Search Results (62)

The accelerated growth of the transport sector has increased oil consumption and greenhouse gas (GHG) emissions, intensifying global environmental challenges. The electrification of transportation has emerged as a key strategy to achieve sustainability targets, with electric vehicles (EVs) expected to account for 50% of global car sales by 2035. However, widespread adoption requires smart infrastructure capable of enabling dynamic in-motion charging. In this context, Electric Road Systems (ERSs), particularly those based on Wireless Power Transfer (WPT) technologies, offer a promising solution by transferring energy between road-embedded transmitters and vehicle-mounted receivers. This study assesses the structural response and service life of conventional and electrified asphalt pavement sections representative of the Spanish road network. Several standard pavement configurations were analyzed under heavy traffic (dual axles, 13 tons) using a hybrid approach combining mechanistic–empirical multilayer modeling and three-dimensional Finite Element Method (FEM) simulations. The electrified designs integrate prefabricated charging units (CUs) placed at a 9 cm depth, disrupting the structural continuity of the pavement. The results reveal stress concentrations at the CU–asphalt interface and service life reductions of up to 50% in semiflexible pavements. Semirigid sections performed better, with average reductions close to 40%. These findings are based on numerical simulations of standard Spanish sections and do not include experimental validation. Full article

Asphalt pavements face escalating challenges from traffic loading, climate change, and material degradation, necessitating innovative maintenance solutions. Thermally induced self-healing technologies, leveraging the viscoelastic properties of asphalt binders, can autonomously repair microcracks through targeted thermal activation. This review explored thermally induced self-healing in asphalt mixtures, with a focus on leveraging steel slag as a functional aggregate to enhance sustainability and durability. Two thermal-activation methods, electromagnetic induction and microwave heating, were critically analyzed, highlighting their distinct advantages in heating efficiency, depth, and uniformity. Steel slag offers dual benefits: improving mechanical interlock and skid resistance in mixtures while facilitating efficient heat generation via electromagnetic induction or microwave heating. However, challenges such as hydration-induced expansion, heterogeneous slag composition, and energy-intensive heating processes impede widespread adoption. Pretreatment methods, including natural aging, carbonation, and surface modifications, are essential to mitigate volumetric instability and optimize slag performance. Key factors influencing healing efficacy, including binder properties, operational parameters (e.g., microwave power, frequency), and environmental trade-offs, were systematically evaluated. Future research directions emphasized standardized pretreatment protocols, hybrid heating technologies for uniform temperature distribution, and smart-infrastructure integration for predictive maintenance. Full article

Accurate segmentation of pavement cracks from high-resolution remote sensing imagery plays a crucial role in automated road condition assessment and infrastructure maintenance. However, crack structures often exhibit asymmetry, irregular morphology, and multi-scale variations, posing significant challenges to conventional CNN-based methods in real-world environments. Specifically, the proposed ETAFHrNet focuses on two predominant pavement-distress morphologies—linear cracks (transverse and longitudinal) and alligator cracks—and has been empirically validated on their intersections and branching patterns over both asphalt and concrete road surfaces. In this work, we present ETAFHrNet, a novel attention-guided segmentation network designed to address the limitations of traditional architectures in detecting fine-grained and asymmetric patterns. ETAFHrNet integrates Transformer-based global attention and multi-scale hybrid feature fusion, enhancing both contextual perception and detail sensitivity. The network introduces two key modules: the Efficient Hybrid Attention Transformer (EHAT), which captures long-range dependencies, and the Cross-Scale Hybrid Attention Module (CSHAM), which adaptively fuses features across spatial resolutions. To support model training and benchmarking, we also propose QD-Crack, a high-resolution, pixel-level annotated dataset collected from real-world road inspection scenarios. Experimental results show that ETAFHrNet significantly outperforms existing methods—including U-Net, DeepLabv3+, and HRNet—in both segmentation accuracy and generalization ability. These findings demonstrate the effectiveness of interpretable, multi-scale attention architectures in complex object detection and image classification tasks, making our approach relevant for broader applications, such as autonomous driving, remote sensing, and smart infrastructure systems. Full article

