Search Results (55)

As urban underground spaces and infrastructure development accelerate, subsurface elements such as buried pipelines, integrated utility tunnels, subway tunnels, cavity defects, and deep-seated hidden hazards become increasingly intertwined. Consequently, urban target detection is characterized by pronounced scale discrepancies, intense environmental interference, and severely confined operational spaces. The transient electromagnetic method (TEM) is highly valuable for rapid surveys and hazard identification in urban underground spaces owing to its inherent advantages, including non-contact operation, adaptability to hardened pavements, high sensitivity to low-resistivity anomalies, and the ability to probe a broad range of depths. In recent years, research has shifted from improving isolated instrumentation to synergistically optimizing sensing platforms, transmitter–receiver systems, anti-interference methodologies, and imaging interpretation workflows. Specifically, small-loop configurations and high-frequency excitation technologies have improved shallow-sounding capabilities in confined urban spaces; anti-interference techniques have increased data reliability in complex noise environments; and apparent resistivity mapping, virtual wave-field migration, and rapid inversion methodologies have enabled profiling results to transition from qualitative identification to fine-scale interpretation. Concurrently, the evolution of ground-towed, UAV-borne, helicopter-borne, and semi-airborne platforms has progressively endowed urban TEM profiling with continuous, mobile, and scenario-specific operational capabilities. Looking to the future, further technical breakthroughs in urban TEM technology are required to improve shallow-resolution, deep-seated penetration, multi-source interference decoupling, and real-time concurrent imaging. Full article

Concrete pavement noise generated at tire–surface contact is a negative phenomenon that might be limited differently, especially by applying surface texture. To estimate the texture’s loudness, various tests are used to measure the road noise level. One of the increasingly used tests is On-board Sound Intensity (OBSI). Performing such tests enables to explore the road noise phenomenon more efficiently; however, it also enables to distinguish and compare the texture impact on the road noise level. Moreover, it might also contribute to quiet concrete pavement further development. The article presents an OBSI method application review to investigate road noise in concrete pavements considering known texturing methods. The focus is to answer how broadly the OBSI method was applied in concrete pavements regarding texture and what the main findings are. Additionally, the following review notices if complex tests considering other pavement parameters related to road noise level and safety were performed simultaneously with the OBSI measurement. Road noise on concrete pavements has been widely investigated using the OBSI method. However, this review identifies significant research gaps. The effects of customized surface texture configurations and acoustic durability remain underexplored. Furthermore, comprehensive studies are lacking. Specifically, skid resistance (measured by TWO or SRT-3 devices) should be evaluated simultaneously with OBSI levels. These measurements must be conducted under identical conditions, including the same test speed and continuous surveying. Further research in this area will fill existing knowledge gaps and clarify pavement noise generation mechanisms. Ultimately, this will enable the development of design and maintenance guidelines for low-noise concrete pavements, optimizing texturing methods, safety, and economic factors. Full article

Edge computing plays an important role in structural health monitoring (SHM) for transportation infrastructure because it enables local data processing and low-latency decision support. However, SHM systems encounter challenges due to data anomalies caused by sensor faults, transmission errors, or irregular structural behavior. This study evaluates the deployment performance of a SHM anomaly data-processing workflow on resource-constrained edge devices. The workflow includes data cleaning, response separation, and anomaly detection. Missing data are processed using cubic spline interpolation. Jump points and drift are corrected using the Laida criterion, and noise is reduced using wavelet threshold denoising. Response separation is then performed using the detrending method based on time windows, the 3σ criterion, or wavelet packet decomposition. Anomaly detection is then performed using autoregressive integrated moving average with explanatory variables, support vector machines, and recurrent neural networks. Simulation data and field monitoring data from bridge displacement and highway pavement strain are used to evaluate the workflow. The evaluation focuses on runtime, memory usage, central processing unit usage, and a data-processing throughput proxy. This analysis helps to understand the performance and trade-offs of algorithms on edge devices under the resource constraints typical of SHM applications. Full article

