Search Results (272)

The rapid deterioration of road infrastructure requires accurate and efficient methods for detecting pavement distresses. Unmanned Aerial Vehicles (UAVs) have emerged as a reliable alternative to conventional inspection techniques, enabling high-resolution data acquisition and improved operational safety. This study investigates the application of the Smart Oblique Capture (SOC) technique for pavement inspection through a systematic calibration of UAV flight parameters, including Ground Sample Distance (GSD), frontal and lateral overlap, camera tilt angle, and flight pattern. A structured experimental campaign was conducted, comprising 135 parameter combinations evaluated across three independent scenarios, resulting in a total of 405 UAV flights. The analysis focused on assessing the impact of these parameters on the visual quality of two-dimensional pavement reconstructions and processing efficiency. The results show that a configuration consisting of a 0.5 cm/pixel GSD, 70% frontal overlap, 80% lateral overlap, and a 70° camera tilt angle achieves the best balance between reconstruction quality and computational cost. Furthermore, the findings indicate that Smart Oblique Capture does not provide a statistically significant improvement in reconstruction quality for linear infrastructure compared to conventional oblique configurations, despite requiring a higher number of images and longer processing times. Overall, the results demonstrate that flight parameter calibration plays a more critical role than the adoption of advanced acquisition strategies such as Smart Oblique Capture. This study provides practical and reproducible guidelines for UAV-based pavement inspection, supporting efficient data acquisition while minimizing redundant information and unnecessary computational costs in infrastructure monitoring workflows. Full article

The preventive maintenance (PM) of asphalt pavements reduces life-cycle costs and minimizes resource consumption compared with reactive rehabilitation, yet its cost-effectiveness is highly sensitive to application timing. This study develops a data-driven framework for determining optimal PM timing on Korean expressways by integrating network-level pavement management system (PMS) deterioration modeling with life-cycle cost analysis (LCCA). Using 10-year PMS time-series data from approximately 2200 asphalt pavement sections (2012–2021), a nonlinear regression of the Highway Pavement Condition Index (HPCI) yielded an exponential deterioration model with exponent β = 1.87 (R² = 0.996), confirming accelerating deterioration beyond a critical service age. The HPCI inflection coincides with the Grade-2 boundary (3.5–4.0), where surface distress growth—dominated by linear cracking (91.3% of total SD)—also peaks. A LCCA across 44 scenarios demonstrated that PM applied immediately before this acceleration onset minimizes the 40-year net present value (NPV; discount rate 4.5%). The optimal first PM application time was estimated at 10.8 years (≈56% of the 19.3-year average service life), reducing the 40-year NPV by up to 7 million KRW per section relative to the milling and overlay baseline (up to 16 million KRW in absolute NPV terms for concrete overlay sections). These findings provide a quantitative, reproducible basis for PM timing decisions applicable to the approximately 4000 km of expressway pavement managed by Korea Expressway Corporation. Full article

The fast drainage of surface water from road pavements is essential to ensure both driving safety and adequate infrastructure service life. For close-graded asphalt mixtures, surface runoff relies on sufficient longitudinal and transverse slopes that convey water toward hydraulic drainage devices. However, construction defects, surface distress, or inadequate placement of drainage systems may compromise this process and reduce pavement durability. When water infiltrates beneath the wearing course and saturates the underlying layers, heavy traffic loads can accelerate deterioration through erosion, pumping, interlayer delamination, and subgrade overstress. This work investigates the joint use of Ground Penetrating Radar (GPR) and Mobile Laser Scanning (MLS) to evaluate drainage deficiencies and detect signs of layer delamination in bituminous pavements. A highway section in Salerno (Italy) was selected as a case study due to known hydraulic-related issues. MLS data were used to reconstruct pavement geometry and model surface runoff patterns, while GPR surveys assessed the condition of the bonding between asphalt and base layers. The results revealed ineffective runoff management and identified multiple areas affected by delamination, confirming a relationship between surface drainage behaviour and subsurface damage. These findings highlight the broader potential of the integrated GPR–MLS framework as a scalable and transferable approach for proactive drainage assessment and structural monitoring in pavement management practices. Full article

