Search Results (1,676)

The valorisation of industrial by-products as supplementary cementitious materials is a promising strategy to reduce clinker consumption and improve the sustainability of cement-based materials. In this study, the influence of submerged arc welding (SAW) slag on the hydration behaviour and microstructural evolution of cement pastes was investigated. Two SAW slags from different industrial sources were incorporated as partial replacements of ordinary Portland cement at 5%, 15%, and 30% by mass. Cement pastes were prepared with water-to-binder ratios of 0.3 and 0.4 and characterised through setting time, water demand, mercury intrusion porosimetry (MIP), differential scanning calorimetry (DSC), and X-ray diffraction (XRD). The results showed that SAW slag systematically delayed both initial and final setting times, while having only a negligible effect on water demand. Under the fixed mix conditions adopted in this study, this retardation is interpreted as the combined effect of clinker dilution and modified fresh-state conditions. MIP analysis revealed higher early-age porosity in SAW-containing pastes, particularly at high replacement levels and higher water-to-binder ratios, although mixtures with up to 15% slag approached the reference pore structure at later ages. Thermal analysis indicated lower bound water and portlandite contents at early ages, mainly due to clinker dilution, while long-term hydration development remained comparable at moderate replacement levels. At higher slag contents, some mixtures showed higher calcium carbonate contents, suggesting a tendency toward increased carbonate formation under the investigated conditions. Overall, the results indicate that SAW slag primarily affected early paste behaviour and pore structure development, with clinker dilution appearing to be the main mechanism, although weak secondary physical or chemical contributions cannot be completely excluded. Full article

To investigate the mechanical properties of coarse-grained soil in high-altitude mountainous areas, experimental research was conducted to explore the shear process, shear modulus, shear dilation, and stress axis rotation of coarse-grained soil under varying conditions. The sensitivity and mechanisms of these factors were also analyzed. The results indicate that increasing water content and fine particle content significantly diminish the strain-hardening characteristic, whereas dry density and normal stress augment this effect. Under shear stress, the samples exhibit pronounced non-uniform shear dilatancy. Elevated water content, fine particle content, and normal stress enhance shear contraction at the rear of the sample while suppressing shear dilation at the front. In contrast, dry density produces the opposite effect. The rotation of the stress axis initially follows a nonlinear growth pattern before transitioning to linear growth. The growth rate and ultimate rotation angle increase monotonically with water content, fine particle content, and normal stress but decrease with increasing dry density. Additionally, the shear modulus decreases exponentially with increasing water content and increases exponentially with dry density, fine particle content, and normal stress. Ultimately, normal stress is identified as the most sensitive factor, followed by dry density and fine particle content, with water content being the least sensitive. These findings can provide geotechnical parameters and a theoretical basis for the scientific prevention of high-altitude geological hazards. These findings can provide indoor mechanical parameters and deformation laws of coarse-grained soils for engineering. Full article

Driver behavior is a critical factor in road safety, contributing to the majority of traffic crashes. The i-DREAMS project introduced the concept of a Safety Tolerance Zone (STZ) to enhance driving safety through real-time and post-trip interventions. This study develops and evaluates three hybrid machine learning models—(i) Deep Neural Network–Random Forest (DNN-RF), (ii) Convolutional Neural Network–Long Short-Term Memory (CNN-LSTM), and (iii) Recurrent Neural Network–AdaBoost (RNN-AdaBoost)—to classify risky driving behavior into three safety levels using naturalistic driving data from Belgium and the UK. The dataset includes 69 drivers, 15,389 trips, and 265,512 min of driving data. Among the models tested, the DNN-RF model demonstrated the highest accuracy, reaching 98% in Belgium and 97% in the United Kingdom, outperforming other approaches. Feature importance analysis identified harsh acceleration and braking as the most critical factors in Belgium, while total trip distance and harsh acceleration were predominant in the UK. To enhance model transparency, we applied the Local Interpretable Model-agnostic Explanations (LIME) algorithm, providing valuable insights into model predictions. The findings support the potential of hybrid deep learning models in improving road safety by accurately detecting risky driving behaviors. These insights can inform targeted interventions and driver assistance technologies to mitigate crash risks and promote safer driving practices. Full article