Low-noise pavements (LNPs) are increasingly important under Green Public Procurement policies, yet public administrations still lack clear guidance on selecting pavement types based on noise-related externalities. Although traffic noise generates substantial societal costs—affecting health, education, and property values—these external burdens are often overlooked or excluded from traditional pavement appraisal and investment decisions, leading to systematically underestimated life cycle costs (LCC). This study develops an integrated framework to monetise traffic-noise impacts within an LCC perspective by combining health effects (Disability-Adjusted Life Years, DALYs), property-value capitalisation (willingness to pay, WTP), and noise-induced educational losses. The system limit is intentionally restricted to noise-related externalities during pavement operations, while agency, user, and vehicle operating costs are excluded. A comprehensive review of existing monetisation approaches is provided, and a new unified method is proposed. The framework is applied to a case study from the LIFE SNEAK project on Via La Marmora (Florence, Italy), comparing existing, acoustically non-optimised, and acoustically optimised surfaces. The results showed that the LIFE SNEAK pavement significantly alleviated the burden of noise on public health and education costs, which were 34% and 33% lower than in the baseline scenario, respectively, with a welfare surplus of +€2.38 million over the ten-year period. In particular, it was noted that the most important economic contribution of LNPs stems from the WTP approach. This study provides clear evidence that noise externalities play a considerable role in long-term pavement cost estimates, thereby supporting the systematic inclusion of these costs in LCC analyses. The proposed method puts forward a practical approach to support the selection of noise-sensitive, sustainable, and socially responsible road pavements. 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

Vision-based structural defect detection methods based on YOLOv11 have achieved promising performance in recent years; however, their robustness in real engineering environments remains limited due to illumination variation, shadow occlusion, surface contamination, and complex background textures. Existing data-driven approaches primarily rely on visual appearance features while neglecting the intrinsic geometric continuity and morphological characteristics associated with structural failures such as cracks and spalling. To address these challenges, this study proposes an enhanced defect detection framework termed GCA-YOLO for intelligent structural inspection. The proposed method integrates a Geometric Constraint Attention (GCA) module and a Residual Efficient Channel Attention (RECA) module to improve feature representation. Instead of explicit physical simulation, the GCA module embeds morphology-guided geometric priors into the attention mechanism using differentiable gradient and Laplacian operators. This enforces structural continuity perception and suppresses geometrically inconsistent responses caused by background noise. Furthermore, a geometry confidence gating mechanism adaptively modulates the contribution of morphological features, while the RECA module recalibrates channel-wise responses to enhance the representation of weak and low-contrast defects. To comprehensively evaluate the proposed method, experiments were conducted on three representative datasets, including a public crack dataset and two self-built datasets (one for peeling/detachment and one for crack defects). These datasets were collected from diverse civil infrastructure scenarios such as bridges, tunnels, and pavements under challenging conditions including low illumination, shadow occlusion, complex textures, and heterogeneous backgrounds. Compared with the baseline YOLOv11 model, the proposed GCA-YOLO framework improves mAP@0.5 by 2.2%, 2.5%, and 15.9% on the public crack dataset, the self-built peeling/detaching dataset, and the self-built crack dataset, respectively. Meanwhile, Recall is improved by 4.6%, 3.8%, and 33.1%, respectively, demonstrating the effectiveness of the proposed dual-attention framework in enhancing the completeness of defect localization and reducing missed detections. Despite these performance gains, the proposed framework maintains a lightweight architecture and does not introduce significant computational overhead. Experimental results demonstrate that the proposed framework achieves strong robustness, stable generalization capability, and favorable detection efficiency across different defect categories and engineering scenarios, demonstrating promising potential for intelligent infrastructure inspection, urban safety monitoring, and practical engineering deployment. Full article