This study investigates the influence of key parameters—vehicle speed, weight, loading lane, and pavement roughness—on the Dynamic Amplification Factor (DAF) and ride comfort of a beam–arch composite continuous rigid-frame bridge under vehicle–bridge coupling. A six-span bridge is analyzed using a spatial beam-element model in ANSYS and a typical three-axle vehicle model is adopted to conduct the coupled dynamic response analysis. Based on the modal and structural characteristics of this bridge, key response indices are selected, including vertical displacement and bending moment at midspan, longitudinal displacement and bending moment at pier top, arch crown displacement, and tensile force in the long hanger. Control sections are identified in Span 4 (midspan, arch crown, long hanger) and at the top of Pier 16. The results demonstrate that pavement roughness significantly influences ride comfort, with the root mean square (RMS) value varying up to 107%, whereas the loading lane shows a negligible effect. Vehicle speed effects are divided into two distinct regimes: at 60 km/h and within 70–90 km/h, with dynamic responses in the higher speed range approximately 22% greater. Increasing vehicle weight raises the peak dynamic response by up to 77.68%, but does not lead to a proportional increase in DAF. Transverse loading eccentricity has a more pronounced impact on vertical bridge responses (>20% change) than on longitudinal responses (<10% change). Deterioration in pavement roughness elevates both dynamic response and DAF, with maximum increases reaching 27.97% and 28%, respectively. Full article

Several highway agencies have implemented full-depth reclamation (FDR) as a sustainable technology for rehabilitating deteriorated asphalt pavements. However, the lack of standardized mix design procedures and limited field assessment, in terms of rutting and cracking resistance, pose challenges to the widespread implementation of FDR. This study addresses these challenges by synthesizing current FDR mix design and construction practices and validating highway agency-recommended practices through laboratory performance evaluation. The study objectives were achieved by (1) reviewing current FDR mix design and construction specifications of highway agencies across the US and internationally, (2) conducting surveys with highway agencies and interviews with subject matter experts (SMEs), and (3) evaluating the laboratory performance of FDR mixtures. Based on the findings from the literature, survey responses, and SME interviews, three FDR mixtures were designed in the lab: (i) cement-only, (ii) asphalt emulsion and cement, and (iii) foamed asphalt and cement. Each mix was then evaluated for rutting susceptibility using the Asphalt Pavement Analyzer (APA) and cracking resistance using the indirect tensile (IDT) test to identify optimum dosages of bituminous and cementitious additives. Laboratory results showed that FDR mixtures with 3% asphalt emulsion and 1% cement improved rutting resistance by 46% and cracking performance by 70% compared to cement-only mixtures with 4% cement. In contrast, foamed asphalt did not result in a significant improvement in FDR performance. Survey responses indicated that 89% of respondents reported good field performance of FDR, with Pennsylvania and North Dakota exhibiting excellent performance 10 years after construction. Full article

Under continuous erosion of dynamic water pressure generated by vehicle–water–pavement coupling interaction, asphalt mixture will gradually deteriorate and severe moisture damage finally emerges. The fine aggregate mixture (FAM) component is notably eroded and stripped, while the aggregate component even cracks sometimes. Sufficient attention has not been paid to these critical phenomena. This study employed the 3D laser scanning technique to detect changes in surface roughness of the asphalt mixture before and after it was eroded by dynamic water pressure. The degree of erosion of the asphalt mixture, FAM component, and aggregate component were thereby evaluated. The influences of experimental parameters such as water temperature and pore water pressure magnitude, as well as variable parameters including lithology and asphalt type, were also taken into account. By integrating the detection of physical and mechanical properties evolution of aggregates, the mechanism of moisture damage was comprehensively illustrated from the perspectives of both components of FAM and aggregate. The findings revealed that the 3D laser scanning technique could clearly detect and quantitatively assess the morphological changes on the asphalt mixture surface after been eroded in dynamic water pressure. Both types of asphalt mixtures exhibited varying degrees of erosion and wear, and obvious increases in surface unevenness were observed in each case. Variations in either temperature or pore water pressure magnitude showed limited influence on moisture damage in basalt-based asphalt mixture. In contrast, moisture damage sustained by limestone-based asphalt mixture was notably sensitive to temperature changes but remained largely insensitive to fluctuations in pore water pressure magnitude. The increase in surface roughness of asphalt mixture was primarily attributed to the scouring action of dynamic water pressure, which removed the FAM component surrounding coarse aggregate particles. Degradation in coarse aggregate particles would lead to the deterioration of the entire asphalt mixture. The compatibility between the stripping rate of FAM component and the deterioration rate of coarse aggregate governed the macroscopic manifestation of overall moisture damage in the asphalt mixture. Full article