Expressway interchange guide signs must support rapid route decisions under limited viewing time, high information load, and potential vehicle occlusion. This study developed a human-centered framework for optimizing guide-sign information and presentation in complex expressway networks by integrating an on-road field experiment, driving simulation, eye-movement measures, physiological responses, and driving behavior. The field experiment involved ten drivers unfamiliar with the Fuzhou expressway network and yielded 119 valid sign-reading episodes. A separate simulation experiment with 60 licensed drivers examined character heights of 55, 65, and 75 cm and roadside, median-side, and dual-side installation under three ordinal traffic background conditions. Drivers generally initiated visual search in the upper-left region of sign panels and focused on route-relevant place names and directional arrows. Descriptive observations suggested that usable control-point information could support route inference when the destination was absent, whereas the absence of both destination and usable control-point information was accompanied by more sustained deceleration and more dispersed visual search. Effective viewing distance increased across the tested character-height conditions. At a design speed of 120 km/h and an assumed recognition time of 2.6 s, the required viewing distance was approximately 86.7 m. The mean viewing distance for the 55 cm condition was below this requirement, whereas the 65 and 75 cm conditions exceeded it. The findings were applied to Xiuzhai Interchange. In a dynamic screen-based route-choice test, 24 of 25 participants made acceptable choices. Overall, guide signs in complex networks should be designed as coordinated information systems integrating control-point hierarchy, information continuity, character height, and installation position. Full article

Graded aggregates used in high-speed railway subgrades often undergo micro breakage (i.e., corner and edge spalling) under cyclic loading, whereas the mechanism linking local particle spalling to contact-network evolution and permanent deformation remains unclear. To address the lack of a physically based micro breakage criterion and a continuous local shape-updating scheme in existing DEM approaches, this study proposes an energy-driven micro breakage method. The method uses the relationship between contact elastic energy and fracture energy for newly created free surfaces as the breakage criterion. It also employs a radial function representation to simulate local particle shape evolution. The proposed method is subsequently validated through multilevel tests and shows reasonable agreement with the experimental results for particle micro breakage and the associated mechanical responses. Furthermore, dynamic triaxial simulation results show that micro breakage exhibits a distinct cumulative characteristic and increases with loading frequency and amplitude. Meanwhile, micro breakage drives particle rearrangement and new contact formation, leading to skeleton densification and weakening of the strong force-chain network, which in turn accelerates plastic deformation accumulation. These coupled processes constitute an important mechanism governing the progressive degradation of graded aggregates under cyclic loading. This study clarifies the associated particle-scale mechanism and provides a reference for performance optimization. Full article

The increasing demand for sustainable road infrastructure has encouraged the development of asphalt mixtures incorporating recycled materials and industrial by-products. This study developed and validated a highly circular AC14 asphalt surface mixture incorporating steel slag aggregates (SSA) and reclaimed asphalt pavement (RAP). The laboratory-designed mixture contained 63.8% SSA and 17.2% RAP, corresponding to 81.0% secondary materials, or 83.0% when recovered filler is included. Its volumetric and mechanical performance was compared with that of a conventional AC14 surface mixture with natural aggregates. The highly circular formulation was then produced in an asphalt plant and applied in a full-scale field trial. A life cycle assessment (LCA), following EN 15804:2012+A2:2019, and a production-stage cost analysis were conducted using plant-specific data. The highly circular mixture showed improved rutting resistance, higher stiffness modulus, very high resistance to water damage, and better fatigue indicators than the conventional reference mixture. The field trial supported its feasibility under real production and construction conditions. The LCA showed reductions in 12 of the 13 product-stage environmental impact indicators, including reductions of 18.1% in total global warming potential, 26.6% in abiotic depletion potential for fossil resources, 77.6% in abiotic depletion potential for minerals and metals, and 81.5% in water deprivation potential. The estimated production-stage unit price was 36.4% lower than that of the conventional mixture and 45.4% lower than the Portuguese market benchmark. These results demonstrate the technical, environmental, and economic potential of highly circular asphalt surface mixtures incorporating SSA and RAP. Full article

