Search Results (153)

The integration of waste plastics into pavement materials offers a dual benefit of enhancing road performance and mitigating the environmental burden of plastic waste. This review critically examines the opportunities and challenges associated with incorporating waste plastics in pavement construction, with an emphasis on their impact on the mechanical properties, durability, and life cycle performance of pavements. Special attention is given to the environmental implications, particularly the potential generation and release of micro- and nano-plastics during the pavement life cycle. This paper further evaluates current monitoring and analytical methodologies for detecting plastic emissions from road surfaces and explores emerging approaches for minimizing environmental risks. By providing a comprehensive synthesis of existing knowledge, this review seeks to support sustainable practices and inform policy development within the frameworks of circular economy and environmental stewardship. Full article

Asphalt pavement cracking represents a prevalent form of deterioration that significantly compromises road performance and safety under the combined effects of environmental factors and traffic loading. Crack sealing has emerged as a widely adopted and cost-effective preventive maintenance strategy that restores the pavement’s structural integrity and extends service life. This paper presents a systematic review of the development of crack sealing technology, conducts a comparative analysis of conventional sealing materials (including emulsified asphalt, hot-applied asphalt, polymer-modified asphalt, and rubber-modified asphalt), and examines the existing performance evaluation methodologies. Critical failure mechanisms are thoroughly investigated, including interfacial bond failure resulting from construction defects, material aging and degradation, hydrodynamic scouring effects, and thermal cycling impacts. Additionally, this review examines advanced sensing methodologies for detecting premature sealant failure, encompassing both non-destructive testing techniques and active sensing technologies utilizing intelligent crack sealing materials with embedded monitoring capabilities. Based on current research gaps, this paper identifies future research directions to guide the development of intelligent and sustainable asphalt pavement crack repair technologies. The proposed research framework provides valuable insights for researchers and practitioners seeking to improve the long-term effectiveness of pavement maintenance strategies. Full article

This paper is devoted to determining the influence of some risk elements on the asphalted surfaces of bridge ramps, in order to detect possible damages or potential collapses of the embankment. The main factors will be characterized by (a) movements of floating reinforced concrete (r.c.) slab over the embankment connected to the border of the bridge; (b) longitudinal cracks on the asphalt produced by small sliding deformations; (c) emerging vegetation from the slope of the ramps. The authors propose a set of possible techniques to determine level of risk indicators, illustrating a set of case studies related to several asphalt roads approaching r.c. bridges. Full article

In recent decades, numerous studies have investigated the application of Artificial Intelligence (AI), and more precisely of Artificial Neural Networks (ANNs), in the prediction of complex technical parameters, particularly in the field of road infrastructure management. Among them, prediction of the widely used International Roughness Index (IRI) has attracted much attention due to its importance in pavement maintenance planning. This study focuses on predicting future IRI values using traditional regression models and neural networks, specifically Multilayer Perceptron (MLP) and Long Short-Term Memory (LSTM) networks, on two highway sections, each analyzed in two experimental setups. The models consider only traffic and structural road characteristics as variables. The results show that the LSTM method provides significantly lower prediction errors for both highway sections, indicating better performance in capturing roughness trends over time. These results confirm that ANNs are a useful tool for engineers by predicting future IRI values, as they help to extend pavement life and reduce overall maintenance costs. The integration of machine learning into pavement evaluation is a promising step forward in ongoing efforts to optimize pavement management. Full article

This study presents the development of a discrete algorithm for interpreting ground-penetrating radar (GPR) data in vertically inhomogeneous media for the diagnostics of road structures. Experimental data were obtained using an OKO-2 GPR system, followed by primary radargram processing using the CartScan software. This included noise and interference filtering, as well as the initial estimation of the dielectric permittivity of detected layers. The resulting dataset was used to validate numerical algorithms for solving the forward and inverse problems of geolectrics. The proposed approach is based on minimizing a quadratic misfit functional between the calculated and observed values of the horizontal component of the electromagnetic field. The gradient of the functional required for optimization is obtained via the numerical solution of an adjoint problem. A discrete version of this problem was developed, which satisfies the properties of conservativeness and uniformity according to finite difference theory. The inverse problem reconstruction of dielectric permittivity is considered a non-destructive method for radargram interpretation. Assuming a piecewise-continuous medium structure eliminates the need for computing gradients at material interfaces. The proposed methodology enhances the accuracy and reliability of pavement condition assessment and holds practical value for road infrastructure monitoring. Full article

