Search Results (127)

Introduction: Pedestrians lying on the road—collapsed through medical emergency, intoxication, or displacement following a prior collision—represent a disproportionately lethal and underaddressed category in road traffic safety. Forensic database analyses derived from Japan’s national police records document a fatality rate of 33.0% for collisions involving pedestrians lying on the road, more than double the rate for upright pedestrian collisions. Standard Advanced Driver-Assistance Systems (ADAS) yield a True Positive Rate (TPR) of only 21.4% for detecting pedestrians lying on the road under night conditions—a classification gap of 73.3 percentage points. Methods: In simulation trials, we evaluated the Advanced Falling Object Detection System (AFODS—where “falling object” denotes the low-profile human form at road level, distinguishing the prone pedestrian from the upright postures addressed by conventional ADAS) on a composite dataset of 3200 annotated fall events and 12,000 negative samples (training/validation), with 320 independent controlled simulation trials used for performance evaluation, spanning real-world, forensic-reconstruction, and Total Human Body Model for Safety (THUMS)-validated synthetic scenarios. No physical prototype has been evaluated; all performance data are derived from simulation, and 37.5% of positive samples are synthetically generated. These simulation conditions represent a first feasibility demonstration pending real-world hardware validation. This paper introduces three original contributions absent from prior work: a three-stage quantitative injury-risk model, a formal ISO 26262 Hazard Analysis and Risk Assessment (HARA), and a medicolegal SHAP interpretability framework. The injury-risk model translated detection latency via impact velocity to Head Injury Criterion (HIC) and estimated fatal injury probability (AIS ≥ 5); these model outputs should be interpreted as exploratory estimates pending ATD validation. Reporting follows principles consistent with the TRIPOD statement. Results: Under clear daytime conditions, AFODS demonstrated a TPR of 98.2% (95% CI: 97.4–98.8%) in simulation, decreasing to 95.6% under night dry-road conditions and 89.4% under night rain. The system achieved an AUC of 0.981 and a mean end-to-end latency of 46.5 ms, representing a 76.8 percentage-point improvement in simulation over the monocular RGB baseline (p < 0.001). The injury-risk model projects a reduction in estimated fatal head injury probability from 66.2% (Monte Carlo mean) (no detection, 50 km/h full-speed impact) to 0.7% under AFODS worst-case night/rain conditions, and to ≈0% under clear daytime simulation conditions. Conclusions: A 73.3 percentage-point classification gap places pedestrians lying on the road outside the effective detection envelope of current ADAS, compounded by the systematic exclusion of non-upright postures from regulatory test protocols and benchmark datasets. AFODS supports proof-of-concept feasibility under simulation conditions. Three translational steps are required: prototype validation on real-world hardware using instrumented Anthropomorphic Test Devices (ATDs); prone-posture biomechanical injury modelling using HIC and BrIC criteria; and regulatory extension of pedestrian AEB test standards to non-upright scenarios. Full article

Forestry is recognized as one of the most dangerous industries in the world. To enhance forestry safety, autonomous machinery and safety systems for such machinery are essential. This study aims to introduce large language models (LLMs)—especially their extensions to images, vision–language models (VLMs)—to enable human-like decision-making for autonomous forest machinery. This research focused on VLMs as an active safety system that can adapt to environments and evaluated the effectiveness of a system that quantitatively makes decisions regarding hazard levels using contrastive language–image pretraining (CLIP). The results of industry type, tree state, and road state classification using pretrained models showed that for three tasks—forestry identification, hung-up tree detection, and road collapse sensing—the target classes consistently exhibited higher similarity with disaster texts compared with nontarget classes. Although the F1 scores were 0.693, 0.324 and 0.634, respectively—indicating that the system is insufficient as a direct active safety system—the application of a similarity threshold optimized to maintain a recall of 0.9 yielded F1 scores of 0.291 and 0.584 for tree state and road state, respectively. These results suggest that the system can potentially be used as a quantitative indicator of hazard by setting a threshold on the similarity score. Full article