The integration of the Smart Pavement Management Center aims to improve the efficiency and quality of road maintenance in New Taipei City. This paper explores the application of the I-ROAD Reporting System and analyzes its effectiveness in providing real-time road condition updates and alerts, which assist road authorities in making timely decisions. Additionally, the study establishes a comprehensive road maintenance lifecycle model, encompassing road construction, maintenance, repair, and milling. This model systematically manages road resources to reduce maintenance costs and extend the service life of roads. Through the optimization of inspection methods and the evaluation and selection of construction techniques, the most cost-effective solutions were identified to improve the efficiency and quality of maintenance work. Lastly, the paper discusses how to optimize the budget for road maintenance within financial constraints by applying scientific budget allocation and management strategies. This ensures the efficient use of funds and that road conditions are maintained at an optimal level. This study explores the use of a road management system for the analysis and optimization of future road maintenance projects. Within a limited budget, it aims to achieve optimal allocation and management of maintenance funds through scientific financial planning, ensuring efficient use of resources to maintain road conditions at an optimal level. 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

Harnessing the power of phase change materials (PCMs) in asphalt pavements proposes a sustainable solution for addressing temperature-related issues, affording more robust and energy-efficient infrastructure. PCMs hold enormous potential for reforming various industries due to their ability to store and release large amounts of thermal energy, offering noteworthy benefits in energy efficiency, thermal management, and sustainability. The integration of PCMs within pavements presents an increasingly exciting field of research. PCMs have the ability to efficiently manage the changes in and distribution of temperature in asphalt pavements via the release and absorption of latent heat that occurs during the phase shifts of PCMs. Asphalt pavements experience less severe temperatures and a slower rate of temperature fluctuation as a result of this, which in turn reduces the amount of stress caused by temperature. In addition, the function of temperature adjustment that PCMs provide is natural, intelligent, and in line with the direction in which the development of smart pavements is heading in the future. This study aims to explore the impact of organic, inorganic, and mixed organic–inorganic PCMs on diverse surface characteristics of asphalt. In addition, this review addresses current challenges associated with using PCMs in asphalt and explores potential advantages that could facilitate future research in addition to broadening the implementation of PCMs in construction. Full article

Smart pavement is composed of a monitor network, communication network, data center, and energy supply system, and it requires reliable and efficient energy sources to power sensors and devices. The mechanical energy is wasted and dissipated as heat in traditional pavement; this energy can be reused to power low-power devices and sensors for smart pavement. Mechanical energy harvesting systems typically perform through electromagnetic, piezoelectric, and triboelectric methods. Among the different methods, electromagnetic harvesters stand out for their higher output power. However, current electromagnetic harvesters face challenges such as bulky designs, low power density, and high input displacement requirements. This study proposed a green electromagnetic harvester (GEH) based on up-frequency and a unidirectional rotation mechanism to harvest mechanical energy from the pavement. A prototype was designed and prepared. The influence of different parameters on the electrical performance of the harvester was studied by using an MTS test instrument and simulation methods. The results demonstrate that increasing the frequency and optimizing the magnetic array significantly enhances electrical output. The open-circuit voltage in the N-S mode is 3.1 times higher than that in the N-N mode. At a frequency of 9 Hz and a displacement of 3.0 mm, the open-circuit voltage of the GEH is 6.73 V, the maximum power output is 171.14 mW, the peak power density is 1277.16 W/m³, and the voltage has almost no decay after 100,000 cycles. Further, the application of the GEH in charging sensors and capacitors was demonstrated, which indicates the potential of a GEH to power sensors for smart roads. Full article

Roads are essential for economic development, facilitating the circulation of services and resources. This research seeks to provide local governments with a comprehensive framework to enhance road maintenance, focusing on the surface and functional evaluation of pavements. It compares the conventional methods International Roughness Index (IRI) and the Pavement Condition Index (PCI) with novel methodologies that employ smart technologies. The efficiency of such technologies in the maintenance of local roads in Peru is analyzed, taking as a case study a 2 km section of the AR-780 highway in the city of Arequipa. The International Roughness Index (IRI) obtained through the Merlin Roughness Meter and the Roadroid application were compared, finding a minimum variation of 4.0% in the left lane and 8.7% in the right lane. Roadroid turned out to be 60 times faster than the conventional method, with a cost difference of 220.11 soles/km (USD $57.92/km). Both methods classified the Present Serviceability Index (PSI) as good, validating the accuracy of Roadroid. In addition, the Pavement Condition Index (PCI) was evaluated with conventional methods and a DJI Mavic 2 Pro drone, finding a variation of 6.9%. The cost difference between the methodologies was 1047.73 soles/km (USD $275.72/km), and the use of the drone proved to be 10 times faster than visual inspection. This study contributes to closing the knowledge gap regarding the use of smart technologies for better pavement management on local roads, so the actors in charge of such infrastructure make decisions based on science, contributing to the well-being of the population. Full article