This study quantifies a critical winter safety hazard caused by lateral heterogeneity of skid resistance: under non-uniform snow and ice removal, the friction coefficient in edge lanes and near barrier guardrails can be 2–5 times lower than in the central part of the carriageway, creating conditions prone to loss of control during braking and lane changes. Field measurements of friction coefficient and macrotexture were conducted on highways of different technical categories with asphalt concrete and cement concrete pavements in Kazakhstan’s continental climate. Long-term monitoring showed that, over three years of operation, texture peak height decreases by 22–33%, depending on traffic intensity and heavy-vehicle share, leading to a gradual reduction in friction. Predictive assessments of skid-resistance deterioration and braking distance calculations for passenger cars and heavy vehicles under different friction levels were performed. The results support the need for regular texture monitoring, explicit consideration of across-width friction heterogeneity in accident analysis, and targeted improvements in winter maintenance practices, particularly in edge zones adjacent to barriers. Full article

To clarify the wear degradation of airport cement concrete pavements under combined environmental and traffic actions, this study established an environment-tire-pavement multi-physics finite element model incorporating surface texture, freeze–thaw deterioration, temperature gradients, and aircraft lift during taxiing. Indoor rapid freeze–thaw tests, accelerated wear tests, and 3D texture scanning were further conducted to calibrate and validate the model. The results show that temperature gradients significantly amplify pavement wear. At 180 km/h and 1.2 million wear cycles, increasing the temperature gradient from 0 to 60 °C/m increased wear depth and wear mass by about 40% and 96%, respectively. Taxiing speed was negatively correlated with wear, mainly because higher speed reduced tire-pavement contact duration and effective vertical load. Freeze–thaw deterioration was the dominant factor affecting wear, and the coupled freeze–thaw–temperature–load condition produced the most severe damage. The experimental and simulation results agreed well, with R² values above 0.98. Based on the combined experimental-simulation dataset, an interpretable CNN-BiLSTM model was developed for wear-depth prediction, achieving RMSE values of 0.019 and 0.035 for the training and test sets, respectively. SHAP analysis further confirmed that freeze–thaw cycles contributed most to wear prediction. This study can provide a quantitative basis for the wear resistance evaluation, life prediction, and maintenance decision-making of airport pavements. Full article

The use of marginal sedimentary aggregates in pavement construction remains a major challenge in mountainous regions due to their high porosity, weak lamination planes, and susceptibility to moisture-induced deterioration. This study investigates the potential of low-density polyethylene (LDPE) plastic waste to enhance the engineering performance of laminated Miocene soft rock aggregates used in bituminous concrete. Aggregates sourced from the Surma Group (Bhuban Formation) in Mizoram, India, were characterized through physico-mechanical, geochemical, and mineralogical analyses to evaluate their durability and moisture sensitivity. X-ray fluorescence (XRF) analysis revealed elevated feldspar and total alkali contents (≈5.15%), indicating a mineralogical composition prone to hydrophilic behavior and stripping within bituminous mixtures. To mitigate these limitations, aggregates were coated with varying proportions of LDPE plastic using the dry process. An optimum LDPE content of 9% by weight of aggregate produced significant improvements in aggregate performance, resulting in a 70.03% reduction in Aggregate Impact Value (from 17.72% to 5.31%), a decrease in Los Angeles Abrasion Value from 42.93% to 31.45%, and an 89.82% reduction in water absorption (from 4.52% to 0.46%). The polymer coating effectively sealed lamination planes and reduced moisture ingress within the sedimentary structure. Bituminous concrete mixtures incorporating LDPE were further evaluated using Marshall stability and indirect tensile strength tests. The addition of 1.1% LDPE by weight of mix significantly enhanced moisture resistance. For mixtures with nominal maximum aggregate sizes (NMASs) of 13 mm and 19 mm, the Tensile Strength Ratio (TSR) increased from 52.59% and 58.58% in the control mixtures to 82.81% and 87.10%, respectively, thereby satisfying the minimum requirement of 80% specified by MoRTH. The results indicate that LDPE functions as a hydrophobic barrier and structural sealant that improves binder–aggregate adhesion and prevents stripping along weak lamination planes. The findings demonstrate that LDPE-modified bituminous concrete provides a sustainable and technically viable strategy for upgrading marginal sedimentary aggregates into durable pavement materials while simultaneously promoting the beneficial reuse of plastic waste. Full article

