress. In contrast, decentralized and nature-based solutions such as urban ponds and canals remain underutilized despite their inherent capacity to store, delay, and infiltrate stormwater. This paper investigates the role of urban ponds and canals as decentralized micro-storage nodes within urban flood-resilience frameworks. It examines their hydrological, spatial, and ecological functions and identifies a key research gap: the absence of a parameter-based planning framework to evaluate and integrate these waterbodies into urban flood-management systems. To address this gap, eight national and international case studies including examples from Singapore, India, China, Malaysia, Sri Lanka, and Iraq are analysed to assess the hydrological performance of ponds and canal systems under flood conditions. Through comparative analysis, ten critical performance parameters are identified, including storage capacity, infiltration rate, retention and detention time, peak-flow attenuation, runoff volume, retention capacity ratio, pond depth, side slope stability, sedimentation rate, and catchment–pond relationship. These parameters are synthesized into quantitative, planning-oriented guidelines that provide measurable benchmarks for assessing, restoring, and redesigning urban ponds and canals as functional micro-storage units. The findings demonstrate that when reconnected, restored, and systemically integrated, urban ponds and canals can collectively operate as a decentralized blue-green infrastructure network capable of significantly reducing flood peaks, enhancing groundwater recharge, and improving urban resilience. The study contributes a replicable, evidence-based framework to support water-sensitive and climate-responsive urban planning, particularly in the context of Indian cities.

Mountainous regions, particularly the Lesser Himalayas, face persistent slope stability challenges due to complex geological formations and extreme climatic variability. This study investigates the potential of Nano-silica (NS) as an eco-efficient soil stabilizer for enhancing the mechanical behaviour of fine-grained soils in these terrains. Consolidated undrained (CU) triaxial tests were performed on clay of intermediate plasticity (CI), silt of intermediate plasticity (MI), and low-plasticity clay-silt (CL-ML) soil types treated with different NS contents and subjected to various curing durations. To develop reliable predictive insights, both linear and non-linear regression models were constructed using essential geotechnical parameters, including cohesion, internal friction angle, and the pore-water pressure ratio. A novel model simplification process was employed to derive explicit closed-form equations for the Factor of Safety (FoS), retaining only the most influential terms. The non-linear models demonstrated high predictive accuracy, with R2 values exceeding 0.97, outperforming their linear models for all the soil types stabilized with NS. These interpretable regression models offer a practical tool for slope stability assessments in NS-stabilized soils. The integration of material innovation with simplified computational models contributes to resilient and sustainable infrastructure design in data-scarce highland regions.

Assessment of Slope Stability in Eastern Golestan Province-Iran, Following Rainfall-Induced Landslides

Prediction of rock mass mechanical properties from acoustic wave velocity using a Hoek-Brown constrained Component-Parameter model.

Abstract Rainfall-induced slope failures present significant global risks, resulting in substantial economic losses and threatening lives. As climate change potentially impacts the frequency and intensity of extreme rainfall events, which may influence the likelihood of rainfall-induced slope failures, a deeper understanding of these failures can be beneficial. While the effects of rainfall infiltration—particularly concerning factors like critical intensity, duration, pattern, and antecedent conditions—on slope stability have been studied, research has typically focused on either antecedent rainfall or main rainfall. This has potentially led to an oversight of the possible combined effects of these rainfall patterns. This study aims to evaluate the influence of the combined temporal pattern of antecedent and main rainfall. Four typical rainfall patterns of advanced, normal, delayed, and uniform rainfall are designed based on historical records from Hong Kong are evaluated in this study. Our findings indicate that these combined rainfall patterns influence slope stability, time to reach minimum safety factor, and rate of safety factor reduction. Notably, the combination of antecedent rainfall with delayed and uniform main rainfall patterns creates the most critical conditions, agreeing with historical rainfall records associated with past notable landslides in Hong Kong. Additionally, various definitions of antecedent rainfall—whether immediately before main rainfall or separated by a gap—along with their critical durations, are often adopted in previous studies. This study explores these influences, particularly in the context of combined rainfall patterns, revealing that the combination of delayed antecedent rainfall and advanced main rainfall is particularly sensitive to the gap durations. The practical implications of our findings are also discussed. (260 words)

Advances in ecological restoration of mining-impacted landscapes: Techniques, case studies, and key challenges.