The quality of bicycle path surfaces significantly influences the comfort of cyclists. This study evaluates the effectiveness of smartphone sensor data and smart bicycle lights data in assessing the roughness of bicycle paths. The research was conducted in Hasselt, Belgium, where various bicycle path pavement types, such as asphalt, cobblestone, concrete, and paving tiles, were analyzed across selected streets. A smartphone application (Physics Toolbox Sensor Suite) and SEE.SENSE smart bicycle lights were used to collect GPS and vertical acceleration data on the bicycle paths. The Dynamic Comfort Index (DCI) and Root Mean Square (RMS) values from the data collected through the Physics Toolbox Sensor Suite were calculated to quantify the vibrational comfort experienced by cyclists. In addition, the data collected from the SEE.SENSE smart bicycle light, DCI, and RMS computed results were categorized for a statistical comparison. The findings of the statistical tests revealed no significant difference in the comfort assessment among DCI, RMS, and SEE.SENSE. The study highlights the potential of integrating smartphone sensors and smart bicycle lights for efficient, large-scale assessments of bicycle infrastructure, contributing to more informed urban planning and improved cycling conditions. It also provides a low-cost solution for the city authorities to continuously assess and monitor the quality of their cycling paths. Full article

In North America and Europe, asphalt shingle waste created during the installation of roofing membranes and tear-off shingles retrieved at the end of the membrane’s life cycle are two major sources of municipal solid waste. Since almost 15–35% of recycled asphalt shingles (RAS) consist of an asphalt binder, the effective recycling of RAS into asphalt mixtures could also allow a reduction in the consumption of non-renewable resources such as asphalt binders. In this context, several studies investigating the use of RAS in asphalt mixtures can be found in the literature, although they exhibit widespread and sometimes conflicting information about the investigated materials, the mix preparation and testing methodologies and the experimental findings. Given this background, this review paper aims at summarizing the existing information and research gaps, providing a synthetic and rational picture of the current literature, where similar attempts cannot be found. In particular, different research studies show that the use of RAS in asphalt mixtures is an economical as well as an eco-friendly option. RAS with up to 20% by weight of binder or 5% by weight of aggregate/mixtures (eventually in combination with 15% reclaimed asphalt pavement aggregate) were found to be relatively suitable to improve the performance properties of asphalt mixtures, both in the laboratory and in the field. Adding RAS to asphalt mixtures could enhance their stiffness, strength and rutting resistance (i.e., high-temperature properties), while negatively affecting the mixtures’ fatigue and thermal cracking resistance. However, the addition of specific biomaterials (e.g., bio-binders, bio-oils) or additives to asphalt mixtures can mitigate such issues, resulting in lower brittleness and shear susceptibilities and thus improving the anti-cracking performance. On the other hand, the literature review revealed that several aspects still need to be studied in detail. As an example, RAS-modified porous asphalt mixtures (fatigue, rutting, moisture susceptibility and thermal cracking) need specific research, and there are no comprehensive research studies on the effects of the RAS mixing time, size and mixing temperature in asphalt mixtures. Moreover, the addition of waste cooking/engine oils (biomaterials) as asphalt binder rejuvenators in combination with RAS represents an attractive aspect to be studied in detail. Full article

This paper explores new sensor technologies and their integration within Connected Autonomous Vehicles (CAVs) for real-time road condition monitoring. Sensors like accelerometers, gyroscopes, LiDAR, cameras, and radar that have been made available on CAVs are able to detect anomalies on roads, including potholes, surface cracks, or roughness. This paper also describes advanced data processing techniques of data detected with sensors, including machine learning algorithms, sensor fusion, and edge computing, which enhance accuracy and reliability in road condition assessment. Together, these technologies support instant road safety and long-term maintenance cost reduction with proactive maintenance strategies. Finally, this article provides a comprehensive review of the state-of-the-art future directions of condition monitoring systems for traditional and smart roads. Full article

