Search Results (270)

On 20 April 2024, an extreme rainfall event occurred in Jiangwan Town Shaoguan City, Guangdong Province, China, where a historic 24 h precipitation of 206 mm was recorded. This triggered extensive landslides that destroyed residential buildings, severed roads, and drew significant societal attention. Rapid acquisition of landslide inventories, distribution patterns, and key controlling factors is critical for post-disaster emergency response and reconstruction. Based on high-resolution Planet satellite imagery, landslide areas in Jiangwan Town were automatically extracted using the Normalized Difference Vegetation Index (NDVI) differential method, and a detailed landslide inventory was compiled. Combined with terrain, rainfall, and geological environmental factors, the spatial distribution and causes of landslides were analyzed. Results indicate that the extreme rainfall induced 1426 landslides with a total area of 4.56 km², predominantly small-to-medium scale. Landslides exhibited pronounced clustering and linear distribution along river valleys in a NE–SW orientation. Spatial analysis revealed concentrations on slopes between 200–300 m elevation with gradients of 20–30°. Four machine learning models—Logistic Regression, Support Vector Machine (SVM), Random Forest (RF), and Extreme Gradient Boosting (XGBoost)—were employed to assess landslide susceptibility mapping (LSM) accuracy. RF and XGBoost demonstrated superior performance, identifying high-susceptibility zones primarily on valley-side slopes in Jiangwan Town. Shapley Additive Explanations (SHAP) value analysis quantified key drivers, highlighting elevation, rainfall intensity, profile curvature, and topographic wetness index as dominant controlling factors. This study provides an effective methodology and data support for rapid rainfall-induced landslide identification and deep learning-based susceptibility assessment. Full article

Optimisation of the anti-skid properties of tyres is a significant area of composite applications. For investigating the wet slip friction characteristics, the wet slip friction test of tread rubber and road surface was carried out using the comprehensive tire friction testing machine. The wet slip properties of different formulated rubbers under various working conditions such as different slip speeds, water film thicknesses and vertical loads were compared through the test. Subsequently, an orthogonal test programme was designed to investigate the degree of significant influence of each factor on the wet slip performance. A three-dimensional finite element model of tread rubber and road surface with water film was established in order to facilitate analysis of the wet slip properties. The simulation results were utilised to elucidate the pattern of the effects of different loads on the wet slip friction characteristics. Results indicate that the wet slip friction coefficient is subject to decrease in proportion to the magnitude of the vertical load; the friction coefficient of rubber block in wet slip condition exhibits a decline of approximately 26% in comparison with that of dry condition; the factor that exerts the most significant influence on the coefficient of friction is the vertical load, while the water film thickness exerts the least influence. The results obtained can serve as a reference source for the design of tire anti-skid performance enhancement. Full article

Water migration occurs in unsaturated loess subgrade due to extreme rainfall, making it prone to subgrade subsidence and other water damage disasters, which seriously impact road safety and sustainable development of the Loess Plateau. The study performed a rainfall test using a compacted loess subgrade model based on a self-developed water migration test device. The effects of extreme rainfall on the water distribution, wetting front, and infiltration rate in the subgrade were systematically explored by setting three rainfall intensities (4.6478 mm/h, 9.2951 mm/h, and 13.9427 mm/h, namely J1 stage, J2stage, and J3 stage), and a lateral water migration model was proposed. The results indicated that the range of water content change areas constantly expands as rainfall intensity and time increase. The soil infiltration rate gradually decreased, and the ratio of surface runoff to infiltration rainfall increased. The hysteresis of lateral water migration refers to the physical phenomenon in which the internal water response of the subgrade is delayed in time and space compared to changes in boundary conditions. The sensor closest to the side of the slope changed first, with the most significant fluctuations. The farther away from the slope, the slower the response and the smaller the fluctuation. The bigger the rainfall intensity, the faster the wetting front moved horizontally. The migration rate at the slope toe is the highest. The migration rate of sensor W3 increased by 66.47% and 333.70%, respectively, in the J3 stage compared to the J2 and J1 stages. The results of the model and the measured data were in good agreement, with the R² exceeding 0.90, which verifies the reliability of the model. The study findings are important for guiding the prevention and control of disasters caused by water damage to roadbeds in loess areas. Full article