Influence of the hard-to-soft rock layer thickness ratio on the tensile strength of Brazilian disc specimens and the stability of anti-dip slopes

Three-dimensional upper bound perturbation method for stability of slopes based on rigid translational-rotational coupled elements

Probabilistic analysis of stress effects on an unsaturated soil slope stability using convolutional neural networks and Bayesian optimisation

Erosion control performance of natural geotextiles for slope stabilization

ABSTRACT The weathered transition zone in rock slopes is a geological interface characterised by heterogeneous development of joints and fractures within the rock mass. This structural heterogeneity can lead to the accumulation of pore fluids at the weathering interface under extreme rainfall, resulting in considerable hydro-mechanical damage. Therefore, monitoring the stability of this interface is crucial for assessing the overall stability of rock slopes. To address the monitoring challenges posed by extreme rainfall-induced instability at rock slope weathering interfaces, this study conducted seepage failure modelling experiments and developed an anchored monitoring model. Temporal relationships among slope displacement, pore water pressure and rock bolt axial force were analysed using the monitoring principles of intelligent terminal structure sensors integrated within slope anchoring systems. This analysis enabled the establishment of monitoring and early warning criteria based on the pore pressure gradient and rock bolt axial force at the weathered interface. Thereafter, the proposed early warning model for the weathered transition zone was validated through numerical simulations. This study provided quantitative criteria for the stability monitoring and early warning of rock slope weathering interfaces under extreme rainfall conditions.

Granite residual soil (GRS) is highly susceptible to water-induced softening, posing significant risks of slope instability and collapse. Conventional impermeable grouting often exacerbates these hazards by blocking groundwater drainage. This study investigates the efficacy of a permeable water-reactive polyurethane (PWPU) in stabilizing GRS, aiming to resolve the conflict between mechanical reinforcement and hydraulic conductivity. Uniaxial compression tests were conducted on specimens with varying initial water contents (5%, 10%, and 15%) and PWPU contents (5%, 10%, and 15%). To reveal the multi-scale failure mechanism, synchronous acoustic emission (AE) monitoring and digital image correlation (DIC) were employed, complemented by scanning electron microscopy (SEM) for microstructural characterization. Results indicate that PWPU treatment significantly enhances soil ductility, shifting the failure mode from brittle fracturing to strain-hardening, particularly at higher moisture levels where failure strains exceeded 30%. This enhancement is attributed to the formation of a flexible polymer network that acts as a micro-reinforcement system to restrict particle sliding and dissipate strain energy. An optimal PWPU content of 10% yielded a maximum compressive strength of 4.5 MPa, while failure strain increased linearly with polymer dosage. SEM analysis confirmed the formation of a porous, reticulated polymer network that effectively bonds soil particles while preserving permeability. The synchronous monitoring quantitatively bridged the gap between internal micro-crack evolution and macroscopic strain localization, with AE analysis revealing that tensile cracking accounted for 79.17% to 96.35% of the total failure events.

Slope stability is a crucial aspect of geotechnical engineering, particularly for landfills where municipal solid waste (MSW) layers are subjected to both static and seismic forces. This study represents the first application of hybrid metaheuristic-neural models to the Barmshour Landfill, introducing an innovative predictive framework capable of guiding real-world design, stability evaluation, and decision-making processes in waste management engineering. Four hybrid models-BBO-MLP, MVO-MLP, VS-MLP, and BSA-MLP-were developed and evaluated using real data from the Barmshour Landfill in Shiraz, Iran. The MVO-MLP model achieved the best performance, with coefficient of determination (R2) values of 0.899 (training) and 0.898 (testing), and corresponding RMSEs of 77.60 and 89.44. The results demonstrate that hybrid metaheuristic-neural models can capture complex slope behaviors more effectively than traditional approaches. The primary advancement of this research lies in its systematic comparison of multiple hybrid algorithms and their demonstration of robustness under variable conditions. Practically, the proposed framework provides engineers with a more reliable and adaptive tool for assessing landfill stability and managing geotechnical risks. These findings highlight the growing potential of intelligent hybrid systems to support safer and more data-driven waste management infrastructure.