Road pavement rehabilitation increasingly incorporates innovative surface technologies aimed at improving pavement performance while reducing environmental impacts. In addition to conventional recycled asphalt pavement (RAP) maintenance strategies, advanced pavement surface systems such as reflective coatings, rejuvenator-based self-healing mixtures, and thin low-noise asphalt layers have been developed to enhance durability and functional performance. This study presents a comparative Life Cycle Assessment (LCA) of four pavement surface technologies using primary inventory data obtained from full-scale road sections. The systems evaluated include a conventional maintenance mixture and three alternative surface solutions: reflective pavement coatings, RAP mixtures incorporating rejuvenator-based self-healing systems, and thin low-noise asphalt layers. The assessment follows ISO 14040 and ISO 14044 standards and applies the ILCD 2011 midpoint+ (EF 2.0) method. To enable comparability between technologies with different durability, the functional unit was defined as 1 m² of rehabilitated pavement per year of service life. The results indicate that thin low-noise asphalt layers provide the highest environmental benefits across most impact categories due to significant material savings associated with reduced layer thickness. Reflective pavement coatings decrease several impacts, particularly fossil resource depletion and atmospheric emissions, although higher burdens are observed in some categories due to synthetic binder production. RAP mixtures incorporating rejuvenator-based self-healing systems improve resource efficiency and extend pavement durability but may increase impacts associated with binder manufacturing. Overall, the findings highlight relevant environmental trade-offs between different pavement surface technologies and demonstrate that parameters such as layer thickness, binder composition, recycled material content, and service life strongly influence environmental performance. The study illustrates how comparative Life Cycle Assessment supports the development and selection of more sustainable pavement surface systems. Full article

Utilizing waste tire crumb rubber to modify asphalt enhances the damping and noise reduction performance of pavements. This study systematically evaluated the damping performance of crumb rubber–modified asphalt over a wide temperature range. A high-temperature damping index based on the loss factor and a low-temperature energy dissipation ratio derived from the Burgers model were proposed for quantitative characterization. The results show that damping performance is primarily controlled by temperature and crumb rubber content, while particle size plays a secondary role. Increasing crumb rubber content markedly improves damping performance. When the crumb rubber content exceeds 20%, the damping temperature stability, peak loss factor, and its retention tend to level off, whereas the low-temperature enhancement diminishes when the content exceeds 25%. Accordingly, the robust combinations are 80-mesh (≈180 μm) with 20% content for high-temperature conditions and 80-mesh with 25% content for low-temperature conditions. Multivariate nonlinear regression models achieved high predictive accuracy (R² = 0.927 and 0.985). Microscopic analyses indicate that crumb rubber increases constrained interfacial phases and system viscosity, and partial particle exposure at 20–25% further enhances interfacial friction and energy dissipation, consistent with the observed macroscopic damping behavior. These findings provide a theoretical basis for robust, noise-reducing pavements. Full article

Traffic noise is one of the main sources of environmental problems and a growing challenge for national traffic authorities. It is widely accepted that tire-pavement interaction is the main cause of traffic noise at speeds between 40 and 90 km/h. Typically, noise attenuation strategies include earthworks, tree belts, or noise barriers. However, a solution that is almost always viable is the use of low-noise pavements, which are characterized by their porous macrotexture, such as Stone Mastic Asphalt (SMA) mixtures. These mixtures are increasingly used for heavy traffic volumes because of their many advantages, including drainage properties and mechanical strength. Based on the experimental results obtained on different roads in southern Spain, this paper compares noise reduction in an SMA standard mixture due to the incorporation of different additives, such as crumb rubber and polymeric additives. According to the analysis, increasing the additives content by 1% reduces CPX by 1.18 decibels, approximately, and none of the analyzed sections shows increases greater than 3 dB within 24 months. Additionally, the paper proposes design recommendations regarding macrotexture and the percentage of voids for zones with warm, dry climates, such as Mediterranean Spain. Full article