Highway–rail grade crossing (HRGC) safety has improved substantially over recent decades through engineering upgrades, active warning systems, crossing closures, enforcement, and public education. Recent national trends, however, suggest that these gains may have slowed, raising the question of whether HRGC safety has entered a persistent plateau. This study investigates whether the historical decline in U.S. HRGC incidents has transitioned into a statistically distinct safety regime and whether the factors associated with casualty occurrence have changed following that transition. An analytical framework integrating regime-transition analysis, cross-regime casualty comparison, and explainable machine learning was applied to nationwide Federal Railroad Administration incident records from 1976 to 2025. Trend analysis, complementary stationarity diagnostics, residual diagnostics, information criteria, and sensitivity analysis consistently identified 2012 as the onset of a statistically stationary safety plateau. Comparison of casualty outcomes showed no meaningful change in either the probability of casualty occurrence or the distribution of injury and fatality outcomes following the transition. Explainable random forest models further demonstrated substantial temporal stability in the factors associated with casualty occurrence. Train speed, vehicle occupancy, driver presence, and highway-user actions remained the dominant predictors across both safety regimes, with driver presence ranking among the most influential characteristics during the plateau period. These findings indicate that the current safety challenge is not the emergence of new collision mechanisms but the persistence of well-established operational and behavioral risk factors. Future reductions in HRGC casualties will likely require targeted engineering improvements, advanced warning technologies, connected-vehicle and vehicle-to-infrastructure systems, artificial intelligence-enabled monitoring, and focused public education to address the persistent residual risks sustaining the national safety plateau. Full article

Urban streetworks are necessary for infrastructure maintenance and renewal, but they may generate local disruptions that are difficult to observe systematically. This study develops a reproducible public-data-based workflow for prioritizing urban streetwork closure days according to their risk of nearby infrastructure-related 311 service requests (municipal non-emergency reports submitted by residents or other street users). The case study uses New York City data from 2022 to 2024 and integrates recorded street closures, selected 311 complaints, and daily weather variables. A closure day is defined as a recorded street closure active on a single calendar day. The main target identifies whether at least one selected 311 request occurred in the vicinity of an active closure on the same day. The final model is a weather-informed, calibrated Random Forest. The final dataset contained 2,708,620 closure day records. The model achieved an ROC-AUC of 0.6408, a PR-AUC of 0.0799, and a Brier score of 0.0459. Among the top 5% highest-risk closure days, precision reached 11.48%, with a lift of 2.35 over random selection, relative to a 2024 test prevalence of approximately 4.88%. Permutation importance showed that location and calendar features dominated prediction, while continuous weather variables provided a smaller but measurable contribution. Full article

The design of curved surface sliders hides some pitfalls that can affect the effectiveness of the isolation system, as pointed out by past theoretical and experimental studies. In this paper, the basic relationships of curved surface sliders are rewritten, defining a characteristic parameter. This relates the design parameters, i.e., the friction coefficient, the equivalent radius, and the seismic displacement, with each other but depends on the damping ratio only. As is well known, a high value of friction could prevent the onset of motion. The self-centering capacity depends on the equivalent radius. Finally, the seismic displacement affects the dimensions and, therefore, the material consumption and the required gap. The design procedure proposed here is organized in two main phases. In the first one, the feasibility of a base isolation system, consistent with the fixed building seismic capacity and maximum displacement, is analyzed. An admissible area, i.e., the couples of values of the effective period and damping ratio, is individualized on the acceleration–displacement (capacity) spectrum plane. To do that, the outcomes of a previous study are used. In the second phase, the iso-R and iso-μ curves are plotted in this area. These allow a suitable choice of the design parameters and, therefore, a performance-oriented design. A flowchart summarizes the method. Finally, some examples show practical applications and allow the verification of the effectiveness of the proposed procedure. Full article