This study explores the design and implementation of a control system integrating the anti-lock braking system (ABS) with frequency-modulated continuous wave (FMCW) radar technology to enhance safety and performance in autonomous vehicles. The proposed system employs a hybrid fuzzy logic controller (FLC) and proportional-integral-derivative (PID) controller to improve braking efficiency and vehicle stability under diverse driving conditions. Simulation results showed significant enhancements in stopping performance across various road conditions. The integrated system exhibited a marked improvement in braking performance, achieving significantly shorter stopping distances across all evaluated surface conditions—including dry concrete, wet asphalt, snowy roads, and icy roads—compared with scenarios without ABS. These results highlight the system’s ability to dynamically adapt braking forces to different conditions, significantly improving safety and stability for autonomous vehicles. The limitations are acknowledged, and directions for real-world validation are outlined to ensure system robustness under diverse environmental conditions. Full article

Combustion processes in compression ignition engines lead to the inevitable generation of nitrogen oxides, which cannot be limited to the currently desired levels just by optimising the in-cylinder processes. Therefore, simulation-based engine development needs to include all engine-related aspects which contribute to tailpipe emissions. Among them, the SCR (selective catalytic reduction) aftertreatment-related processes, such as urea–water solution injection, urea decomposition, mixing, NOx catalytic reduction, and deposits’ formation, are the most challenging, and require as much attention as the processes taking place inside the cylinder. Over the last decade, the urea-SCR aftertreatment systems have evolved from underfloor designs to close-coupled (to the engine) architecture, characterised by the short mixing length. Therefore, they need to be tailor-made for each application. This study presents the CFD-based development of a multi-platform SCR system with a short mixing length for mobile non-road applications, compliant with Stage V NRE-v/c-5 emission standard. It combines multiphase dispersed flow, including wall wetting and urea decomposition kinetic reaction modelling to account for the critical aspects of the SCR system operation. The baseline system’s design was characterised by the severe deposit formation near the mixer’s outlet, which was attributed to the intensive cooling in the mounting area. Moreover, as the simulations suggested, the spray was not appropriately mixed with the surrounding gas in its primary zone. The proposed measures to reduce the wall film formation needed to account for the multi-platform application (ranging from 56 to 130 kW) and large-scale production capability. The performed simulations led to the system design, providing excellent UWS–exhaust gas mixing without a solid deposit formation. The developed system was designed to be manufactured and implemented in large-scale series production. Full article

In response to the increasing frequency of urban rainstorms, this study focuses on investigating the friction coefficient related to pedestrian instability under urban road flooding conditions. The objective is to conduct an in-depth analysis of the friction coefficient between pedestrians and the ground in actual flood scenarios and its variations, providing practical data to support future pedestrian safety assessments under flood conditions. Wet friction coefficient experiments were conducted under waterlogged conditions, with real human subjects tested across various operational scenarios. A buoyancy calculation formula was introduced to explore the impact of pressure changes caused by buoyancy on the human body in water, influencing the friction coefficient. An exponential relationship between pressure and the friction coefficient was established. Furthermore, by considering factors such as outsole hardness, ground type, and pressure variations with water depth, a dynamic method for selecting the friction coefficient was proposed, offering a scientific basis for determining friction coefficient thresholds associated with pedestrian instability risks. Experimental results indicate that, in the combination of hydrophilic materials with experimental asphalt and cement pavements, the friction coefficient under waterlogged conditions is generally higher than under dry conditions. However, as pressure increases, the friction coefficient of rubber materials decreases. This study concludes that the selection of the friction coefficient in pedestrian instability analysis should be treated as a dynamic process, and relying on a fixed friction coefficient for force analysis of pedestrian instability may lead to significant inaccuracies. Full article

