Search Results (159)

Road surface condition monitoring is critical for ensuring safe and efficient transportation networks. This study proposes and evaluates a framework that compares imagery-, Light Detection and Ranging (LiDAR), and accelerometer-based approaches for pavement anomaly detection. The analysis first focused on a 5-mile urban roadway segment, in which all three sensing modalities were evaluated under identical survey conditions using manually interpreted reference anomalies to compare detection accuracy, severity classification, and processing efficiency. The imagery-based Convolutional Transformer-based Crack Segmentation (CT-CrackSeg) model achieved a precision, recall, and F1-score of 88.5%, 88.5%, and 88.5%, respectively, but remained sensitive to environmental factors such as shadows, curbs, roadside features, and pavement texture variations. The LiDAR-based method achieved an F1-score of 93.0%, while the accelerometer-based Isolation Forest and Adaptive Threshold methods achieved F1-scores of 95.2% and 97.2%, respectively. These results indicate strong detection performance under the evaluated validation conditions; however, the reported precision values should be interpreted as dataset-specific rather than universal performance levels. Given the accelerometer-based approach’s strong detection performance, minimal processing time, and low deployment cost, it was further applied across a 36-mile roadway network to evaluate its scalability for network-level monitoring. Across the full route, the spatial agreement among accelerometer systems exceeded 0.91, while the agreement between the two detection methods exceeded 0.96, with 962–996 surface defects detected depending on the sensor and method. Integrating the anomaly detection results into a Potree-based web portal enabled interactive validation with geotagged imagery and point clouds, improving interpretability and diagnostic insight. Overall, the findings highlight that accelerometer-based monitoring, even with consumer-grade sensors, provides a practical, scalable, and low-cost solution for pavement evaluation, while LiDAR and imagery serve as complementary tools for detailed verification and characterization. Full article

In the context of pavement management, conventional data collection methods for the evaluation of pavement functional condition are limited by relatively slow acquisition speeds, that prevent fast-lane motorway surveying at 120–130 km/h, and by survey frequency, which on vast networks typically occurs twice a year. Given these limitations, continuous pavement condition monitoring from moving vehicles offers an attractive solution to move towards real-time digital road assessment. In particular, such a result is achieved by making use of “intelligent” or “smart” tires, which by means of appropriate arrays of sensors can capture contact patch information, thereby providing quantitative information related to pavement roughness and macrotexture. In this study, smart tire data functional condition indicators, Dynamic Index (DI) and Pr index, were collected over several segments of a motorway network, with a total length of 405 km. Correlations were investigated between such parameters and the results of measurements coming from a traditional pavement monitoring technique, expressed in terms of international roughness index (IRI) and mean profile depth (MPD). Furthermore, the ability of smart tire indicators to identify time-dependent trends and to rank different motorway segments was assessed. Obtained results, which were generated by adopting different data processing and homogenization techniques, showed that DI displays a moderate correlation with IRI, while Pr exhibits a strong correlation with MPD. Pavement-age analysis highlighted the existence of meaningful trends for both dense-graded and open-graded asphalt-wearing courses. Motorway rankings based on average DI and Pr values were found to be in agreement with those obtained from average IRI and MPD values, thereby confirming the potential of smart tire technology as a complementary network-level monitoring tool for pavement asset management systems. Full article

Warm mix asphalt (WMA) provides a sustainable way of lowering production temperatures, reducing energy use for sustainable pavement construction; however, moisture damage affects its durability. Reclaimed asphalt pavement (RAP) contains aged binder that is stiffer, harder, and more brittle than virgin binder, resulting in asphalt mixtures with higher stiffness/modulus. This review examines the effect of incorporating RAP to amend the moisture damage susceptibility of WMA. It surveys the various moisture-damage failures reported in the literature on WMA with RAP mixes, including adhesive and cohesive failures, as well as hydraulic scouring and aggregate fracture. The analysis further explains the influence of WMA technology, RAP content, rejuvenation, and interfacial chemistry on the moisture durability of WMA-RAP mixtures. The strengths and limitations of the conventional and emerging moisture damage evaluation tests, including AASHTO T 283 tensile strength ratio (TSR), boiling water test (BWT), surface free energy (SFE), and fracture-energy-based approaches, were compared. This mechanistic synthesis linking production-related moisture sources, RAP heterogeneity and practical mitigation strategies highlights why reliance on TSR alone can conceal moisture-cracking vulnerability. The synthesis clarifies how RAP changes the moisture damage susceptibility of WMA to retain the environmental, economic and social benefits and circularity without compromising durability. This review proposes a practical roadmap based on technology-specific screening, multi-metric performance evaluation, and construction quality control for more reliable WMA-RAP specifications. Full article