Road maintenance management aims to satisfy quality, comfort, and safety requirements for the various assets. To overcome delays and barriers in the widespread adoption of road management systems, the Building Information Modeling (BIM) approach may offer significant advantages as a convenient alternative for road maintenance management. Although existing BIM platforms are not fully equipped for this purpose, defining original modules and scripts can extend their capabilities, allowing for the handling of road condition information and maintenance management. In this context, this paper presents an operative framework designed to leverage BIM benefits for road maintenance management, particularly in terms of virtual inspection, asset condition assessment, and maintenance design. To achieve this, specific original and customized smart objects and routines were coded in I-BIM platforms, tailored to different scales, aims, and detail levels. These smart objects incorporate user-defined extended attributes related to pavement condition and maintenance planning (such as roughness, rutting, structural capacity). In particular, the authors have developed original virtual smart objects in different platforms, serving as “containers” for the survey information. These objects are adapted to display quality levels of the pavement segments in a realistic and user-friendly environment. Additionally, original routines were coded to automatically import survey data from external datasets and associate this information with the appropriate objects. This customized and extended approach, not available in commercial platforms, can effectively support maintenance operators. Full article

Smart highways represent a novel highway concept in the era of big data, emphasizing the synergy among people, vehicles, road facilities, and the environment. However, the operation and management of smart highways have become more intricate, surpassing the adaptability of traditional highway evaluation and management methods. This study integrates the distinctive characteristics of smart highway facilities and operational objectives to enhance and modernize the existing highway evaluation system. Drawing from research on smart highway construction projects, a smart highway evaluation system encompassing facility structure, electromechanical facilities, and operation services is formulated based on a hierarchical analysis method. The quantitative evaluation of each indicator is achieved by combining existing specifications and expert questionnaire solicitation. The group decision-making method is initially employed to optimize subjective weights, followed by the calculation of combined weights using both the entropy weight method and critic method in objective evaluation. Finally, a comprehensive evaluation model is established and validated through engineering projects. The results demonstrate that the evaluation system effectively highlights the advantages and disadvantages in the operation and management of smart highways, thereby fostering the advancement of smart highway iteration. Full article

Intelligent compaction (IC) has emerged as a breakthrough technology that utilizes advanced sensing, data transmission, and control systems to optimize asphalt pavement compaction quality and efficiency. However, accurate assessment of compaction status remains challenging under real construction conditions. This paper reviewed recent progress and applications of smart sensors and machine learning (ML) to address existing limitations in IC. The principles and components of various advanced sensors deployed in IC systems were introduced, including SmartRock, fiber Bragg grating, and integrated circuit piezoelectric acceleration sensors. Case studies on utilizing these sensors for particle behavior monitoring, strain measurement, and impact data collection were reviewed. Meanwhile, common ML algorithms including regression, classification, clustering, and artificial neural networks were discussed. Practical examples of applying ML to estimate mechanical properties, evaluate overall compaction quality, and predict soil firmness through supervised and unsupervised models were examined. Results indicated smart sensors have enhanced compaction monitoring capabilities but require robustness improvements. ML provides a data-driven approach to complement traditional empirical methods but necessitates extensive field validation. Potential integration with digital construction technologies such as building information modeling and augmented reality was also explored. In conclusion, leveraging emerging sensing and artificial intelligence presents opportunities to optimize the IC process and address key challenges. However, cooperation across disciplines will be vital to test and refine technologies under real-world conditions. This study serves to advance understanding and highlight priority areas for future research toward the realization of IC’s full potential. Full article

Moving is an indispensable component of travelling. This paper discusses the experiences of persons with disabilities when moving around cities on foot or wheels, based on research conducted during the EU-funded project TRIPS. Findings comprise participants’ vignettes from 49 interviews in seven European cities, views on smart assistive technologies (e.g., Augmented Reality) from a pan-European quantitative survey, and design concepts related to walking based on a co-creation workshop that actively engaged persons with various types of disabilities in ideation. Findings suggest that people need reliable and clear wayfaring information on accessible travel routes featuring the coordinated design of streets, pavement, stops, stations, and vehicles to ensure seamless, step-free, and obstacle-free access, as well as disability-sensitive management of disruptions such as maintenance works, for example. Findings also suggest that users are open to using any assistive technology that can enable them to live more independently, assuming it is accessible, and are keen to co-innovate. Finally, we make recommendations for policy changes that can facilitate the redesign of urban infrastructure to make cities more accessible for people with disabilities and drive structural changes in urban planning. Full article