Particulate matter (PM) originating from road dust is an increasing concern in urban air quality, particularly as non-exhaust emissions from tire–pavement interactions gain prominence. Existing models often focus on meteorological and traffic-related variables while oversimplifying pavement surface characteristics, limiting their applicability across diverse spatial and traffic conditions. This study investigates the influence of concrete pavement macrotexture—specifically the Mean Texture Depth (MTD) and surface wavelength—on PM₁₀ resuspension. Field data were collected using a vehicle-mounted DustTrak 8530 sensor following the TRAKER protocol, enabling real-time monitoring near the tire–pavement interface. A multivariable linear regression model was used to evaluate the effects of MTD, wavelength, and the interaction between silt loading (sL) and PM₁₀ content, achieving a high adjusted R² of 0.765. The surface wavelength and sL–PM₁₀ interaction were statistically significant (p < 0.01). The PM₁₀ concentrations increased with the MTD up to a threshold of approximately 1.4 mm, after which the trend plateaued. A short wavelength (<4 mm) resulted in 30–50% higher PM₁₀ emissions compared to a longer wavelength (>30 mm), likely due to enhanced air-pumping effects caused by more frequent aggregate contact. Among pavement types, Transverse Tining (T.Tining) exhibited the highest emissions due to its high MTD and short wavelength, whereas Exposed Aggregate Concrete Pavement (EACP) and the Next-Generation Concrete Surface (NGCS) showed lower emissions with a moderate MTD (1.0–1.4 mm) and longer wavelength. Mechanistically, a low MTD means there is a lack of sufficient voids for dust retention but generates less turbulence, producing moderate emissions. In contrast, a high MTD combined with a very short wavelength intensifies tire contact and localized air pumping, increasing emissions. Therefore, an intermediate MTD and moderate wavelength configuration appears optimal, balancing dust retention with minimized turbulence. These findings offer a texture-informed framework for integrating pavement surface characteristics into PM emission models, supporting sustainable and emission-conscious pavement design. Full article

Resource depletion and environmental degradation have resulted from the substantial increase in the use of natural aggregates and construction materials brought on by the growing demand for infrastructure development. Road building using mining waste has become a viable substitute that reduces the buildup of industrial waste while providing ecological and economic advantages. In order to assess the appropriateness of several mining waste materials for use in road building, this study investigates their engineering characteristics. These materials include slag, fly ash, tailings, waste rock, and overburden. To ensure long-term performance in pavement applications, this study evaluates their tensile and compressive strength, resistance to abrasion, durability under freeze–thaw cycles, and chemical stability. This review highlights the potential of mining waste materials as sustainable alternatives in road construction. Waste rock and slag exhibit excellent mechanical strength and durability, making them suitable for high-traffic pavements. Although fly ash and tailings require stabilization, their pozzolanic properties enhance subgrade reinforcement and soil stabilization. Properly processed overburden materials are viable for subbase and embankment applications. By promoting the reuse of mining waste, this study supports landfill reduction, carbon emission mitigation, and circular economy principles. Overall, mining byproducts present a cost-effective and environmentally responsible alternative to conventional construction materials. To support broader implementation, further efforts are needed to improve stabilization techniques, monitor long-term field performance, and establish effective policy frameworks. Full article

The optimisation of road construction planning and design prioritises safety, comfort, cost-effectiveness, and sustainability by aligning with sustainable development goals (SDGs) and integrating life cycle assessment (LCA)-based criteria. Asphalt mixture compaction is a critical construction-phase process that requires careful monitoring due to its significant impact on fuel consumption, CO₂ emissions, and pavement performance. However, characterising the compaction process during the design stage is challenging due to the unavailability of primary data, such as the compaction energy applied by the roller on-site. This study addresses this gap by developing a methodology for deriving compaction-energy-related data at the laboratory stage. An algorithm is proposed to estimate key compaction parameters, specifically the locking point and compaction curves, based on aggregate grading. Equations to improve the design of bituminous mixtures based on compaction targets were derived. The findings support more sustainable planning, the optimised selection of construction equipment, and improved competitive equilibria between different pavement technologies by promoting low-carbon and energy-efficient strategies aligned with SDGS. Full article