Pavement damages along the Qinghai–Xizang Highway (QXH) in permafrost regions are jointly controlled by geographical and engineering factors, leading to higher damage rates than in non-permafrost regions. However, the overall development trend of these damages and the spatial–temporal patterns have not been systematically quantified. To analyze the spatial distribution of different pavement damages, reveal the spatial–temporal associations, and analyze the spatial heterogeneity of the driving factors, three field surveys were conducted in 2014, 2019 and 2024, with records of seven major pavement damages. Statistical analyses were used to examine the relationships among single and co-occurring damages. Then, a novel geographical model, combining a graph convolutional network with multi-scale geographically weighted regression (GCN-MGWR), was further developed to treat the QXH as a linear geographic unit and to assess the spatial heterogeneity and relative contribution of different influencing factors. The results show that the mean pavement damage ratios in permafrost regions during the three surveys are 4.21%, 6.82%, and 4.74%, respectively, with crack-type damages (transverse, longitudinal, and block cracking) exhibiting the highest occurrence rates. The three strongest pairs of correlations are transverse and longitudinal cracking (0.584), transverse and block cracking (0.570), and waving and rutting (0.622). The primary factors influencing crack-type damages are embankment thickness, mean annual ground surface temperature (MAGST), elevation and existing damages. Transverse and longitudinal cracking show a pronounced increase with rising MAGST, and embankment thickness below 1 m or above 4 m significantly contribute to the development of both crack types (SHAP > 0.5). Overall, the evolution of crack-type damages has shifted from being primarily controlled by geographical factors to being controlled by the combined influence of engineering and geographical factors during 2014–2024. The factor contributions identified by the GCN-MGWR model provide quantitative support for the regional adaptive design and specific maintenance of roadway in permafrost regions. Full article

Magnitude alone fails to capture the full complexity of pavement roughness; its spatial distribution along a road is equally vital for effective maintenance planning. While traditional assessment has long relied on specialized survey vehicles, the rise of mobile crowdsensing now allows for massive data acquisition via smartphone sensors. This study investigates the spatial structure of pavement roughness using crowdsensed data from the SmartRoadSense platform. Roughness is quantified through the Power of Prediction Error (PPE) indicator derived from smartphone accelerometer signals. The dataset consists of 475 observations sampled at 20 m intervals over approximately 9.5 km of the A3/E45 motorway in southern Italy. A multi-scale spatial–statistical framework is adopted to analyse the roughness signal. The analysis includes the evaluation of scale-dependent statistical descriptors (mean and coefficient of variation), as well as spatial correlation, spectral, and entropy-based measures. The results indicate a short spatial correlation length (approximately 60–100 m) and the absence of a dominant spatial wavelength, suggesting that pavement roughness behaves as a localized multiscale process. A complementary segmentation analysis based on Classification and Regression Trees (CART) is performed to explore the spatial partitioning of the roughness signal. Our analysis indicates that segmentation complexity spikes once the minimum node size drops below roughly 10 observations. This trend points to the existence of localized irregularities that coarser scales simply overlook. Ultimately, these results suggest that mean roughness values alone are insufficient for describing pavement condition and that hybrid spatial–statistical approaches may support more scalable, data-driven, and spatially targeted pavement monitoring strategies for sustainable transportation infrastructure management. Full article

Punjab, Pakistan, is experiencing severe groundwater depletion due to excessive and unplanned extraction, declining surface water availability, rapid population growth, and increasing climate variability. Groundwater has become the primary source of irrigation and drinking water across the province, contributing about 50%, 90% and 95% of the requirements of agricultural, domestic, and industrial water demands. Natural recharge rates have been reduced due to construction, pavements, and the lining of irrigation channels. This study presents the first pilot-scale Managed Aquifer Recharge (MAR) initiative implemented by the Irrigation Research Institute (IRI) of the Punjab Irrigation department. Floodwater has been diverted into the bed of the abandoned Old Mailsi Canal (OMC), which off-takes from Islam Headworks. About 144 recharge wells have been constructed in the bed of the OMC. During the 2025 flood season, approximately 12,000 acre-feet of floodwater was diverted and stored through engineered ponding, canal-bed rehabilitation, and recharge wells. A comprehensive monitoring program was established, including piezometers, automated data loggers, groundwater quality sampling, pumping tests, geophysical surveys, and sediment analyses. The results indicate a maximum groundwater level rise of up to 11 ft., with average increases ranging from 2.6 to 5.2 ft across the recharge ponds. Groundwater quality also showed an improvement following MAR implementation; electrical conductivity decreased from 900 to 650 μS/cm in Pond-I and from 850 to 750 μS/cm in Pond-III. These findings demonstrate that repurposing abandoned canal infrastructure for floodwater-based MAR provides a technically feasible, environmentally sustainable, and climate-resilient strategy for enhancing groundwater availability for sustainable management in Punjab and other water-stressed regions. Full article