Interferometric Synthetic Aperture Radar (InSAR) is an advanced imaging geodesy technique for detecting and characterizing surface deformation with high spatial resolution and broad spatial coverage. However, as an inherently post-event observation method, InSAR suffers from limited capability for near-real-time and short-term updates of deformation time series. In this paper, we proposed a data-driven adaptive framework for deformation prediction based on a hybrid deep learning method to accurately predict the InSAR-derived deformation time series and take the Xi’erguazi−Mawo landslide complex (XMLC) as a case study. The InSAR-derived time series was initially decomposed into trend and periodic components with a two-step decomposition process, which were thereafter modeled separately to enhance the characterization of motion kinematics and prediction accuracy. After retrieving the observations from the multi-temporal InSAR method, two-step signal decomposition was then performed using the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and Variational Mode Decomposition (VMD). The decomposed trend and periodic components were further evaluated using statistical hypothesis testing to verify their significance and reliability. Compared with the single-decomposition model, the further decomposition leads to an overall improvement in prediction accuracy, i.e., the Mean Absolute Errors (MAEs) and the Root Mean Square Errors (RMSEs) are reduced by 40–49% and 36–42%, respectively. Subsequently, the Radial Basis Function (RBF) neural network and the proposed CNN-BiLSTM-SelfAttention (CBS) models were constructed to predict the trend and periodic variations, respectively. The CNN and self-attention help to extract local features in time series and strengthen the ability to capture global dependencies and key fluctuation patterns. Compared with the single network model in prediction, the MAEs and RMSEs are reduced by 22–57% and 4–33%, respectively. Finally, the two predicted components were integrated to generate the fused deformation prediction results. Ablation experiments and comparative experiments show that the proposed method has superior ability. Through rapid and accurate prediction of InSAR-derived deformation time series, this research could contribute to the early-warning systems of slope instabilities.

Landslides are complex geological phenomena that pose significant hazards to human life, infrastructure, and the environment. Understanding their mechanisms requires reliable data and advanced analytical methods. Thermal surveys offer valuable insights into surface temperature variations and moisture distribution, supporting the detection of precursory signs of slope instability. Numerical modeling, in turn, enables the simulation of physical processes that control landslide activation and propagation, as well as the prediction of potential landslide-affected zones. This study presents a bibliometric analysis of Scopus-indexed publications from January 2005 to March 2025, focusing on the integration of thermal surveys and numerical modeling in landslide research. The results highlight a steady increase in publications over the past two decades, reflecting growing interest in these innovative approaches. China and Italy are the leading contributors in terms of the number of publications, while Italy achieved the highest citation impact, with 445 total citations. These findings highlight the emerging research trends, showing the potential of combining thermal and thermo-numerical methods to enhance landslide monitoring and mitigation strategies.

Road construction in hilly regions of Indonesia presents complex technical and financial challenges due to extreme topographical variations. This study examines the integration of technical design analysis with the Indonesian Standard Unit Price Method (AHSP) to improve cost estimation accuracy and risk management. Using the Batauga–Sampolawa road project in South Buton Regency as a case study, the research analyzes how longsection and cross-section designs influence earthwork volumes and subsequent cost structures. Results indicate that hilly topography necessitates significant slope stabilization measures and unbalanced cut-fill volumes, directly impacting cost distribution. Through AHSP-based cost projection for 2025, the study identifies critical risk factors including fuel price volatility, material classification uncertainties, and equipment productivity in steep terrain. The research demonstrates that an integrated design-cost approach enables more realistic budgeting and proactive risk mitigation in hilly road projects. Recommendations include the adoption of digital terrain modeling, AHSP adjustment factors for hilly conditions, and contingency planning for geotechnical uncertainties.

Spatial relationships among major geomorphological and archaeological features in the Bosnian Valley of the Pyramids were examined using GIS-based methods. The analysis focused on linear alignments and spiral geometries linking pyramid summits, tumuli, and underground tunnel systems. High-resolution LiDAR data, digital elevation models (DEM), satellite imagery, and GPS-derived coordinates were processed using spatial statistics, regression analysis, and Monte Carlo simulations to determine whether the observed configurations exceed random landscape distributions. The results identify statistically significant linear alignments and a strong correspondence with a Fibonacci-based spiral geometry, with correlation coefficients exceeding R² = 0.99 and probability values below p < 0.01. These geometric patterns were further examined in relation to terrain optimization, slope stability, hydrological flow, and geotechnical constraints. The findings demonstrate that GIS-supported geometric analysis provides a robust framework for investigating large-scale landscape organization and its potential relevance to prehistoric civil engineering and spatial planning