Monitoring the vibration response of Portland cement concrete (PCC) pavement under dynamic vehicle loading is critical for road maintenance and traffic analysis. This study embedded micro-electro-mechanical systems (MEMS) accelerometer sensors in PCC pavement to capture vibration signals induced by vehicles. A thresholding method is proposed to automate vehicle detection by analyzing acceleration time-domain data, achieving precision and recall rates exceeding 85%. The study also explored various sensor placement locations and different threshold values for acceleration time-domain signals. Sensor placement optimization revealed that positioning sensors at the front or rear ends of pavement slabs maximizes vibration response, enabling low-cost and efficient detection. Experimental results demonstrated that the proposed method balances simplicity and accuracy, eliminating the need for complex denoising processes. This approach provides a cost-effective solution for real-time vehicle detection and enhances pavement performance monitoring, supporting improved maintenance and traffic management strategies. Full article

This study explores the application of K-means clustering to optimize the selection of sampling locations for suspended silt loading (sL) on asphalt pavements, addressing the limitations of traditional random sampling methods in the EPA method. The objective was to identify reliable sampling points for road dust concentration measurement, with a focus on improving the accuracy of data collection using the vacuum sweep method. The elbow method was used to determine the optimal number of clusters, revealing that three clusters were ideal for 25 m intervals and five for 100 m intervals. The clustering analysis identified specific sampling locations within the 25 m and 100 m road sections, such as 1.5–4.5 m and 12–18 m, and 15–18 m, 39–42 m, 57 m, 69 m, and 87 m, respectively, which adequately captured sL characteristics. The silhouette score of 0.6247 confirmed the effectiveness of the clustering method in distinguishing distinct groups with similar sL characteristics. The comparison of clustered versus non-clustered sections across 15 pavement segments showed an error rate of approximately 6%. Properly selecting sampling points ensures more accurate dust concentration data, which is crucial for effective road maintenance and environmental management. The findings highlight that optimizing the sampling process can significantly enhance the precision of dust monitoring, especially in areas with varying sL characteristics. Full article

This study introduces an innovative and scalable approach for automated road surface assessment by integrating Mobile Mapping System (MMS)-based LiDAR data analysis with an open-source WebGIS platform. In a U.S.-based case study, over 20 datasets were collected along Interstate I-65 in West Lafayette, Indiana, using the Purdue Wheel-based Mobile Mapping System—Ultra High Accuracy (PWMMS-UHA), following Indiana Department of Transportation (INDOT) guidelines. Preprocessing included noise removal, resolution reduction to 2 cm, and ground/non-ground separation using the Cloth Simulation Filter (CSF), resulting in Bare Earth (BE), Digital Terrain Model (DTM), and Above Ground (AG) point clouds. The optimized BE layer, enriched with intensity and color information, enabled crack detection through Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and Random Forest (RF) classification, with and without intensity normalization. DBSCAN parameter tuning was guided by silhouette scores, while model performance was evaluated using precision, recall, F1-score, and the Jaccard Index, benchmarked against reference data. Results demonstrate that RF consistently outperformed DBSCAN, particularly under intensity normalization, achieving Jaccard Index values of 94% for longitudinal and 88% for transverse cracks. A key contribution of this work is the integration of geospatial analytics into an interactive, open-source WebGIS environment—developed using Blender, QGIS, and Lizmap—to support predictive maintenance planning. Moreover, intervention thresholds were defined based on crack surface area, aligned with the Pavement Condition Index (PCI) and FHWA standards, offering a data-driven framework for infrastructure monitoring. This study emphasizes the practical advantages of comparing clustering and machine learning techniques on 3D LiDAR point clouds, both with and without intensity normalization, and proposes a replicable, computationally efficient alternative to deep learning methods, which often require extensive training datasets and high computational resources. Full article

