Search Results (5,055)

The concrete industry intends to include HPC for structural applications in Southern Europe and, therefore, Greece is a region characterized by stricter and problem-oriented sizing and reinforcement limitations due to its strong seismicity. For this purpose, the effect of using of high performance concrete (HPC) on the dimensioning of conventional reinforced concrete (RC) and prestressed concrete (PC) structures is investigated by designing two ten-story office buildings and typical PC pedestrian, railway, and road bridges, while conforming to all the relevant provisions of the Eurocodes. The designs involve practical construction forms obeying geometric limitations, while satisfying all serviceability and ultimate limit states for conventional and accidental earthquake loads. Conventional strength-class concrete, namely C30 for buildings and C35 for PC bridges, and HPC concrete classes C60 to C120 are considered. Based on the parametric designs, it is concluded that using HPC leads to a reduction in concrete volume of between 30% and 50% compared to conventional concrete use. This reduction, however, depends strongly on the design-controlling criteria, building occupancy, and bridge type and span, which may lead to smaller material savings, particularly where serviceability criteria govern. The analysis is extended in order to also investigate the increase in the design service life of these structural forms under marine chloride exposure conditions, using C90 and conventional concretes. It is shown that, in addition to the material reductions above, the use of HPC also results in a significant increase in the design service life of the structures considered. Full article

Driver fatigue is a major cause of road accidents, and in-cabin monitoring is increasingly embedded into the Internet-of-Things (IoT) ecosystem of modern vehicles. Deploying such monitoring directly on microcontroller-class devices is challenging: models must fit tight memory and compute budgets, provide reliable confidence estimates, and adapt online to new drivers and conditions. We propose KAN-CLUE, an uncertainty-aware continual TinyML framework for driver fatigue detection from near-infrared periocular images at the IoT edge. KAN-CLUE combines a compact convolutional backbone with a Kolmogorov–Arnold Network (KAN) classification head that outputs Dirichlet-distributed class probabilities and a principled predictive uncertainty measure. A lightweight activation-histogram mechanism provides an additional out-of-distribution (OOD) score, and both signals drive an on-device continual learning scheme that selectively updates a small subset of parameters under a KAN-specific EWC-style regularization. On the ULg DROZY drowsiness database, the quantized KAN-CLUE model uses roughly 167k parameters (about 165 kB in Flash), requires on the order of ${10}^6$ MACs, and achieves around 3.1 ms latency on a Cortex-M–class microcontroller, while reaching 97.7% test accuracy with improved calibration and OOD detection compared with softmax-based TinyML baselines. Full article

As demand for non-motorized travel continues to rise, the underdevelopment of non-motorized lane infrastructure in high-density cities has become increasingly evident, affecting cyclists’ travel experience and safety. Existing cycling environment assessment methods have developed relatively comprehensive frameworks, but they still have difficulty capturing the various disturbances encountered during actual cycling and identifying segment-level problems for targeted interventions. To address these limitations, this study proposes a cycling-data-based framework for non-motorized lane information extraction and rideability assessment. The framework integrates cycling trajectories, first-person cycling videos, urban road networks, and points of interest (POIs) to extract information on road space, facility attributes, pavement conditions, visual environment, and static and dynamic disturbances, and further transforms this information into segment-level rideability assessment indicators. On this basis, an assessment system covering safety, comfort, attractiveness, and accessibility is constructed, and Wuhan is used as an empirical case study. Fuzzy C-means (FCM) clustering is then applied to identify six typical lane types and support differentiated governance strategies. The findings provide practical references for non-motorized lane planning, slow-traffic space improvement, and the management of motorized–non-motorized traffic conflicts. Full article