The permeability of porous asphalt pavements is a critical skid resistance indicator that directly influences driving safety on wet roads. To ensure permeability (water infiltration capacity), it is necessary to assess the degree of clogging in the pavement. This study proposes a permeability evaluation model for porous asphalt pavements based on 3D laser imaging and deep learning. The model utilizes a 3D laser scanner to capture the surface texture of the pavement, a pavement infiltration tester to measure the permeability coefficient, and a deep residual network (ResNet) to train the collected data. The aim is to explore the relationship between the 3D surface texture of porous asphalt and its permeability performance. The results demonstrate that the proposed algorithm can quickly and accurately identify the permeability of the pavement without causing damage, achieving an accuracy and F1-score of up to 90.36% and 90.33%, respectively. This indicates a significant correlation between surface texture and permeability, which could promote advancements in pavement permeability technology. Full article

The present study examines the application of four machine learning models—Multi-Layer Perceptron, Naive Bayes, Credal Decision Trees, and Random Forests—to assess landslide susceptibility using Mei County, China, as a case study. Aerial photographs and field survey data were integrated into a GIS system to develop a landslide inventory map. Additionally, 16 landslide conditioning factors were collected and processed, including elevation, Normalized Difference Vegetation Index, precipitation, terrain, land use, lithology, slope, aspect, stream power index, topographic wetness index, sediment transport index, plan curvature, profile curvature, and distance to roads. From the landslide inventory, 87 landslides were identified, along with an equal number of randomly selected non-landslide locations. These data points, combined with the conditioning factors, formed a spatial dataset for our landslide analysis. To implement the proposed methodological approach, the dataset was divided into two subsets: 70% formed the training subset and 30% formed the testing subset. A correlation analysis was conducted to examine the relationship between the conditioning factors and landslide occurrence, and the certainty factor method was applied to assess their influence. Beyond model comparison, the central focus of this research is the optimization of machine learning parameters to enhance prediction reliability and spatial accuracy. The results show that the Random Forests and Multi-Layer Perceptron models provided superior predictive capability, offering detailed and actionable landslide susceptibility maps. Specifically, the area under the receiver operating characteristic curve and other statistical indicators were calculated to assess the models’ predictive accuracy. By producing high-resolution susceptibility maps tailored to local geomorphological conditions, this work supports more informed land-use planning, infrastructure development, and early warning systems in landslide-prone areas. The findings also contribute to the growing body of research on artificial intelligence-driven natural hazard assessment, offering a replicable framework for integrating machine learning in geospatial risk analysis and environmental decision-making. Full article

Vehicle collisions with wild animals are a significant cause of wild animal mortality. This impacts their population and spatiotemporal distribution within the ecosystem. Data on the impact of road kills on wild animals, particularly in the south-central part of Uganda, are not present. This study aimed to investigate the number of species involved in road kills in South-Central Uganda and their spatial and temporal distribution within South-Central Uganda. Three transects, each 40 km in length, were surveyed. In both wet and dry seasons, surveys were conducted monthly in the morning and afternoon from November 2019 through April 2024. The findings showed that 161 wildlife–vehicle accidents were detected within a four-and-a-half-year period, with 178 animal species involved. These incidents belonged to 12 mammals, five reptiles, two amphibians, and 32 avian families. Our study adds to a better understanding of the impact of roads on wildlife in Africa and is an essential starting point regarding conservation efforts to mitigate these effects. It provides a first summary of species that are frequently found as roadkill in this area of south-central Uganda. This acts as a reference point for future studies. Full article