Internal-condition sensing for road structures is crucial for road service safety, performance assessments, and maintenance. In recent years, new technologies for monitoring structural conditions of roads have been updated, significantly improving road-sensing capabilities. Most of these techniques use a new generation of sensors and monitoring systems by means of buried sensors in roads to obtain the accurate mechanical status of road-internal structures. This paper presents an exhaustive and systematic literature review of in situ sensing technology for the internal-structure conditions of pavements in the past 20 years. The principles, advantages, and disadvantages of existing monitoring sensors, such as fiber-optic grating sensors and resistive strain gauges, and their applicability in pavement monitoring are reviewed. Meanwhile, sensing technology based on conductive sensitive materials (CSMs) are shown to have broad application prospects, and the details of conductive polymer compositions, preparation processes, and sensing performance factors are discussed. Lastly, further opportunities and challenges for using polymer CSMs for in situ road monitoring are highlighted. Full article

The swift advancement in monitoring and evaluation systems for road pavement conditions highlights the crucial role that this field plays in ensuring the sustainability of roads. This, in turn, contributes to the growth and prosperity of nations and enables users to enjoy the highest levels of luxury and comfort. Despite numerous studies and ongoing research, finding the most precise and efficient monitoring systems to determine the type and severity of road defects, their causes, and appropriate treatments remains a challenge. This study proposes a system that employs a camera to create an application capable of evaluating road conditions with ease by taking images while driving over the road. Based on the results, the application was accurate in identifying road defects of different severity within the same category. The proposed method was compared to the Pavement Condition Index (PCI) method, and a significant match was found in determining the type and severity of each defect on the selected road sections. More clearly, the overall accuracy of detecting and classifying block cracks, alligator cracks, longitudinal cracks, and potholes was significant for detecting and classifying the patches. Full article

Roads are a form of critical infrastructure, influencing economic growth, mobility, and public safety. However, the management, monitoring, and maintenance of road networks remain a challenge, particularly given limited budgets and the complexity of assessing widespread infrastructure. This Special Issue on “Road Detection, Monitoring, and Maintenance Using Remotely Sensed Data” presents innovative strategies leveraging remote sensing technologies, artificial intelligence (AI), and non-destructive testing (NDT) to optimize road infrastructure assessment. The ten papers published in this issue explore diverse methodologies, including novel deep learning algorithms for road inventory, novel methods for pavement crack detection, AI-enhanced ground-penetrating radar (GPR) imaging for subsurface assessment, high-resolution optical satellite imagery for unpaved road assessment, and aerial orthophotography for road mapping. Collectively, these studies demonstrate the transformative potential of remotely sensed data for improving the efficiency, accuracy, and scalability of road monitoring and maintenance processes. The findings highlight the importance of integrating multi-source remote sensing data with advanced AI-based techniques to develop cost-effective, automated, and scalable solutions for road authorities. As the first edition of this Special Issue, these contributions lay the groundwork for future advancements in remote sensing applications for road network management. Full article

Asphalt pavement has become a vital component of modern highway construction due to its high wear resistance, short construction period, economic viability, and excellent skid resistance. However, increasing traffic volume has heightened the structural performance requirements of asphalt pavement, especially during compaction. The compaction degree of asphalt mixtures has emerged as a key indicator for assessing construction quality. This study explores the relationship between the internal structural evolution of asphalt mixtures and their compaction performance, focusing on the motion behavior of coarse aggregates. To achieve this, a wireless smart aggregate was developed using 3D printing technology to simulate coarse aggregate motion and enable real-time monitoring during compaction. Compaction experiments, including Superpave gyratory compaction and wheel rolling, were conducted on asphalt mixtures with different gradations (e.g., AC-13 and AC-20). The dynamic responses of smart aggregates were analyzed to identify motion patterns. The results show that the Superpave gyratory compaction method more accurately replicates aggregate motion observed in road construction. Additionally, asphalt mixture gradation significantly affects the motion behavior of coarse aggregates. This study provides insights into the microscale motion of coarse aggregates and its connection to compaction performance, contributing to improved asphalt pavement quality and efficiency. Full article