Passenger car unit (PCU) values are an essential input for traffic capacity assessment (TCA) of intersections, as they allow different vehicle categories to be converted into a common unit. In the Slovak Republic, current technical guidelines use fixed equivalency factors for specific intersection types. However, international research shows that PCU values depend on local traffic conditions, vehicle composition, road geometry, and vehicle interactions. Incorrectly selected factors may therefore lead to inaccurate capacity calculations and misleading conclusions regarding intersection performance. This study analyses PCU values for different vehicle categories, with a focus on heavy vehicles (HV) at roundabouts and turbo roundabouts (TR). Traffic surveys were conducted at selected intersections near industrial areas, where a higher proportion of freight traffic was expected. Manual and semi-automatic turning-movement counts were combined with high-resolution video recordings and automatic traffic counters (ATC) to obtain data on traffic volumes, vehicle composition, travel times, speeds, vehicle lengths, and time headways. The results indicate that the behavior of trucks and HV combinations may differ from the assumptions reflected in static equivalency factors. In several cases, the measured travel times and time headways did not reach the values implied by the prescribed PCU coefficients. Based on these findings, a methodology for dynamically determining PCU values was proposed. The methodology is based on the time headway principle and uses commonly available measurement devices. The proposed approach enables PCU values to be determined for either a simplified two-category vehicle classification or a more detailed classification. It may serve as an alternative to static tabulated values, particularly under non-standard traffic composition, a high proportion of HV, or specific geometric conditions of intersections. Full article

Landslides are among the most destructive geological hazards, posing significant threats to human life, infrastructure, and ecological environments. In this research, to improve the accuracy and reliability of landslide susceptibility assessment, Guangdong Province was selected as the study area, and a multi-source environmental factor dataset incorporating topographic, geological, hydrological, climatic, vegetation, and anthropogenic factors was constructed. Geological factors, including fault distance and seismic point distance, were introduced to characterize the influence of tectonic activities on slope instability. A landslide inventory and a non-landslide sample dataset were established for model training and validation. The Extreme Gradient Boosting (XGBoost) model was employed for landslide susceptibility mapping, and SHapley Additive exPlanations (SHAP) analysis was used to interpret the contribution of different conditioning factors. The results showed that the model achieved an area under the receiver operating characteristic curve (AUC) of 0.8335 on the independent test dataset and a mean AUC of 0.8457 ± 0.0219 for a five-fold stratified cross-validation. The high-susceptibility areas were primarily distributed in the mountainous and hilly regions of northern and eastern Guangdong Province. Vegetation-related variables, road proximity, land-cover type, slope, and distance to coal mines were identified as important contributors to landslide occurrence. This study provides useful references for geological hazard prevention, risk management, and sustainable regional planning. Full article

Reliable traffic sign classification is essential for advanced driver assistance and autonomous-driving systems because recognition errors under real road conditions can directly affect navigation, warning, and safety-related decisions. This study presents a reliability-oriented comparison of BaselineCNN, ResNet18, MobileNetV2, MobileNetV2 enhanced with the Convolutional Block Attention Module (CBAM), and knowledge-distilled MobileNetV2. All models were trained from scratch on a fixed stratified split of the German Traffic Sign Recognition Benchmark (GTSRB) using five independent random seeds. The evaluation considered clean classification performance, training stability, bootstrap confidence intervals, McNemar paired tests, probabilistic calibration, severity-wise robustness under blur, central occlusion, low-light, and Gaussian noise corruptions, external validation on 360 cropped German Traffic Sign Detection Benchmark (GTSDB) signs, computational efficiency, and Grad-CAM-based diagnostic analysis. Across five seeds, ResNet18 achieved the strongest mean clean performance, with an accuracy of 0.9856 ± 0.0093 and macro-F1 of 0.9817 ± 0.0134. MobileNetV2 remained competitive, with an accuracy of 0.9813 ± 0.0057 and macro-F1 of 0.9773 ± 0.0069, whereas BaselineCNN was substantially weaker, with an accuracy of 0.8459 ± 0.0165 and macro-F1 of 0.8384 ± 0.0189. ResNet18 also showed strong calibration, with an expected calibration error of 0.0017, and achieved the best GTSDB macro-F1 of 0.9389 in the representative Seed-42 external evaluation. Severe central occlusion was the most damaging corruption, reducing all models below 0.11 macro-F1, while low-light degradation was comparatively less harmful for the stronger classifiers. The results show that model ranking changes across accuracy, calibration, robustness, external transfer, computational cost, and visual diagnostic behavior. Therefore, traffic sign classifiers should be selected using multi-seed, multi-metric evaluation rather than clean benchmark accuracy alone. Full article