Compacted loess is widely used as road subgrade filling in northwestern China, but its stability is threatened by traffic loads and repeated dry–wet cycles, leading to subgrade settlement or collapse. This study investigated the compression and resistivity characteristics of Q₃ Malan loess under 0–3 dry–wet cycles by incremental loading (IL) and constant rate of strain (CRS) tests. A self-developed consolidation chamber was used for the IL and CRS tests with the simultaneous monitoring of deformation and resistivity, with the moisture content controlled within the range of 1% to 29% to 15%. The results showed that loess compressibility increased rapidly after the first dry–wet cycle and became slow after other dry–wet cycles; The primary compression index C_c and secondary compression index C_α rose as vertical stress increased, and C_α stabilized at a vertical stress larger than 200 kPa. Resistivity decreased with stress and cycles, and sharply decreased after the first cycle (enhanced pore connectivity) and stabilized after two to three cycles, matching the compression stages. The compression and resistivity characteristics obtained by IL and CRS tests had consistent variation rules, confirming the reliability of the tests. This study provides a preliminary laboratory theoretical basis for exploring the feasibility of using resistivity in subgrade deformation monitoring. Full article

To address the application requirements of dynamic simulation for sudden deep pavement potholes, this study presents an automated cover plate control device that integrates concealment, rapid response, and high load-bearing capacity, thereby overcoming the inherent contradiction between “portable yet weakly load-bearing” and “highly load-bearing yet inflexible” that has long limited conventional cover plate solutions. The core of the device comprises a cover plate mechanism consisting of a UHPC–Q235 composite cover plate, a distributed truss, and specially configured connecting rods, together with a winch hoisting mechanism, a hydraulic locking and rapid-release mechanism, and an embedded steel frame structure. Together, these modules realize a complete operational cycle of “closed load-bearing support → hydraulic release → gravity-driven rotation → winch reset.” Theoretical analysis and experimental measurements demonstrate that hydraulic release can be accomplished within 0.5 s, the cover plate can form a standard collapse pothole of 2000 mm in diameter within approximately 1 s, and a single cycle requires approximately 11 s, thereby faithfully reproducing the dynamic process of sudden pavement collapse. Refined mechanical design and ABAQUS finite element simulations verify that under the most adverse loading conditions, the stress in all structural components remains below the material design strength limit, with clear and reliable load transfer paths maintained in all operational states. The integrated camouflage design achieves over 95% visual and tactile similarity to the existing pavement surface, meeting the design requirement of concealment under normal conditions. The proposed device offers a high-fidelity physical simulation solution for autonomous vehicle perceptual training under emergent road hazards and for roadway safety assessment. Full article

In emergency response, dispatch speed and trauma-center activation depend on accurate severity classification. Current classifiers face two problems: extreme class imbalance and a semantic gap that leaves numerical models blind to textual severity cues. Resampling methods adjust class distributions but add no new information, while LLM-based hybrids exhibit feature dilution, where numerical priors override semantic reasoning. We propose SAFE (Semantic-Augmented Fusion Ensemble), a framework that routes features through parallel branches: XGBoost for numerical data and a Small Language Model for text. Structured records are enriched into narratives with severity-predictive keywords. The branches merge through class-adaptive probability fusion, governed by an analytically derived condition that preserves minority-class detections against majority-biased priors. On the US Accidents dataset and UK road accident records, Severe Recall rises from 30.7% (RF + SMOTE) to 91.2%, with overall accuracy reaching 83.3%; Serious Recall reaches 54.5% against 33.8% (XGBoost + SMOTE-ENN) on UK data. Keyword enrichment is essential: its removal collapses recall regardless of model size. SAFE enables severity-aware triage using only structured records that transportation agencies already collect. Deployment efficiency remains practical. SAFE achieves 188.4 ms mean per-sample latency at 5.3 samples/s on consumer hardware (Qwen3-4B INT8, 6.41 GB memory footprint), supporting operational batch classification of incident records. Full article