Urban expressways increasingly struggle with declining skid resistance and rising tire–pavement noise as asphalt surfaces age, yet few studies integrate friction, acoustic behavior, and permeability over multiple pavement types and service years. This research provides the first multi-year, multi-indicator field evaluation of four widely used surface systems in Korea (double-layer drainage asphalt, thin-layer low-noise overlays, SMA, and WC-6) linking wet-skid resistance (BPN), rolling friction (GN), acoustic metrics (LAeq, LAFmax), density evolution, and field permeability (Q) into a single performance framework. A three-year dataset from 2021–2024 (n = 20 sites) revealed that wheelpath friction deteriorated steadily at −1.77 BPN/year, with WC-6 exhibiting the fastest decline (≈−5.5 BPN/year) and drainage asphalt the slowest (≈–2.5 BPN/year). Predicted wheelpath service lives spanned 3.8–5.7 years across pavement types. Acoustic monitoring showed a consistent hierarchy (Drainage < Thin-Layer < SMA < WC-6), with WC-6 producing up to 20 dB higher LAFmax due to strong air-pumping effects. Permeability trends mirrored acoustic behavior, with Q decreasing 9–18% over 3 years but maintaining clear ranking: Drainage > Thin-Layer > SMA > WC-6. SMA NMAS analysis further showed that SMA-10 mm delivers higher initial friction but deteriorates faster than SMA-8 mm. Correlation analysis demonstrated that permeability governs both friction and noise (Q vs. LAeq: r = −0.84; Q vs. GN: r = +0.81), confirming the dominant mechanistic role of void connectivity. Collectively, the findings provide quantitative evidence that drainage and thin-layer systems deliver the most balanced long-term performance across safety, noise, and hydraulic functionality, offering practical guidance for pavement selection on noise-sensitive expressways. Full article

Asphalt mixtures designed with an elevated air void content are intended to lower traffic noise as well as to improve traffic safety and quality by improving rainwater evacuation through the layer of the surface mixture, not just on top of it. While undoubtedly mixtures with high air voids have significant advantages, the durability of such mixes could be an issue. In the research presented in this paper, a performance evaluation case study of asphalt mixes with medium and high air void content was investigated, in both the laboratory and the trial section. The study assessed asphalt mixtures intended for so-called quiet pavements in terms of selected properties (such as water and frost resistance, low temperature cracking, fatigue life, and water permeability) that significantly impact the durability of the pavement surface course under traffic loads and climatic conditions. Five different mixtures were designed, which differed in the proportion of individual components, grain size, asphalt content, and void content. The conducted research indicates that mixtures with increased void content may exhibit lower durability parameters. In addition, the surface drainage performance can be effectively managed by selecting the appropriate mixture type, maximum aggregate size, and target air void content, depending on the functional requirements for macrotexture and pavement type. This should be considered both in the mix design process, by using the best possible materials and conducting additional testing, and also when selecting the mixture type to find an optimum between durability and acoustic parameters of the pavement layer. Full article

Rutting is a critical form of pavement distress that compromises driving safety and long-term structural integrity. Traditional detection methods predominantly rely on cross-sectional measurements and high-cost inertial navigation-assisted laser scanning, which limits their applicability for large-scale, full-section evaluation. To address these limitations, this study proposes a framework for full-section rutting detection leveraging an area-array structured light camera for efficient 3D data acquisition. A multi-scale texture enhancement strategy based on 2D wavelet transform is introduced to extract latent surface features, enabling robust and accurate point-cloud registration without the need for artificial markers. Additionally, an improved Random Sample Consensus—Density-Based Spatial Clustering of Applications with Noise (RANSAC-DBSCAN) algorithm is designed to enhance the precision and robustness of rutting region segmentation under real-world pavement conditions. The proposed method is experimentally validated using 102 multi-frame pavement point clouds. Compared to Fast Point Feature Histograms (FPFH) and Deep Closest Point (DCP), the registration approach achieves a 71.31% and 80.64% reduction in point-to-plane error, respectively. For rutting segmentation, the enhanced clustering method attains an average F1-score of 90.5%, outperforming baseline methods by over 15%. The proposed workflow can be seamlessly integrated into vehicle-mounted structured-light inspection systems, offering a low-cost and scalable solution for near real-time, full-lane rutting detection in routine pavement monitoring. Full article