The aim of this article is to demonstrate the significance of Iceland’s Ring Road as a key route providing access to geotourism attractions and to discuss its role in shaping visitor traffic in the context of tourist feedback and the principles of sustainable tourism development. The study is based on an analysis of 223 online reviews concerning the Ring Road, supplemented by the author’s own field observations from travelling around Iceland. Opinions relating to natural and anthropogenic assets, tourist infrastructure, transport accessibility, travel safety, visitor concentration and the interpretation of geological heritage were analysed. The results indicate that the Ring Road is highly rated by tourists, primarily because of the exceptional natural assets located along and near the route. At the same time, the analysis revealed challenges related to traffic concentration at the most recognisable attractions, the uneven quality of tourist infrastructure, high costs, travel safety and the limited use of the educational potential of geosites. The findings show that online reviews, when combined with field observations, can serve as a useful source of knowledge about the practical conditions for the sustainable use of geotourism attractions in popular natural destinations. Full article

With the continuing advancement of global environmental change and rapid urbanization, rural human settlements are facing multiple pressures, including ecological degradation, spatial decline, population outflow, and functional weakening. Based on the vulnerability analysis framework, studies on rural vulnerability provide an important perspective for assessing villages’ risk exposure, disturbance response, and functional degradation when coping with internal and external disturbances. However, existing studies often rely on single-dimensional or linearly weighted evaluations, making it difficult to comprehensively reveal the coupling relationships among multiple discriminative variables and the spatial differentiation patterns of vulnerability. Taking rural areas in Hancheng City, Shaanxi Province, as the research object, this study selects 12 indicators from three dimensions—natural ecological constraints, settlement spatial organization, and public service support—to provide proxy representations of conditions related to potential rural vulnerability. K-means clustering was used to identify potential vulnerability configuration types under multidimensional indicator combinations. A Python-based XGBoost model was then employed as an interpretable surrogate model to assist in characterizing the clustering boundaries, while SHAP analysis was used to explain the key discriminative variables associated with type membership. The results show that the potential rural vulnerability configurations in Hancheng City present a significant west–central–east spatial differentiation pattern. Elevation, village core density, topographic wetness index, distance to town centers, accessibility of daily service facilities, distance to major roads, and normalized difference vegetation index are the main discriminative variables distinguishing different potential vulnerability configuration types. Among them, village core density shows a particularly strong explanatory role. Different key discriminative variables also exhibit evident nonlinear response characteristics across different potential types. Under the indicator system and the $K$ = 4 clustering scheme adopted in this study, the potential rural vulnerability configurations in Hancheng City can be summarized into four types: service-concentrated settlement type, complex terrain-constrained type, human–land coupling transitional type, and natural ecological isolation type. The findings reveal the spatial differentiation characteristics, variable combination relationships, and typological discriminative features of potential rural vulnerability configurations in Hancheng City. They can provide a case-based reference for identifying potential vulnerability, conducting spatial zoning diagnosis, and supporting classified governance in similar county-level rural areas within the loess hilly and gully region. In practical terms, this framework can serve as a diagnostic tool for local governments and planners in classified rural governance. It can be used to identify priority areas for public service and infrastructure investment, review key risk-control areas in complex terrain zones, delineate low-intensity use and protection boundaries in ecologically isolated areas, and guide differentiated resource allocation for different types of villages. Full article