Habitat degradation poses a critical threat to the Malabar slender loris (Loris lydekkerianus malabaricus), yet little is known about its microhabitat requirements in intact forest. In Aralam Wildlife Sanctuary, we combined nocturnal trail surveys (337 loris sightings) with plotless sampling of 2830 trees (86 species from 35 families) to characterize both vegetation structure and loris presence. Our results show that lorises occur almost exclusively in mildly degraded wet evergreen and secondary moist deciduous subcanopies, where understory trees and climber networks provide continuous pathways. Individuals are most often encountered at heights of 5–15 m—ascending into higher strata as the night progresses—reflecting a balance between foraging access and predator avoidance. Substrate analysis revealed strong preferences for twigs ≤ 1 cm (36.98%) and small branches 2–5 cm in diameter, oriented obliquely to minimize energetic costs and maintain stability during slow, deliberate arboreal locomotion. Day-sleeping sites were overwhelmingly located within dense tangles of lianas on large-girth trees, where intertwined stems and thorny undergrowth offer concealment from both mammalian and avian predators. Vegetation surveys documented a near-equal mix of evergreen (50.6%) and deciduous (49.4%) species—including 26 endemics (18 restricted to the Western Ghats)—with Aporosa cardiosperma emerging as the most abundant riparian pioneer, suggesting both ecological resilience and potential simplification in fragmented patches. Complementing field observations, our recent habitat-suitability modeling in Aralam indicates that broad-scale climatic and anthropogenic factors—precipitation patterns, elevation, and proximity to roads—are the strongest predictors of loris occupancy, underscoring the interplay between landscape-level processes and microhabitat structure. Together, these findings highlight the imperative of multi-strata forest restoration—planting insect-hosting native trees, maintaining continuous canopy and climber networks, and integrating small “mini-forest” modules—to recreate the structural complexity vital for slender loris conservation and the broader resilience of Western Ghats biodiversity. Full article

The conservation of biodiversity and the balance between ecological and societal needs are critical but often contested global issues. Wildlife-vehicle collision (WVC) on vital infrastructure, especially linear infrastructure, remains a persistent challenge from policy to practice and poses a serious life-threatening implication to humans and other non-human lives. Addressing this issue effectively requires solutions that provide win-win outcomes from both ecological and societal perspectives. This study critically analyzes a decade of roadkill incidents along Nepal’s longest East-West national highway, which passes through a biologically diverse national park in the western Terai Arc Landscape Area (TAL). Findings are drawn from field-based primary data collection of the period 2012–2022, secondary literature review, key informant interviews, and spatial analysis. The study reveals significant variations in roadkill incidence across areas and years. Despite Bardia National Park being larger and having a higher wildlife density, Banke National Park recorded higher roadkill rates. This is attributed to insufficient mitigation measures and law enforcement, more straight highway segments, and the absence of buffer zones between the core park and adjacent forest areas—only a road separates them. Wild boars (Sus scrofa) and spotted deer (Axis axis), the primary prey of Bengal tigers (Panthera tigris tigris), were the most frequently road-killed species. This may contribute to human-tiger conflicts, as observed in the study areas. Seasonal trends showed that reptiles were at higher risk during the wet season and mammals during winter. Hotspots were often located near checkpoints and water bodies, highlighting the need for targeted mitigation efforts such as wildlife crossings and provisioning wildlife requirements such as water, grassland, and shelter away from the regular traffic roads. Roadkill frequency was also influenced by forest cover and time of day, with more incidents occurring at dawn and dusk when most of the herbivores become more active in search of food, shelter, water, and their herds. The findings underscore the importance of road characteristics, animal behavior, and landscape features in roadkill occurrences. Effective mitigation strategies include wildlife crossings, speed limits, warning signs, and public education campaigns. Further research is needed to understand the factors in driving variations between parks and to assess the effectiveness of mitigation measures. Full article

The goal of this study was to develop a GIS-based Decision Support Model for selecting the best timber harvesting systems on steep terrain. The model combines multiple layers, each representing an important factor in mechanized logging. These layers are used to create a final map that functions as a spatially explicit Decision Support Model that helps decide which machines are best suited for different forest areas. A key idea of this study is to consider not only operational criteria (slope, ruggedness, wetness, and road accessibility), but also a fundamental silvicultural aspect, i.e., the assessment of tree growth classes to enable the integration of silvicultural deliberations into timber harvest planning. The data used for this model come from orthophoto image and a Digital Terrain Model (DTM). The operational factors were analyzed using GIS tools, while the silvicultural aspects were assessed using the deep learning algorithm DeepForest and tree growth equations (allometric functions). The model was tested by comparing its results with field data taken in a Norway Spruce stand in South Tyrol/Italy. The findings show that the model reliably evaluates operational factors. For silvicultural aspects, it tends to underestimate the number of small trees, but provides a good representation of tree size classes within a forest stand. The innovation of this method is that it relies on low-cost, open-source tools instead of expensive 3D scanning devices. Full article