As an innovative structural system aligned with construction industrialization, prefabricated Steel-Reinforced Concrete (SRC) structures are characterized by high load-bearing capacity, efficient material utilization, and rapid construction. In this study, the mechanical behavior, failure mechanisms, and ductility characteristics of a prefabricated SRC multi-floored grain warehouse frame were investigated through quasi-static cyclic loading tests. To complement the experimental program, high-fidelity numerical models were developed using Abaqus, incorporating concrete plastic damage and steel material nonlinearity. The simulation results were rigorously validated against the experimental data. The findings indicate that the specimens exhibited typical shear failure modes with full hysteretic loops, demonstrating substantial energy dissipation capacity (equivalent viscous damping coefficient of 0.261). Notably, the results of the parametric study indicate that the integration of wall panels can significantly increase the load-bearing capacity and lateral stiffness of the frame system. The ductility of the samples was excellent, with displacement ductility coefficients ranging from 3.1 to 3.7. The ultimate inter-story drift angles at failure (1/49–1/38) substantially exceeded the code-specified limit (1/50), indicating robust collapse-prevention capacity. The numerical results strongly agreed with the experimental observations in terms of the hysteretic behavior, failure patterns, and skeleton curves, confirming the reliability of the modeling strategy for subsequent seismic performance analyses and parametric evaluations. Full article

Maintaining the geometric quality of railway tracks is essential for safe and efficient operations. This study examines the prediction of the Track Quality Index (TQI) using Multiple Linear Regression (MLR) and Random Forest (RF) models, based on inspection data from the mountainous Kolashin–Podgorica railway section. Data collected from 2017 to 2022 were used for model development, while 2024 data served as an independent validation set. The primary contribution is a high-resolution analysis of 20-m homogeneous track units across multiple years, combined with independent validation. The dataset includes precise measurements segmented into 20-m units, covering infrastructure, geometric, operational, and maintenance variables. Model variables were selected for their relevance to track quality and data completeness. Both models were trained on scaled input data and evaluated using the coefficient of determination (R²), mean absolute error (MAE), and root mean square error (RMSE). The RF model outperformed MLR, achieving a higher R² (0.69 versus 0.57) and an MAE approximately 15% lower. Furthermore, the RF model identified severe localized degradation trends and demonstrated robust screening capability, achieving 86.4% sensitivity in detecting high-risk track segments requiring urgent intervention. These findings highlight the effectiveness of ensemble machine learning methods in reducing prediction errors and enabling proactive, data-driven track maintenance in complex railway networks. This methodology allows railway engineers to identify segments most susceptible to rapid deterioration, supporting more precise scheduling of tamping, renewals, or other targeted interventions. Model outputs can assist managers in prioritizing maintenance activities, optimizing resource allocation, and minimizing unexpected failures, thereby enhancing safety and cost efficiency in daily operations. However, the study is limited by its focus on a single railway corridor and a relatively short observation period with few maintenance events, which may restrict the generalizability of the findings to other lines or maintenance regimes. Full article

Floating Car Data (FCD) are increasingly used to analyse mobility patterns and support transport-system management, but the evidence remains fragmented across heterogeneous applications, sensing technologies, and data-processing methods, particularly regarding road-infrastructure monitoring. This study presents a systematic literature review and structured evidence map of FCD research published between 2010 and 2025. Following PRISMA methodology, Scopus and Google Scholar were searched using the exact expression “floating car data”. The search retrieved 2127 records; after bibliographic harmonisation, duplicate removal, title-and-abstract screening, full-text retrieval, and eligibility assessment, 165 publications were included. The studies were classified through a top-down framework covering application domain, sensing technology, processing approach, validation method, geographical region, deployment scale, and integration with Pavement Management Systems (PMSs). Traffic-state estimation and mobility-planning applications covered 100 publications (60.6%), whereas infrastructure monitoring was the primary domain in 20 studies (12.1%). GPS or GNSS data were used in 128 publications (77.6%), while accelerometers, gyroscopes, or inertial measurement units were reported in 29 studies (17.6%). Only 15 publications (9.1%) described operational or real-time deployment, and explicit PMS-oriented integration was identified in only 9 studies (5.5%). The findings show that FCD research is methodologically mature for traffic and mobility applications but remains comparatively fragmented for pavement-condition assessment. The review therefore proposes an operational pathway linking accelerometric data acquisition, preprocessing, normalization, fleet-level aggregation, validation, data fusion, and PMS decision-making. These results highlight that the principal research gap concerns not sensor availability, but the development of standardized, transferable, and operationally validated frameworks for network-wide pavement monitoring. Full article