With the rapid development of intelligent connected vehicles and autonomous driving, real-time and accurate road condition perception has become increasingly critical. Aiming at the limitations of traditional direct and indirect detection methods, this paper proposes an ultrasonic-based real-time detection system for road roughness. Most urban roads today feature asphalt pavements; therefore, this system focuses its research on asphalt pavements. Under the same pavement type (asphalt roads), there is a strong correlation between pavement roughness and the friction coefficient. By measuring the roughness of different pavements, the friction coefficient is estimated using the fuzzy processing method. Then the system through measuring ultrasonic echo amplitude and sensor–road distance, combined with software digital filtering, dual-parameter compensation (distance and temperature–humidity), probabilistic statistical analysis, and fuzzy inference, the mapping relationship among echo signals, road roughness and friction coefficient is established. The system mainly includes an ultrasonic transceiver module, a hardware signal conditioning module, and an MCU-based data processing, display and transmission module. Both simulated experiments and real asphalt pavement tests are carried out for verification. The results show that the system can effectively suppress noise, compensate distance attenuation and environmental interference, and achieve accurate real-time detection of road roughness, with a relative error less than 10% compared with the reference value. The proposed system can provide reliable data support for vehicle active safety systems and autonomous driving applications. Full article

Aging and deterioration of urban underground drainage pipelines frequently trigger road collapses, urban waterlogging and groundwater contamination, posing critical challenges to the operation, maintenance and disaster prevention of urban underground infrastructure. Conventional rehabilitation solutions, including cement-based linings and traditional polymer liners, suffer from inherent limitations such as reduced effective flow cross-sections caused by excessive lining thickness, unsatisfactory corrosion resistance and durability, and high construction disturbance. In this study, a modified polyurea (MPU) material was applied to the trenchless rehabilitation of drainage pipelines via spray-applied pipe lining technology. The mechanical properties and interfacial bonding performance of MPU were systematically characterized at the material scale; full-scale external pressure tests were conducted to investigate the effects of 3–8 mm thick MPU linings on the bearing capacity and failure characteristics of structurally damaged concrete pipes; and the anti-seepage repair performance for local perforation defects was evaluated through void-crossing testing. The results demonstrate that MPU lining can meet the engineering performance requirements for pipeline rehabilitation when applied with matched interfacial primer following standard construction procedures. Even the baseline bond strength tested without primer remains sufficient to ensure stable cooperative load bearing between the lining and the host concrete pipe. The 3–8 mm thick linings increase the cracking load of damaged pipes by 61.7–145.7% and the ultimate load by up to 162.2%, while transforming the failure mode from brittle fracture to ductile failure. For local perforation repair, the 3 mm thick MPU lining achieves a critical hydrostatic failure pressure of 1.23 MPa, maintaining favorable structural integrity and interfacial bonding stability under the test conditions. With a well-balanced combination of thin lining thickness, rapid curing and high structural strengthening efficiency, as well as favorable inherent corrosion resistance, the MPU lining provides novel material alternatives and fundamental experimental evidence for the green trenchless rehabilitation of aged underground pipelines and offers technical support for the safe operation and maintenance of urban underground infrastructure. Full article

Ride-hailing global positioning system (GPS) mobile probe data provide flexible urban traffic observations, but their sparse and uneven coverage makes model evaluation difficult because observed targets, valid predictions, and historical input support do not always coincide. This study audits ultra-short-term road-segment speed forecasting under sparse mobile sensing using a mask-consistent support-chain framework. A three-day GPS dataset is aggregated into 5 min speed observations over 1970 road segments and used as a controlled sparse-sensing case study rather than a general-purpose long-term forecasting benchmark. The evaluation protocol distinguishes the full test grid, the set of directly observed target speeds, model-valid prediction support, strict complete-history support, and common-support subsets for coverage-limited baselines. The directly observed target set is used as the primary relaxed support because it retains all verifiable ground-truth targets, while strict and common-support subsets are reported as sensitivity checks. Under this support-conditioned evaluation, the adaptive graph convolutional recurrent network (AGCRN) is associated with lower mean absolute error (MAE) among full-coverage models, the historical mean (HIST_MEAN) baseline is associated with lower root mean squared error (RMSE), and congestion recall remains below 0.24 for all full-coverage deep models. These complementary results indicate conditional and metric-dependent strengths rather than universal model superiority. Because the dataset covers only three consecutive days, weekday/weekend variation, incident-specific fluctuations, seasonal effects, and spatial transferability cannot be fully examined and are treated as limitations. Overall, the findings show that evaluation support should be reported as a first-order experimental factor alongside model accuracy under sparse mobile probe sensing. Full article