Accurately identifying road conditions, particularly wetness, is crucial for ensuring road safety and enhancing vehicle performance. We conducted road surface classification and road wetness estimation using state-of-the-art deep learning models in this study. Raspberry Pi Model 4 was used to classify road surfaces and estimate road wetness. SqueezeNet, a lightweight convolutional neural network, was used to recognize wet and dry road surfaces with an accuracy of 90%. The ENet model, known for its efficiency in semantic segmentation tasks, was used to estimate the degree of wetness, categorizing roads into damp, wet, and very wet roads. The ENet model showed an accuracy of 90.48%. The efficiency of the deep learning models in road surface wetness monitoring was validated using a confusion matrix created with the margin classifier. A total of 300 images per category were used for training, amounting to 1200 in total. A total of 20 testing images were used for road surface classification and 21 for road wetness estimation. The results highlighted the robustness and applicability of SqueezeNet and ENet models in estimating diverse environmental road conditions. Full article

A four-steering-wheel heavy-duty Automated Guided Vehicle (AGV) is prone to lateral instability and wheel slippage during acceleration, climbing, and small-radius turns. To address this issue, a trajectory tracking strategy considering lateral stability and an optimal driving torque distribution strategy considering load transfer and tire adhesion coefficient are proposed. Firstly, a three-degree-of-freedom AGV trajectory tracking model is established, tracking error and sideslip angle are incorporated into the cost function, and an improved model predictive trajectory tracking controller is proposed. Secondly, the longitudinal and yaw dynamic model of AGV is established, and vertical load transfer is analyzed. With the goal of minimizing tire adhesion utilization rate, quadratic programming is used for the optimal distribution of driving torque. Finally, through co-simulation using ADAMS and MATLAB on a narrow “climbing straight+ S-curve” road, the maximum tracking error is 0.0443 m. Compared to the unimproved model predictive control and average driving torque distribution strategy, the sideslip angle is reduced by 58.18%, the maximum tire adhesion utilization rate is reduced by 6.62%, and climbing gradeability on wet roads is enhanced. Full article

Understanding soil properties’ spatial and temporal variability is essential for optimizing road construction and maintenance practices. This study investigates the seasonal variability of soil properties along a 4.8 km roadway in Maiduguri, Nigeria. Using a novel integration of network analysis and geotechnical testing, we analyzed nine soil parameters (e.g., particle size distribution (PSD), Atterberg limits, California bearing ratio) across wet (September 2024) and dry (January 2021) seasons from 25 test stations. Average Atterberg limits (LL: 22.8% wet vs. 17.5% dry; PL: 18.7% wet vs. 14.7% dry; PI: 4.2% wet vs. 2.8% dry; LS: 1.8% wet vs. 2.3% dry), average compaction characteristics (MDD: 1.8 Mg/m³ wet vs. 2.1 Mg/m³ dry; OMC: 12.3% wet vs. 10% dry), and average CBR (18.9% wet vs. 27.5% dry) were obtained. Network construction employed z-score standardization and similarity metrics, with multi-threshold analysis (θ = 0.05, 0.10, 0.15) revealing critical structural differences. During the wet season, soil networks exhibited a 5.0% reduction in edges (321 to 305) and density decline (1.07 to 1.02) as thresholds tightened, contrasting with dry-season networks retaining 99.38% connectivity (324 to 322 edges) and stable density (0.99). Seasonal shifts in soil classification (A-4(1)/ML wet vs. A-2(1)/SM dry) underscored moisture-driven plasticity changes. The findings highlight critical implications for adaptive road design, emphasizing moisture-resistant materials in wet seasons and optimized compaction in dry periods. Full article