Road energy harvesting is an ingenious horizon for clean and renewable energy production. The concept is very compatible with current traffic trends and the ongoing depletion of natural resources. Yet, the idea of harvesting roadway energy is still in its genesis, and only a few real-time implementation projects have been reported in the literature. This review article summarizes the current state of the art in road energy harvesting technology, with a focus on piezoelectric systems, including an analysis of the impact of the technology from social and environmental standpoints. Based on an extensive desktop review study, this article provides a comprehensive insight into roadway energy harvesting technologies. Specifically, the article discusses the societal and environmental benefits of road energy harvesting technologies, as well as the challenges. The study outlined the meaningful benefits that positively align with the concept of sustainability. Overall, the literature findings indicate that the expansion of the roadway energy harvesting technology to a large practical scale is feasible, but such an undertaking should be wisely weighed from broader perspectives. Ultimately, the article provides a positive outlook of the potential contributions of road energy harvesting technologies to the ongoing energy and environmental challenges of human society.

A three-dimensional finite element (3-D FE) modelling was conducted based on non-uniform distributed tire-pavement contact pressure, full interfacial layer bonding conditions, and viscoelastic characterisation of the asphalt layers. In general, high temperatures were observed to be beneficial to the bottom of the asphalt layer – both the tensile and shear stresses decreased as the temperature was increased. By contrast, the shear stresses increased on the surface of the asphalt layer with an increase in temperature – thus, increasing the propensity to surface shear failure as the temperature increased. Overall, the results indicated that under elastic modelling assumptions, there is a high propensity for failure at the bottom of the asphalt and/or base layer due to high tensile stresses. With viscoelastic modelling assumption, however, the asphalt layer is more likely to suffer from surface shear failure due to high shear stresses on the surface.

Monitoring and restoring soil quality in areas neighboring roads affected by traffic activities require a thorough investigation of heavy metal concentrations. This study examines the spatial heterogeneity of copper (Cu) and chromium (Cr) concentrations in a 0.113 km² area adjacent to Jin-Long Avenue in Wuhan, China, using Unmanned Aerial Vehicle (UAV)-based hyperspectral remote sensing technology. Through this UAV-based remote sensing technology, we innovatively achieve a small-scale and fine-grained analysis of soil heavy metal pollution related with traffic activities, which represents a major contribution of this research study. In our approach, we generated 4375 spectral variates by transforming the original spectrum. To enhance result accuracy, we applied the Boruta algorithm and correlation analysis to select optimal spectral variates. We developed the retrieval model using the Gradient Boosting Decision Tree (GBDT) regression method, selected from a set of four regression methods using the LOOCV method. The resulting model yielded R-square values of 0.325 and 0.351 for Cu and Cr, respectively, providing valuable insights into the heavy metal concentrations. Based on the retrieved heavy metal concentrations from bare soil pixels (17,420 points), we analyzed the relationship between heavy metal concentrations and the perpendicular distance from the road. Additionally, we employed the universal kriging interpolation method to map heavy metal concentrations across the entire area. Our findings reveal that the concentration of heavy metals in this area exceeds background values and decreases as the distance from the road increases. This research significantly contributes to the understanding of spatial distribution characteristics and pollution caused by heavy metal concentrations resulting from traffic activities.

Buton Rock Asphalt (BRA) refers to the natural rock asphalt natively produced on the Buton island of Indonesia. It is often used as a modifier to enhance the performance of asphaltpavement. However, the segregation of BRA in BRA-Modified Asphalt (BRA-MA) has restricted its application. This study aims to investigate how the particle size and content of BRA affect the physical properties and storage stability of BRA-MA. Penetration, softening point, viscosity, and viscosity-temperature susceptibility (VTS) were analyzed. The evaluation method of storage stability was discussed and determined. The segregation of BRA in BRA-MA of static storage and transportation process were simulated and tested. The results suggest that the softening point and viscosity were positively correlated to BRA content and inversely determined by particle size. Penetration, VTS, and ductility were reduced due to the decline in particle size and increment of BRA content. The index of segregation value based on viscosity difference showed better statistical and quantitative significances than the softening-point difference in evaluating the storage stability. The particle size and content of BRA are positively correlated to the segregation of BRA-MA. Both the storage temperature and time were positively correlated to the segregation of BRA-MA. We prove that the relationship between specific surface area and segregation are power functional. BRA-MA with BRA whose 50% particle sizes are lower than 13.6 μm showed low segregation in transportation.