Road disasters such as subsidence and bridge failures pose severe threats to traffic safety. Existing warning distance calculation methods typically assume homogeneous traffic flow and overlook the spatial heterogeneity of vehicle responses across different vehicle types, limiting their applicability for geospatial early warning systems. This paper proposes a dynamic warning distance model that integrates mixed-traffic flow composition—comprising human-driven vehicles (HDVs), Level 2 advanced driver-assistance system vehicles (ADASVs), and automated vehicles (AVs) of Level 3 and above—within a geospatial risk propagation framework. The model introduces vehicle-type weighting coefficients to quantify response differences, incorporates interaction delays calibrated through SUMO microsimulations, and accounts for cascading reaction delays caused by abrupt HDV braking. The methodology is illustrated using a counterfactual reconstruction of the 2024 Meizhou–Dapu Expressway collapse in China (52 fatalities). Based on reconstructed traffic conditions (80% HDVs, 15% ADASVs, 5% AVs; average speed 27.5 m/s; flow 1800 veh/h), the calculated dynamic warning distance is 153 m, which is 12% shorter than the speed-matched conventional stopping sight distance of 174 m (computed under consistent wet-pavement assumptions). Sensitivity analyses reveal that warning distance decreases substantially with increasing AV penetration (to 42 m in AV-dominated scenarios, a potential reduction of up to 74% compared with the HDV-dominated baseline, provided that residual HDVs are supported by V2X-based alerting) and varies monotonically with traffic flow, demonstrating the model’s adaptive capability. The proposed framework provides a theoretical foundation for adaptive geospatial disaster warning strategies and offers practical guidance for infrastructure development in the era of mixed-traffic automation. Full article

Existing flood risk models have improved predictive accuracy, but they prioritize natural and hydrological factors while giving limited attention to fine-grained urban morphology. This study develops an interpretable deep learning framework to examine how high-resolution, three-dimensional urban form shapes two dimensions of flood risk: inundation risk, measured by grid-level inundated area, and infrastructure risk, measured by flood-related disruptions, including water supply interruption, power outage, road blockage, and collapse-related damage. Using Zhengzhou, China, as a case study, we combine multi-source spatial data, convolutional neural networks, ablation analysis, SHAP interpretation, and Gaussian Mixture Model classification to examine how fine-grained urban morphology affects these two risk dimensions. Incorporating urban morphology improved inundation risk prediction, reducing MSE from 0.0431 to 0.0371. The improvement was greater for infrastructure risk, with accuracy increasing from 0.7327 to 0.8218, and ROC-AUC from 0.83 to 0.95. SHAP results show that inundation risk is associated with vegetation, elevation, hydrological proximity, and localized spatial disorder, whereas infrastructure risk is amplified by vertical intensity, imperviousness, building concentration, porosity, and shape. Spatially, very high infrastructure-risk areas accounted for only 2.30% of the city but 12.88% of the central districts, while 74.62% of very high infrastructure-risk zones were concentrated in dense mid- to high-rise morphology. These findings suggest that flood-resilient planning should move beyond hydrology-sensitive flood management toward morphology-sensitive planning. Full article

As a complex structural form capable of simultaneously bearing upper-deck highway traffic, lower-deck highway traffic, and rail transit, the curved chord stiffened double-deck continuous steel truss bridge is distinct from traditional single-deck bridges. The spatial superposition of multiple traffic types within this structure may result in multiple components approaching their critical states concurrently. Despite prior research efforts on this structural type, the failure evolution process from local yielding to global collapse under mixed traffic loads remains ambiguous. This study addresses these questions through systematic numerical investigation of a nine-span bridge with a 300 m main span. A two-stage analytical approach is employed: a Midas/Civil analysis first identifies critically stressed regions, then ABAQUS multi-scale modeling enables refined analysis of critical components while maintaining computational efficiency. Twenty-nine combined traffic loading cases encompassing dual- and triple-category configurations are systematically analyzed. The results show that the ultimate load-carrying capacity coefficients range from approximately 7 to 18, with a minimum of 7.137, and the dual-level highway combinations exert greater influence than road–rail combinations. More importantly, three failure path convergence characteristics were discovered. First, the initial failure position under each working condition tends to be consistent, initiating at the lower chord near the top of the mid-span pier, which confirms that inherent structural defects exist at this location. Second, the gusset plate at the top of pier W6 appears as the second failure location in 48% of cases and ranks within the first four locations across all cases. Third, path similarity progressively increases with traffic diversity. Additionally, Q370qE steel exhibits 5–22% stress exceedance with variable critical locations depending on traffic conditions. Based on these convergence characteristics, a safety monitoring scheme is proposed: monitoring points need to be arranged symmetrically on both sides of the bridge on the top chords, bottom chords, web members, and wedge plates near the tops of the piers. Full article