Road tunnel fires can produce rapid heat accumulation and severe thermal loading, particularly when fixed firefighting systems are not activated during the early stages of fire development. Although previous tunnel fire studies have examined ventilation effects and individual fire scenarios, only a limited number have quantitatively evaluated the performance of water-mist fixed firefighting systems under substantially different fire intensities using identical tunnel geometry and operating conditions. This gap restricts the ability to assess suppression efficiency across both moderate and severe tunnel fire scenarios. Computational fluid dynamics modelling, particularly the FDS–LES framework, enables controlled comparison of such scenarios that would be difficult, costly, or unsafe to reproduce in full-scale tunnel experiments, while providing detailed information on temperature field development and heat propagation. This study evaluates the influence of a water-mist fixed firefighting system on temperature development and the spatial extent of high-temperature zones in a road tunnel. Numerical simulations were performed in PyroSim using the Fire Dynamics Simulator (FDS) and the Large Eddy Simulation (LES) approach. Four scenarios were analyzed under identical tunnel geometry, ventilation conditions, and operational settings, combining two heat release rates (30 MW and 200 MW) with suppressed and unsuppressed fire conditions. The 30 MW case represented a passenger vehicle or light commercial vehicle fire, whereas the 200 MW case represented a severe heavy goods vehicle fire. The results showed that, in the 200 MW scenario, activation of the fixed firefighting system reduced the maximum temperature from 950 °C to 700 °C (−26%), while in the 30 MW scenario the maximum temperature decreased from 310 °C to 160 °C (−48%). Minimum temperatures were reduced from 550 °C to 200 °C in the 200 MW scenario and from 290 °C to 110 °C in the 30 MW scenario. The water-mist system also limited the propagation of the high-temperature layer beneath the tunnel ceiling, with a more pronounced relative effect under the lower heat release rate. Although complete suppression of the 200 MW fire was not achieved, the system reduced peak temperatures and limited the extent of critical high-temperature zones. The main contribution of this study is the quantitative comparison of water-mist suppression performance under moderate and severe tunnel fire conditions using the same tunnel configuration, which provides practical evidence for assessing peak-temperature reduction, high-temperature zone limitation, and thermal loading mitigation in road tunnel fire safety design. Full article

Infrared (IR) and visible image fusion aims to synthesize a composite representation that integrates the thermal target saliency of IR imagery with the textural richness of visible imagery. Existing deep learning-based methods have achieved promising progress in this field. However, they either operate at the pixel level without semantic priors, or rely on segmentation supervision to obtain such priors. Both approaches limit their practicality and performance in complex scenes. To design a lightweight fusion network that leverages semantic priors without segmentation supervision, we propose SPE2Fusion, a semantic prior-driven fusion network that operates through a dual-stage semantic injection paradigm. Specifically, a lightweight semantic encoder is designed to extract multi-scale scene priors in an end-to-end manner optimized solely by the fusion loss, without requiring segmentation mask annotations. Then, these priors are injected at two complementary stages: the Efficient Semantic Feature Awareness (ESFA) module applies spatially adaptive attention at the encoding stage to amplify semantically salient regions, while the Efficient Semantic Feature Embedding (ESFE) module applies semantically conditioned spatial normalization at the decoding stage to ensure coherent texture reconstruction. Finally, a bidirectional cross-attention fusion block is introduced to integrate complementary cross-modal features under this dual semantic guidance. The network is supervised by a multi-constraint loss combining gradient fidelity, intensity preservation, and structural similarity terms. Comprehensive experiments on the MSRS, LLVIP, and RoadScene benchmarks demonstrate that SPE2Fusion achieves state-of-the-art performance against representative methods (e.g., CrossFuse and DDBFusion), ranking first on four of six metrics on the MSRS test set, specifically EN (6.70), $Q^{AB/F}$ (0.86), AG (6.06), and SD (43.44), while maintaining strong generalization on unseen datasets without domain adaptation. Full article