In southern Kazakhstan, landslide control is highly relevant for settlements located in areas with mountainous terrain. This study was conducted to observe the results of applying electrical, seismic and magnetic surveys to investigate a landslide-prone slope in the Almaty Region. The aim was to improve the reliability of geophysical data when assessing the state of the landslide body. For the first time for this landslide, based on a joint analysis of geoelectric and velocity characteristics, the structural heterogeneities associated with unconsolidated and water-saturated soils, as well as fractures, were identified. The combination of data on the elastic, electrical and magnetic properties of the studied medium significantly improved the clarity of the interpretation of geophysical data when studying the landslide massif. This made it possible to refine the internal structure of the landslide and identify areas with an increased likelihood of deformation. The obtained results provide a reliable basis for assessing slope stability and demonstrate the need for integrated geophysical surveys to reduce the risk of landslides. Along with engineering–geological methods, geophysical surveys will be an integral component of monitoring landslide-prone areas, which will enable timely implementation of organizational measures to protect infrastructure facilities. Full article

To accurately predict temperature rise due to the concrete hydration heat released from the #0 segment of a continuous concrete girder bridge at an early construction stage for structural design purposes, researchers proposed an approach incorporating empirical predictive formulae with a preliminary numerical analysis. However, due to the uniqueness of the structural geometry and material in each engineering case and the limited data shared by the whole engineering community, no universal predictive empirical model for temperature rise due to hydration heat has yet been identified for practical use that can be applied to a variety of different projects. Moreover, the preliminary numerical analyses are usually based on questionable assumptions and simplifications of the physical truth, the accuracy of which also requires further validation. To this end, the present research measured the time-varying temperature samples of early age massive concrete in the #0 segment of Huangsha Harbor Bridge (a twin-deck three-span continuous concrete box girder bridge located in Jiangsu Province, China) and examined the accuracy of the predictive empirical models formulated by other researchers and the usability of a numerical modal established on a commercial finite element (FE) platform by comparing the corresponding results with the data from the present field measurements. The results suggest that the empirical formulae proposed can generally effectively describe the actual temperature distribution patterns related to the thermal issue, but they are characterized by inferior usability in some cases. In addition, the present comparison also indicates that the actual maximum temperature rise can be correctly predicted by the preliminary FE analysis in most cases. Full article

Bridge health assessment and deterioration prediction are essential for traffic safety and maintenance planning. However, conventional bridge evaluation still relies heavily on expert judgment and heuristic rules, limiting objectivity and long-term forecasting capability. This study proposes a physics-constrained multi-task learning–semi-Markov framework for bridge condition assessment, multi-year deterioration forecasting, and risk-aware maintenance prioritization. Guided by the Highway Bridge Technical Condition Rating Code, the proposed model jointly predicts health grade, defect type, and severity from inspection item/defect entry records, improving assessment robustness through cross-task information sharing. The predicted health states are then incorporated into a physics-constrained semi-Markov model to forecast bridge deterioration over a three-year horizon, and the current and predicted states are further used for maintenance prioritization. Experiments on real bridge inspection data show that the proposed model achieves test accuracies of 95.20%, 99.67%, and 99.72% for health grade, defect type, and severity, respectively, with a Cohen’s kappa of 0.872, while the proposed semi-Markov model achieves a three-year prediction accuracy of 95.4% and a weighted accuracy of 99.1%. Comparative and ablation studies further demonstrate the effectiveness of the proposed framework for bridge lifecycle management. Full article