Surface deformation is characterized by long-term accumulation and significant spatial differences, commonly inducing ground fissures and structural damage to roads and buildings, and in severe cases, causing collapses and other accidents that directly threaten human life. Reliable deformation monitoring and prediction are of great significance for early warning and infrastructure safety. Synthetic aperture radar interferometry (InSAR) technology can be used to acquire high-spatiotemporal-resolution temporal observations of surface deformation over a large area, but research on surface deformation prediction using InSAR temporal images is still relatively limited. Therefore, in this study, we used Sentinel-1 temporal imagery as the data foundation. Firstly, small baseline subset (SBAS)-InSAR inversion was used to obtain the line-of-sight-oriented cumulative deformation sequence and subsidence rate results. Based on this, the causal multi-trend fusion patch-to-point (CMTF-P2P) surface deformation prediction framework was developed. This framework effectively separates the long-term trends and short-term fluctuations in the deformation sequence through causal decomposition; introduces external drivers such as rainfall and event phase characteristics to enhance the temporal expression; and utilizes patch-to-point fusion of neighborhood spatial information to improve the spatial continuity and the ability to characterize local differences. Experimental results show that the method has an RMSE of 0.43 mm and an R² of 0.92, outperforming time-series deformation prediction models such as LSTM and iTransformer. Compared with traditional models that learn overall change patterns from historical time series, this paper alleviates the confusion between trends and volatility using causal STL decomposition, making the model’s expression of long-term trends and seasonal and short-term fluctuations in volatility clearer; event phase encoding enhances the model’s ability to characterize sudden disturbances and phased responses to events; the patch-to-point structure incorporates spatiotemporal information within the neighborhood, enhancing the model’s ability to apply spatiotemporal information; multi-branch collaborative modeling enhances the model’s ability to characterize multi-scale temporal features; and incremental cumulative consistency constraints enhance the physical consistency of the prediction results. Full article

A central open question in automotive intrusion detection is not merely whether relational representations of Controller Area Network (CAN) traffic improve performance, but which aspects of CAN traffic structure transfer robustly across attacks and which do not transfer across vehicle platforms, and why. To investigate this question systematically, we develop a lightweight intrusion-detection framework combining statistical traffic descriptors, structural identifier transition features, and graph topology representations extracted from sliding windows of CAN frames. Because CAN is a broadcast-only bus with no request–response mechanism, each ECU independently transmits its identifiers at fixed periodic rates; accordingly, the structural and graph-based features capture the temporal scheduling regularity of identifier broadcasts, not directed inter-ECU communication dependencies. Stress-testing the framework under cross-attack and cross-dataset transfer reveals a clear four-level hierarchy: (1) statistical features collapse under cross-attack transfer (ROC-AUC as low as 0.009), failing to generalise beyond the attack type seen during training; (2) structural transition features are the most robust form of representation, maintaining high cross-attack performance (ROC-AUC > 0.999) across all evaluated scenarios within the same vehicle platform; (3) graph topology features are scenario-dependent, achieving high robustness in DoS-trained scenarios but producing sub-random results in Fuzzy-trained scenarios, exposing a sensitivity to injection density profiles; and (4) the hybrid combination provides the strongest overall operational package, consistently across four classifiers. Cross-dataset transfer to the ROAD dataset reveals the precise boundary conditions of transferability: structural representations transfer only when an attack perturbs identifier transition regularity (correlated signal attacks, ROC-AUC = 0.81–0.83), while attacks that affect only payload semantics (speedometer) or exploit identifier–space novelty (fuzzing) lie outside the detection scope of transition-based features, regardless of the vehicle platform. A vehicle-specific calibration experiment further shows that the correlated-attack generalization gap can be closed with as little as 10% of target-vehicle normal traffic, whereas speedometer attacks remain structurally invisible by design. A key contribution of this work is therefore a transparent approach for identifying when relational CAN representations transfer and when they do not—a finding that is more scientifically valuable than a uniformly optimistic performance claim and which provides concrete guidance for practitioners designing cross-platform automotive IDS. Full article