Reliable pavement condition assessment using UAV-derived orthophotos remains challenging under manual flight conditions, where acquisition parameters are not predefined and photogrammetric quality is highly operator-dependent. This study evaluates how UAV flight configuration influences orthophoto quality and operational usability for road infrastructure assessment in real-world manual survey scenarios. Eight flight treatments combining altitude (30–40 m AGL), flight speed (low/normal), and image capture interval (2–3 s) were tested over an urban–peri-urban road segment in Misantla, Veracruz, Mexico, using a DJI Air 3S platform. Orthomosaic quality was assessed through ground sampling distance (GSD), tie-point density, multiplicity, RMS reprojection error, dense cloud size, orthomosaic continuity, and a criteria-based interpretability index supported by field observations. Results show that while altitude controls spatial resolution, resolution alone is insufficient for reliable pavement assessment. Configurations with higher image overlap and photogrammetric redundancy (notably Treatment 1 (T1) and Treatment 3 (T3)) achieved superior geometric consistency, reduced seam artifacts, and improved detection of subtle surface irregularities. In contrast, reduced-overlap configurations produced complete but less interpretable orthomosaics. The study provides experimentally validated operational guidelines for optimizing UAV flight parameters under manual conditions, bridging the gap between controlled photogrammetric theory and practical infrastructure monitoring. Full article

Urban forests and green spaces provide important ecosystem services that support climate adaptation, public health, and urban sustainability. Despite growing evidence from individual Portuguese cities, nationwide data on how citizens perceive, use, and support the governance of urban green spaces remain limited. This study addresses that gap through a nationwide online survey conducted in Portugal in 2024, gathering 927 valid responses from Portuguese adults across metropolitan, intermediate-density, and low-density municipalities, to investigate public perceptions of ecosystem services, patterns of green space use, management challenges, and attitudes toward urban forestry governance policies. Results revealed strongly positive perceptions of urban trees and green spaces across all sociodemographic groups, with over 95% of respondents acknowledging that urban green spaces positively influence physical and mental health. Regulating services, including air quality improvement, urban noise reduction, climate change mitigation, and flood mitigation, received the highest levels of agreement, while cultural ecosystem services were positively perceived but with comparatively lower agreement. Accessibility emerged as a critical determinant of visitation frequency: 85% of respondents could reach a green space within 15 min, and 82% of daily users lived within 300 m of one, broadly consistent with the 3 + 30 + 300 principle. Frequent visitation was primarily associated with relaxation, physical activity, and social interaction. Conversely, only 6% considered that trees cause more damage than benefits, with pavement damage and superficial roots cited as the more significant management challenges. Support for public investment was broad, with over 90% of respondents favouring allocating municipal tax revenues to urban tree management. However, 68% remained unfamiliar with Law No. 59/2021, revealing a gap between public support and policy awareness. These findings establish a national baseline to support municipalities in developing more resilient, inclusive, and health-promoting urban environments in the face of climate change, as they align urban forestry practices with citizens’ expectations. Full article

Automated pavement distress detection (PDD) is critical for the structural health monitoring (SHM) of transportation infrastructure, yet existing methods struggle with real-time multi-target detection under resource constraints. In this paper, YOLOv8-PDD was constructed based on YOLOv8 by introducing the large separable kernel attention (LSKA) mechanism module into the Spatial Pyramid Pooling—Fast (SPPF) module, replacing Complete-IoU (CIoU) loss with Distance-IoU (DIOU) loss as the loss function, and adopting Soft-Non-Maximum Suppression (NMS) to replace the original NMS algorithm. The proposed YOLOv8-PDD achieved 78.3% mean average precision with intersection over union above 0.5 (mAP@0.5 +8.1%) with a minimal complexity increase of +0.2 GFLOPs compared to the baseline YOLOv8n model. While incurring a negligible increase in latency (+0.09 ms), YOLOv8-PDD significantly outperforms YOLOv8n in detection accuracy (mAP@0.5 +8.1%), offering a superior accuracy–efficiency trade-off for real-time applications. YOLOv8-PDD performed well in detecting all categories, with AP values above 75% except for transverse crack and strip patch. Significant improvements in pothole detection AP@0.5 (+22.1%) and strip patch detection AP@0.5 (+17.7%) indicate superior small target and complex background adaptability. Our model achieved a detection efficiency of 68 frames per second (FPS) on consumer-grade CPUs (OpenVINO-optimized), outperforming 10 models (e.g., YOLOv5n and RTDETR-l) in accuracy–speed balance. Full article