Collision accidents involving construction vehicles and workers frequently occur at construction sites. Computer vision (CV) technology presents an efficient solution for collision-risk pre-warning. However, CV-based methods are still relatively rare and need an enhancement of their performance. Therefore, a novel three-stage collision-risk pre-warning model for construction vehicles and workers is proposed in this paper. This model consists of an object-sensing module (OSM), a trajectory prediction module (TPM), and a collision-risk assessment module (CRAM). In the OSM, the YOLOv5 algorithm is applied to identify and locate construction vehicles and workers; meanwhile, the DeepSORT algorithm is applied to the real-time tracking of the construction vehicles and workers. As a result, the historical trajectories of vehicles and workers are sensed. The original coordinates of the data are transformed to common real-world coordinate systems for convenient subsequent data acquisition, comparison, and analysis. Subsequently, the data are provided to a second stage (TPM). In the TPM, the optimized transformer algorithm is used for a real-time trajectory prediction of the construction vehicles and workers. In this paper, we enhance the reliability of the general object detection and trajectory prediction methods in the construction environments. With the assistance afforded by the optimization of the model’s hyperparameters, the prediction horizon is extended, and this gives the workers more time to take preventive measures. Finally, the prediction module indicates the possible trajectories of the vehicles and workers in the future and provides these trajectories to the CRAM. In the CRAM, the worker’s collision-risk level is assessed by a multi-factor-based collision-risk assessment rule, which is innovatively proposed in the present work. The multi-factor-based assessment rule is quantitatively involved in three critical risk factors, i.e., velocity, hazardous zones, and proximity. Experiments are performed within two different construction site scenarios to evaluate the effectiveness of the collision-risk pre-warning model. The research results show that the proposed collision pre-warning model can accurately predict the collision-risk level of workers at construction sites, with good tracking and predicting effect and an efficient collision-risk pre-warning strategy. Compared to the classical models, such as social-GAN and social-LSTM, the transformer-based trajectory prediction model demonstrates a superior accuracy, with an average displacement error of 0.53 m on the construction sites. Additionally, the optimized transformer model is capable of predicting six additional time steps, which equates to approximately 1.8 s. The collision pre-warning model proposed in this paper can help improve the safety of construction vehicles and workers.

This study introduced phosphogypsum coupled with steel slag powder to prepare the phosphogypsum based filler (PF) for asphalt mixture. Penetration, penetration index, softening point, ductility, equivalent softening point, moisture stability of asphalt mortars with different steel slag powder content, filler-asphalt ratio, and PF content were studied. Mechanical properties of PF based asphalt mortar (P-AM) were then analyzed to determine the optimum steel slag content in PF. Overall desirability method was used to determine the optimum replacement ratio of PF content in limestone filler. Rheological properties of P-AM were also analyzed through dynamic shear rheometer. Volumetric performance, high-temperature performance, low-temperature performance, and moisture stability tests were carried out on PF based AC-20 asphalt mixture. Results showed that P-AM presented the optimum performance when the content of steel slag powder was 23% by mass of phosphogypsum. Fatigue and rutting factor of asphalt mortar were enhanced by PF. The optimum PF content in replacing limestone filler was 75% through overall desirability evaluation. PF developed the high-temperature performance and moisture stability of asphalt mixture. Additionally, volumetric and low-temperature performance were not significantly affected by PF. It is suggested that using PF which is based on phosphogypsum as a filler of asphalt mixture to partially replace traditional limestone filler was adequate.