Assembled H-shaped steel strut (AHSS) has been widely applied in deep excavation projects. In this study, the collapse failure of AHSS C1 in a deep excavation project in China was investigated. The collapse of C1 was directly attributed to the settlement of its supporting columns in the mid-span, which was triggered by a nearby pit bottom leakage through an exploration borehole. Then the implementation of the emergency measures and reconstruction works were introduced. Theoretical and numerical pre-assessments confirmed that the reconstructed C1 exhibited adequate safety for strength, in-plane stability and out-of-plane stability, with all steel components and bolts within their safe limits. The good working performance of reconstructed C1 was finally verified through the monitoring results (i.e., strut axial force, soil horizontal displacement, column vertical displacement, road settlement and building settlement) of the foundation pit during the subsequent soil excavation and basement construction. This study is believed to provide references for future excavation projects using AHSS with similar risks. Full article

For electric vehicles, accurate real-time estimation of the road friction coefficient is critical for maintaining stability, as the millisecond-level response of electric motors and the integration of regenerative braking demand higher perception fidelity than traditional internal combustion vehicles. This paper proposes a methodological framework for road friction estimation specifically designed for intelligent E-Chassis based on micro-signal features of intelligent tires and deep learning. An intelligent tire system, integrated with tri-axial accelerometers and strain gauges, was installed on the front-left wheel of a test vehicle to capture raw dynamic signals during transitions from cement to snow-covered surfaces across a velocity gradient of 10–50 km/h. The Savitzky–Golay convolutional smoothing algorithm was applied to reconstruct the high-frequency raw signals, enabling the extraction of a five-dimensional feature vector comprising vehicle velocity, peak strain, contact patch width, peak-to-peak acceleration, and signal standard deviation. The study revealed a natural filtering effect originating from the porous elastic properties of snow, resulting in a 60–70% reduction in signal standard deviation compared to cement, accompanied by a cliff-like feature collapse at the moment of snow entry. A BP neural network model with a 5-7-1 architecture achieved an identification accuracy of 96.2% on the test set, facilitating a rapid real-time prediction of the friction coefficient transitioning from 0.75 to 0.23. Unlike traditional methods, the proposed approach does not rely on high slip ratios and can complete identification within the first physical rotation cycle. This provides a robust physical criterion for the torque vectoring and regenerative braking stability of intelligent electric vehicles in extreme environments. Full article

Urban road collapses are characterized by sudden occurrence and strong spatial heterogeneity, posing substantial challenges for proactive infrastructure management. Susceptibility mapping can provide spatially explicit evidence to support targeted inspection and early-warning strategies. Using Futian District, Shenzhen (China) as a case study, a total of 315 road collapse events recorded during 2019–2023 were compiled to develop an integrated framework for urban road collapse relative susceptibility mapping based on multi-source remote sensing and urban spatial data. First, an indicator-based susceptibility index (SI) was constructed using eight conditioning factors, including PS-InSAR-derived deformation, topographic–hydrological conditions, and distance-based infrastructure variables (distance to underground utilities, metro lines, and roads). Factor weights were determined by coupling the Analytic Hierarchy Process (AHP) with the Entropy Weight Method (EWM), producing a comprehensive SI for historical collapse locations. Subsequently, a set of 17 remote-sensing predictors, including Sentinel-2 spectral bands, Sentinel-2 GLCM texture features, and Sentinel-1 SAR backscatter variables, was used to train a Random Forest model to predict SI and generate continuous susceptibility maps at the urban road-network scale. The influence of neighborhood window size on predictive performance was systematically evaluated. Results show that the Random Forest model performed best at the 5 × 5 window scale (R² = 0.70, RMSE = 0.0172, MAE = 0.0122), outperforming both pixel-based inputs (1 × 1) and larger windows. Uncertainty analysis further indicated that the 5 × 5 RF configuration yielded the most stable and spatially coherent predictions, whereas overly small windows and less robust learners produced more fragmented or higher-uncertainty susceptibility patterns. Spatiotemporal analysis indicates that susceptibility patterns remained broadly stable from 2019 to 2023, with moderate susceptibility accounting for 50.82–57.89% and high susceptibility for 21.94–23.30%, while very high susceptibility consistently remained below 1%. Overall, this study demonstrates that integrating multi-source remote sensing with scale-optimized machine learning provides an effective approach for fine-scale susceptibility mapping of urban road collapses, offering practical guidance for differentiated monitoring and risk prevention along critical road corridors. Full article