The use of resource-saving technology in road construction material production is a current problem, the solution of which will allow us to increase the environmental and economic efficiency of the road construction industry. Nowadays, secondary raw materials are widely used in highway construction, obtained both from the waste of old road construction materials and collected from other industries. During asphalt production, up to 90% of raw materials can be replaced by reclaimed asphalt pavement (RAP). This technology requires residual binder modification to reduce the negative impact on the technological and operational asphalt concrete properties. On the other hand, the use of rubber crumbs or granules obtained from the disposal of old car tires in asphalt–concrete mixtures is widespread. However, some types of car tires cannot be used as raw materials to produce an effective modifier. Truck tires and tires from special vehicles are suitable for use as a modifier for asphalt–concrete mixtures. Tires designed for passenger cars do not contain enough polymer. As an experiment on asphalt–concrete mixture production using secondary resources only, a testing facility was developed. The testing facility uses hot gas obtained by burning automobile tires in a special oven as a heat source. Rubber residues from the recycling of automobile tires are used as fuel, which cannot be used to produce rubber powder or granules. RAP obtained by cold milling of the pavements of city and public roads was used as the object of the research. When studying the characteristics of the asphalt–concrete-mixture-based binder, it was found that the sulfur compounds present in the composition of hot gases change the properties of the binder, leading to a serious deterioration in the technological characteristics of asphalt–concrete mixtures. The asphalt–concrete mixture obtained during RAP processing is characterized by a narrow temperature range in which it can be laid and compacted to the required density values. After laying the pavement, quality control revealed a significant variation (the number of air voids ranged from 0.8 to 5.5%) in the average density of samples taken from the compacted layer. In addition, there were significant violations of the longitudinal evenness of the finished coating. Experiments were carried out to extract the binder from asphalt–concrete mixtures before and after regeneration. The physico-mechanical and rheological characteristics were studied and qualitative analysis of the binder was realized by IR spectroscopy. The data obtained allow us to establish the mechanism of how sulfur-containing gases influence the bitumen binder’s properties in asphalt mixtures. Additionally, the features of thermo-oxidative degradation occurring during the hot recycling of asphalt–concrete mixtures were established. A justification is also given for the need to use anti-aging modifiers to restore the properties of the residual binder. Full article

Clogging is the main mechanism that deteriorates the hydraulic functionality of permeable pavements, particularly in porous asphalt mixtures (PAM). This study evaluated the hydraulic impact of sediments from three peri-urban micro-watersheds in the Boyacá region of Colombia on the infiltration capacity of PAM. Road infrastructure and drainage conditions were analysed using orthophotos and field inspections to identify geomorphological factors that favour sediment transport toward the roadway. Annual erosion rates were estimated using the Universal Soil Loss Equation (USLE), and sediments were characterized both within the watersheds and at their outlet onto the road. Hydraulic performance was assessed through laboratory tests using a Falling Head Permeameter, complemented by field infiltration measurements with a Modified Cantabrian Infiltrometer (0.25 m²). Results showed erosion rates of up to 7.9 t/ha·year and infiltration losses above 90% under clogged conditions. A partial hydraulic recovery of around 40% was observed after maintenance, particularly when sediments exhibited a higher sand fraction. These findings demonstrate that combining USLE-based erosion modelling with controlled hydraulic testing provides a robust framework for evaluating clogging risks in peri-urban roads and offers new evidence on the hydraulic behaviour of PAM exposed to non-urban sediments in the design and maintenance of sustainable pavements. Full article

Accurate pavement performance prediction is critical for effective pavement management systems (PMS), enabling optimal maintenance and rehabilitation decisions. The Pavement Condition Index (PCI) is the most widely used performance indicator, yet reliable prediction requires models that capture full spectrum of deterioration drivers, including structural characteristics, traffic loads, and the increasingly impactful extreme climate events. While machine learning (ML) approaches have improved PCI prediction, most existing models overlook climate extremes. This study proposes a comprehensive ML-based PCI model that integrates extreme climate variables from the Expert Team on Climate Change Detection and Indices (ETCCDI). Eleven algorithms were evaluated on a dataset combining pavement age, structural characteristics, traffic loads, and extreme climate variables. Among the evaluated models, categorical boosting model achieved the lowest error values and the highest R² (0.81). Explainability analyses using feature importance and SHapley Additive exPlanations (SHAP) identified the number of icing days (ID), daily temperature range in December (DTR_Dec) and consecutive dry days (CDD) as the extreme climate indicators with the greatest negative predictive influence on PCI. Incorporating ETCCDI indices provided additional explanatory power beyond traditional annual average climatic variables, significantly improving both predictive accuracy and model interpretability. These findings highlight the importance of integrating standardized extreme climate indicators into PMS frameworks to support more resilient and sustainable pavement management under evolving climate conditions. Full article