Moisture damage is a common hidden distress in asphalt pavements in hot and rainy regions, where it can rapidly develop into severe surface deterioration if not detected in time. To address this issue, this study proposes an automated framework integrating ground-penetrating radar (GPR) data and the YOLOv13 model for multi-scale moisture damage detection on the Jingang Expressway in Cambodia. A total of 1672 GPR images containing moisture damage were collected through field surveys using a 2.3 GHz GPR system. Based on field statistical analysis, the detected damage was classified into three scale levels: large-scale (>2 m), medium-scale (0.8–2 m), and tiny-scale (<0.8 m). Several recent YOLO variants were compared, and YOLOv13s was identified as the optimal model, achieving the best balance between detection accuracy and inference efficiency, with an mAP@0.5 of 85.3% and an FPS of 48. The proposed method was further validated through laboratory and field tests. The results indicate that the developed framework can effectively detect and localize multi-scale moisture damage under practical engineering conditions, providing a non-destructive and efficient approach for pavement condition assessment in hot and rainy regions. By enabling early-stage detection of moisture damage deterioration, the proposed framework may contribute to more sustainable pavement maintenance and long-term transportation infrastructure management. Full article

Rural public spaces are crucial to the daily activities of older adults; however, limited research has examined the effects of their environmental characteristics on older adults’ spatiotemporal behavior and perception from a multisensory perspective. This study hypothesizes that composite sensory environments have significant nonlinear predictive effects on older adults’ behavior types and satisfaction. In this study, 10 sample spaces were selected in Qingdao, China. Multi-source data were collected through a two-week period of unobtrusive observation and subjective questionnaire surveys (N = 241). Multiple logistic regression was used to analyze the main effects of environmental characteristics, and an MLP model with a single hidden layer of 100 units was constructed to predict dwell time and satisfaction. The results show that, in the investigated rural context, older adults’ dominant behavior was social activity (81.12%), which mainly occurred in built spaces such as squares. Multiple logistic regression indicated that, among the various environmental factors, visual aesthetics had a statistically significant effect on behavior types (p = 0.013). The MLP model achieved prediction accuracies of 85.3% for dwell time and 93.1% for satisfaction. The key predictive variables were volume perception (100% importance), the Natural Sound Index (NSI) (92.1%), and visual aesthetics (89.3%). Subgroup heterogeneity analysis further showed that older-old adults and those with poorer health conditions were more sensitive to pavement quality and physical comfort, whereas older adults living alone or with limited household companionship were more strongly influenced by visual aesthetics and natural soundscape quality. The theoretical significance of this study lies in proposing quantitative measures of natural sound and odor indices and revealing that, in the specific northern rural built environment, the coordinated design of visual and auditory environments plays an important role in improving spatial quality. The findings provide empirical support for the age-friendly micro-renewal of rural public spaces in specific regions. However, due to the limitations of single-season data and a relatively small sample size, their generalizability needs to be further verified across regions. 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

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

Unlike image-only systems that falter in shadows, glare, and low contrast, LiDAR directly records surface geometry and supports depth-aware quantification. This survey examines LiDAR-based road surface damage classification across the entire pipeline, encompassing acquisition with mobile and terrestrial laser scanning, preprocessing and representation choices, supervised, semi-supervised, and unsupervised learning techniques, as well as multisensor fusion at early, mid, and late stages. A consistent thread is measurement, not just detection: we describe how LiDAR damage classification maps to agency practices such as the Distress Identification Manual and the Pavement Condition Index. We summarize datasets and evaluation protocols for detection, segmentation, 3D reconstruction, and ride quality. We outline practical concerns for corridor-scale deployment: calibration and timing, intensity normalization, tiling/streaming, and runtime budgeting. The review concludes with open problems and outlines directions for robust, severity-aware, and scalable field systems. Full article

This study presents a multidisciplinary investigation of the archeological site of Iuvanum (Abruzzo, central Italy), integrating geophysics, geomatics, architectural analysis and archeology with the purpose of exploring the relationship between surface remains and buried structures of archeological value. This research focuses on the area covering part of the forum and part of the basilica, where ground-penetrating radar (GPR) surveys were conducted to detect subsurface anomalies potentially associated with unexcavated architectural features. GPR line scans were acquired under complex topographic conditions, processed, and assembled into a three-dimensional representation, from which volumes of interest (VOIs) were extracted. These geophysical results were integrated into a comprehensive three-dimensional framework together with high-resolution UAV photogrammetry, digital elevation models, orthophotos and a virtual architectural model (VAM) of the site. The integrated visualization environment greatly facilitates the recognition of spatial relations between the detected anomalies and the hypothesized architectural elements. The observed GPR anomalies confirmed wall remains that were initially speculated or located along their geometrical continuation. Pavement levels, as well as some structures asymmetrical with respect to the purely geometric reconstruction, were also identified. This study demonstrates how integrating GPR with geomatic and archeological approaches improves the reliability and interpretative depth of non-invasive archeological prospecting. The proposed workflow provides a reproducible methodological framework propedeutical to excavation planning and suitable for the integration of information from multi-data sensors. Full article