Fire represents a relatively infrequent but potentially severe hazard for bridges, with collapse rates comparable to or exceeding those caused by seismic events. Despite this, fire risk is often neglected in bridge design and assessment, particularly for existing infrastructures in urban contexts. Beyond collapse, fire can induce significant post-event consequences, including material degradation, serviceability loss, traffic disruption, and economic and social impacts. Existing studies highlight the influence of bridge material, fire scenario, and traffic characteristics—especially the presence of fuel tankers—on damage severity. In this context, this paper proposes a rapid fire-risk assessment methodology applicable to large bridge stocks. The approach adapts and modifies existing methods from the literature, integrating them into the multi-risk framework defined by the Italian Guidelines for existing bridges, where fire is not explicitly addressed. The methodology is specifically adapted to urban and suburban bridges and European roadways, validated through its application to a stock of 30 bridges along the Palermo ring road. The results enable the classification of bridges by fire risk, supporting infrastructure Authorities in prioritizing detailed assessments and intervention strategies on the most vulnerable bridges. Multi-risk assessment considering the fire–seismic risk is also addressed, by adopting a simplified seismic risk approach consistent with the Italian Guidelines for existing bridges and comparing it with internationally accepted methods, particularly the North American HAZUS system. Results show that accounting for the actual condition and deterioration of bridges leads to higher seismic risk classes, more consistent with the fire risk assessment procedure proposed. In contrast, expedited methods such as HAZUS, which neglect maintenance conditions, may underestimate seismic risk. Full article

In many densely populated towns and semi-urban areas, masonry buildings often stand close to busy roads, exposing them to blasts from improvised explosives or other localized sources. Such structures are rarely designed to resist sudden explosive forces, making severe damage or even progressive collapse likely. Even moderate-intensity blasts can weaken walls, endanger occupants, and cause significant property loss. Unlike reinforced concrete, masonry is highly susceptible to explosive impact. Therefore, understanding how these buildings behave under blast loads and developing affordable protection methods is crucial. Low-rise unreinforced masonry (URM) structures, usually up to about 13 m in height (roughly 2–4 stories), common in villages, semi-urban regions, and conflict-prone zones, are particularly at risk. In many areas, these poorly constructed buildings lack proper engineering design and are therefore highly vulnerable to blast damage. Non-load-bearing internal dividers and perimeter enclosures are especially prone to lateral displacement, which can initiate instability and, in severe cases, lead to overall structural failure. This research focuses on reducing catastrophic damage in URM walls when exposed to close-proximity blast forces using concrete-based protective coatings, both with and without embedded steel-welded wire mesh. The study references a previously tested laterally supported clay brick wall built with cement–sand mortar as the baseline model, with its behavior validated against experimental findings from existing literature. Two blast cases were considered corresponding to scaled stand-off distances of 2.19 m/kg^1/3 and 1.83 m/kg^1/3, representing moderate flexural-shear cracking and full structural failure, respectively. To replicate the observed behavior, a comprehensive 3D numerical simulation was developed using the ABAQUS/Explicit 2020 solver. The model’s predictions were benchmarked and verified through comparison with reported test data. While both blast intensities were used to confirm computational accuracy, the effectiveness of UHPC and UHPFRC protective coatings with and without embedded wire mesh was specifically evaluated under the more severe collapse scenario (Z = 1.83 m/kg^1/3). Results indicated that at a scaled distance of 1.83 m/kg^1/3, the uncoated URM wall could not withstand the blast because of poor tensile and bending capacity. In contrast, the UHPC- and UHPFRC-coatings provided improved confinement and better stress distribution. When welded wire mesh was embedded, crack control improved further, the interface bond strengthened, and a larger portion of blast energy was absorbed and dissipated. Full article