Reliable quantification of crop evapotranspiration (ET_c) at field resolution is a prerequisite for evidence-based irrigation scheduling in agricultural systems subject to nitrate leaching constraints. This study presents and evaluates a multi-sensor data fusion framework integrating MODIS Terra (500 m, daily) and Sentinel-2 (10–20 m, 5-day revisit) imagery to generate cloud-robust, daily ET_c maps for an 87.5 ha irrigated maize field in Nyírbátor, Hungary, during the 2020 and 2021 growing seasons. Three gap-filling strategies for missing Sentinel-2 NDVI observations were systematically compared: (i) co-regionalisation with cokriging, (ii) local time series interpolation of MODIS pixel centres using ordinary kriging, and (iii) a median time series of cotemporal MODIS pixels—a novel approach developed to suppress sub-pixel spectral contamination from roads and irrigation infrastructure. For field-mean temporal reconstruction, the median approach consistently outperformed the alternatives (adjusted R² = 0.81, NRMSE = 0.15–0.17; pixel-wise correlation 0.70–0.85), effectively filtering heterogeneous landscape artefacts. Daily crop coefficients (K_c) derived from fused NDVI time series via the FAO-56 framework yielded ET_c ranging from 0.99 mm day⁻¹ (initial stage) to 6.40 mm day⁻¹ (peak crop development). Seasonal precipitation–ET_c deficit analyses revealed contrasting patterns: near balance in 2020 versus an 85 mm mid-season deficit at critical nodes in 2021, demonstrating the potential utility of spatially explicit daily ET_c monitoring for irrigation scheduling. These deficit estimates represent irrigation demand indicators; a complete water balance would additionally require measured irrigation volumes, soil water storage changes, deep percolation, and surface runoff data. The methodology provides a proof-of-concept framework for EU Nitrates Directive compliance monitoring, relying solely on freely available satellite data. Independent ET_c validation is required before operational deployment, and transferability to other crops and regions requires validation across contrasting pedoclimatic conditions. Full article

This article discusses the relevant problem of ensuring transport connectivity under the conditions of temporal restrictions of the road network, which arise during repair, communal and emergency operations. It is established that the existing organizational and intellectual methods of traffic management do not eliminate physical decrease in road capacity, while construction of stationary structures with different levels is limited by high costs and long terms of implementation. The above substantiates the need for the development of mobile overpasses as adaptive engineering solutions ensuring continuity of the traffic flows. The purpose of the research is to develop a scientifically substantiated theoretical and experimental methodology for designing a mobile overpass as an integrated system “structure-moving load”, taking into account its dynamic behavior. The paper proposes an integrated approach based on the use of physical similarity theory and dimensionless analysis. A differential equation of dynamic bending of a beam on an elastic foundation is formulated taking into account inertia, damping, base reaction and the effect of a moving mass, and then its nondimensionalization is performed to obtain a similarity criteria system. The scientific novelty of the research consists in developing a system of dimensionless criteria to describe the relationship between the structural, dynamic and operational parameters of a mobile overpass, as well as in the formation of a criterion base for large-scale modeling and transfer of the results to full-scale structures. The proposed methodology describes the mobile overpass as an integrated transport-engineering system accounting for the coupled interaction between the deformable structure, moving traffic load, elastic foundation, and damping effects. Experimental verification was performed on a specially designed stand in the scale 1:4. The results obtained showed the quasi-static nature of the structure performance with moderate damping and rigid base. It is established that the distribution of engineering stresses along the span length has a regular character and retains its shape when the load level changes, which confirms fulfillment of similarity conditions. Regression analysis revealed a close to linear dependence of stresses on the load mass with a high degree of confidence $(R^2\approx 0.995)$. The practical significance of the research consists in creating an engineering method for express design of mobile overpasses, which allows for assessing their stress–strain state, stability and serviceability without expensive full-scale tests. The proposed approach can be used in designing temporary transportation structures under the conditions of urban area, and in operation in areas of road operations and emergency situations. Full article