Since children spend a significant portion of their developmental years in educational settings, the environmental quality of these institutions—specifically, the extent to which they expose their occupants to green space and heat stress—is a critical determinant of well-being and academic performance. This study assesses the green environmental quality of 121 educational institutions (kindergartens, and elementary and secondary schools) in Debrecen, Hungary. The main objective of the research is to identify educational institutions that require immediate intervention to address their lack of green spaces, improve the green environment, and mitigate the urban heat island (UHI) effect. A further aim of the study is to understand how different urban planning practices over the past century have led to the current situation. Therefore, we utilized high-resolution geospatial data (specifically, WorldView-2 imagery) to classify schoolyard vegetation; Landsat data to derive Land Surface Temperature (LST); and the Hoover index to quantify institutions’ spatial concentration. We developed a composite indicator to categorize green environmental quality and heat stress exposure. Our results reveal deep spatial and institutional inequalities. 47.5% of students attend institutions with low environmental quality. While kindergartens typically offer green-rich environments, secondary schools with significant student populations—which are primarily concentrated in the dense historical downtown—are trapped in “grey” zones possessing poor environmental quality. Furthermore, we identify a “green paradox” in socialist housing estates: despite abundant surrounding greenery, schools here record high LST values due to the heat-trapping morphology of vertical concrete structures. The study also highlights institutional maladaptation, such as converting schoolyards into parking lots and using rubber pavements for safety reasons, which contributes to the deterioration of environmental quality. We conclude that current urban planning and school architecture must shift paradigms, treating schoolyards as integral components of the public green infrastructure network through climate-adaptive design. In addition, stakeholders should develop the green environment of educational institutions comprehensively, taking into account both on-site and surrounding green spaces. Full article

The vehicular energy consumption, primarily determined by the vehicle’s characteristics, exhibits significant variations influenced by driving behavior, traffic, and road attributes, with repercussions for emissions. This paper presents experimental results from real-traffic runs to characterize the relationship between fuel consumption and these factors. Data on consumption, performance, and kinematics of a light-duty vehicle were obtained using low-cost devices, including an On-Board Diagnostics (OBD) scanner, a unit integrating an Inertial Measurement Unit (IMU) and a Global Positioning System (GPS) receiver. The data allowed distinguishing consumption patterns between two distinct scenarios: a collector road stretch with deteriorated pavement and an express road stretch with lower surface roughness. Relevant association was identified between fuel consumption and factors such as discrete pavement anomalies and variables related to driving and traffic. Moderate correlations were observed with slope, and weaker ones with pavement roughness. Regarding the regression analysis, results identified acceleration and engine speed as the primary operational determinants of fuel consumption, with road grade emerging as the dominant geometric constraint across all scenarios. The results reveal relevant associations between fuel consumption and road, driving, and traffic-related factors while simultaneously demonstrating a robust and replicable experimental methodology based on commercially available sensing devices for real-traffic energy and emission assessments. Full article

Punchouts distress represents a major structural deficiency in Continuously Reinforced Concrete Pavements (CRCPs), contributing to premature deterioration, reduced ride quality, and increased maintenance demands. To improve the prediction of punchout occurrence, this study develops a hybrid data-driven modeling approach that combines Gradient Boosting Machines (GBMs) with Particle Swarm Optimization (PSO). The proposed framework utilizes 395 observations obtained from 33 CRCP sections in the Long-Term Pavement Performance (LTPP) database, incorporating structural, climatic, traffic, and performance-related variables. PSO was applied to systematically tune key GBM hyperparameters, including the number of boosting iterations, learning rate, and tree complexity, in order to enhance predictive accuracy. Model performance was evaluated using five-fold cross-validation, where the optimized PSO-GBM model achieved an average RMSE of 1.09 and an R² value of 0.947, outperforming conventional GBM as well as Random Forest, Support Vector Regression, Artificial Neural Networks, and Linear Regression models. Variable importance and sensitivity analyses revealed that Layer 3 thickness, pavement age, annual average daily traffic, and precipitation play dominant roles in punchout development. The consistency of residual distributions and the stability of hyperparameter sensitivity trends further confirm the robustness of the proposed framework. Overall, the results demonstrate that integrating evolutionary optimization with ensemble learning provides an effective tool for modeling complex pavement distresses and offers practical support for proactive maintenance planning and long-term management of CRCP infrastructure. Full article