Accurate pavement crack detection under adverse weather conditions is essential for road safety and effective pavement maintenance. However, factors such as reduced visibility, background noise, and irregular crack morphology make this task particularly challenging in real-world environments. To address these challenges, we propose CrackNet-Weather, which is a robust and efficient detection method that systematically incorporates three key modules: a Haar Wavelet Downsampling Block (HWDB) for enhanced frequency information preservation, a Strip Pooling Bottleneck Block (SPBB) for multi-scale and context-aware feature fusion, and a Dynamic Sampling Upsampling Block (DSUB) for content-adaptive spatial feature reconstruction. Extensive experiments conducted on a challenging dataset containing both rainy and snowy weather demonstrate that CrackNet-Weather significantly outperforms mainstream baseline models, achieving notable improvements in mean Average Precision, especially for low-contrast, fine, and irregular cracks. Furthermore, our method maintains a favorable balance between detection accuracy and computational complexity, making it well suited for practical road inspection and large-scale deployment. These results confirm the effectiveness and practicality of CrackNet-Weather in addressing the challenges of real-world pavement crack detection under adverse weather conditions. Full article

Traffic-induced noise pollution is a significant environmental issue, driving the development of advanced noise-reducing pavement materials. Semi-dense graded asphalt mixtures (SDAMs) present a promising compromise, offering enhanced acoustic properties compared to conventional dense-graded asphalt mixtures while maintaining superior durability to porous asphalt mixtures. However, the mechanism underlying the relationship between the energy dissipation characteristics and noise reduction effects of such mixtures remains unclear, which limits further optimization of their noise reduction performance. This study designed and prepared semi-dense graded noise-reducing asphalt mixtures SMA-6 TM, SMA-10 TM, and SMA-13 TM (SMA TM represents noise-reducing SMA mixture) based on traditional dense-graded asphalt mixtures SMA-6, SMA-10, and SMA-13, and conducted tests for water stability, high-temperature performance (60 °C), and low-temperature performance (−10 °C). Subsequently, energy loss parameters such as loss factor and damping ratio were calculated through dynamic modulus tests to characterize their energy dissipation properties. The mechanism linking the energy dissipation characteristics of semi-dense graded asphalt mixtures to noise reduction was investigated. Finally, the noise reduction effect was further verified through a tire free fall test and a close-proximity (CPX) method. The indoor test results indicate that the semi-dense mixtures exhibited a trade-off in performance: their dynamic stability was 11.1–11.3% lower and low-temperature performance decreased by 4.2% (SMA-13 TM) to 14.1% (SMA-6 TM), with moisture stability remaining comparable. Conversely, they demonstrated superior damping, with consistently higher loss factors and damping ratios. All mixtures reached peak damping at 20 °C, and the loss factor showed a strong positive correlation (R² > 0.91) with energy dissipation. Field results from a test section showed that the optimized SMA-10 TM mixture yielded a significant tire–road noise reduction of 3–5 dB(A) relative to the SMA-13, while concurrently meeting key performance criteria for anti-water ability and durability. This study establishes a link between the energy dissipation in SDAM and their noise reduction efficacy. The findings provide a theoretical framework for optimizing mixture designs and support the wider application of SDAM as a practical noise mitigation solution. Full article

This study proposes a deep metric learning-based pavement distress classification method to address critical limitations in conventional approaches, including their dependency on large training datasets and inability to incrementally learn new categories. To resolve high intra-class variance and low inter-class distinction in distress images, we design a CNN head with multi-cluster centroins trained via SoftTriple loss, simultaneously maximizing inter-class separation while establishing multiple intra-class centers. An adaptive weighting strategy combining sample similarity and class priors mitigates data imbalance, while soft-label techniques reduce labeling noise by evaluating similarity against support-set exemplars. Evaluations on the UAV-PDD2023 dataset demonstrate superior performance—3.2% higher macro-recall than supervised learning, and 6.7%/8.5% improvements in macro-F1/weighted-F1 over iCaRL incremental learning—validating the method’s effectiveness for real-world road inspection scenarios with evolving distress types and limited annotation. Full article