Journal of Road Engineering最新文献

The pressing demand for sustainable advancements in road infrastructure has catalyzed extensive research into environmentally conscious alternatives for the maintenance and restoration of asphalt concrete pavements. This paper offers a comprehensive review and analysis of bio-based rejuvenators as a promising avenue for enhancing the longevity and sustainability of asphalt. Through a multifaceted exploration, it delves into various aspects of this innovative approach. Providing a thorough overview of bio-based rejuvenators, the study highlights their renewable and environmentally friendly characteristics. It conducts an in-depth examination of a wide spectrum of bio-derived materials, including vegetable oils, waste-derived bio-products, and biopolymers, through a comprehensive survey. The paper evaluates how bio-based rejuvenators enhance aged asphalt binders and mixes, effectively mitigating the adverse impacts of aging. Furthermore, it investigates how these rejuvenators address environmental concerns by identifying compatibility issues, assessing long-term performance, and evaluating economic feasibility. Finally, the paper outlines potential advancements and research pathways aimed at optimizing the utilization of bio-based rejuvenators in asphalt concrete, thereby contributing to the sustainable evolution of road infrastructure.

Automated pavement condition survey is of critical importance to road network management. There are three primary tasks involved in pavement condition surveys, namely data collection, data processing and condition evaluation. Artificial intelligence (AI) has achieved many breakthroughs in almost every aspect of modern technology over the past decade, and undoubtedly offers a more robust approach to automated pavement condition survey. This article aims to provide a comprehensive review on data collection systems, data processing algorithms and condition evaluation methods proposed between 2010 and 2023 for intelligent pavement condition survey. In particular, the data collection system includes AI-driven hardware devices and automated pavement data collection vehicles. The AI-driven hardware devices including right-of-way (ROW) cameras, ground penetrating radar (GPR) devices, light detection and ranging (LiDAR) devices, and advanced laser imaging systems, etc. These different hardware components can be selectively mounted on a vehicle to simultaneously collect multimedia information about the pavement. In addition, this article pays close attention to the application of artificial intelligence methods in detecting pavement distresses, measuring pavement roughness, identifying pavement rutting, analyzing skid resistance and evaluating structural strength of pavements. Based upon the analysis of a variety of the state-of-the-art artificial intelligence methodologies, remaining challenges and future needs with respect to intelligent pavement condition survey are discussed eventually.

This study introduces and evaluates a novel artificial hummingbird algorithm-optimised boosted tree (AHA-boosted) model for predicting the dynamic modulus (E∗) of hot mix asphalt concrete. Using a substantial dataset from NCHRP Report-547, the model was trained and rigorously tested. Performance metrics, specifically RMSE, MAE, and R², were employed to assess the model's predictive accuracy, robustness, and generalisability. When benchmarked against well-established models like support vector machines (SVM) and gaussian process regression (GPR), the AHA-boosted model demonstrated enhanced performance. It achieved R² values of 0.997 in training and 0.974 in testing, using the traditional Witczak NCHRP 1-40D model inputs. Incorporating features such as test temperature, frequency, and asphalt content led to a 1.23% increase in the test R², signifying an improvement in the model's accuracy. The study also explored feature importance and sensitivity through SHAP and permutation importance plots, highlighting binder complex modulus |G∗| as a key predictor. Although the AHA-boosted model shows promise, a slight decrease in R² from training to testing indicates a need for further validation. Overall, this study confirms the AHA-boosted model as a highly accurate and robust tool for predicting the dynamic modulus of hot mix asphalt concrete, making it a valuable asset for pavement engineering.

Microbial geoengineering technology, as a new eco-friendly rock and soil improvement and reinforcement technology, has a wide application prospect. However, this technology still has many deficiencies and is difficult to achieve efficient curing, which has become the bottleneck of large-scale field application. This paper reviews the research status, hot spots, difficulties and future development direction microbial induced calcium carbonate precipitation (MICP) technology. The principle of solidification and the physical and mechanical properties of improved rock and soil are systematically summarized. The solidification efficiency is mainly affected by the reactant itself and the external environment. At present, the MICP technology has been preliminarily applied in the fields of soil solidification, crack repair, anti-seepage treatment, pollution repair and microbial cement. However, the technology is currently mainly limited to the laboratory level due to the difficulty of homogeneous mineralization, uneconomical reactants, short microbial activity period and large environmental interference, incidental toxicity of metabolites and poor field application. Future directions include improving the uniformity of mineralization by improving grouting methods, improving urease persistence by improving urease activity, and improving the adaptability of bacteria to the environment by optimizing bacterial species. Finally, the authors point out the economic advantages of combining soybean peptone, soybean meal and cottonseed as carbon source with phosphogypsum as calcium source to induce CaCO₃.

Longitudinal joint construction quality is critical to the life of flexible pavements. Maintaining deteriorated longitudinal joints has become a challenge for many highway agencies. Improving the joint's quality through better compaction during construction can help achieve flexible pavements with longer service lives and less maintenance. Current quality control (QC) and quality assurance (QA) plans provide limited coverage. Consequently, the risk of missing areas with poor joint compaction is significant. A density profiling system (DPS) is a non-destructive alternative to conventional destructive evaluation methods. It can provide quick and continuous real-time coverage of the compaction during construction in dielectrics. The paper presents several case studies comparing various types of longitudinal joints and demonstrating the use of DPS to evaluate the joint's compaction quality. The paper shows that dielectric measurements can provide valuable insight into the ability of various construction techniques to achieve adequate levels of compaction at the longitudinal joint. The paper proposes a dielectric-based longitudinal joint quality index (LJQI) to evaluate the relative compaction of the joint during construction. It also shows that adopting DPS for assessing the compaction of longitudinal joints can minimize the risk of agencies accepting poorly constructed joints, identify locations of poor quality during construction, and achieve better-performing flexible pavements.

Due to the rapid advancement of the transportation industry and the continual increase in pavement infrastructure, it is difficult to keep up with the huge road maintenance task by relying only on the traditional manual detection method. Intelligent pavement detection technology with deep learning techniques is available for the research and industry areas by the gradual development of computer vision technology. Due to the different characteristics of pavement distress and the uncertainty of the external environment, this kind of object detection technology for distress classification and location still faces great challenges. This paper discusses the development of object detection technology and analyzes classical convolutional neural network (CNN) architecture. In addition to the one-stage and two-stage object detection frameworks, object detection without anchor frames is introduced, which is divided according to whether the anchor box is used or not. This paper also introduces attention mechanisms based on convolutional neural networks and emphasizes the performance of these mechanisms to further enhance the accuracy of object recognition. Lightweight network architecture is introduced for mobile and industrial deployment. Since stereo cameras and sensors are rapidly developed, a detailed summary of three-dimensional object detection algorithms is also provided. While reviewing the history of the development of object detection, the scope of this review is not only limited to the area of pavement crack detection but also guidance for researchers in related fields is shared.

Recent trends in road engineering have explored the potential of incorporating recycled solid wastes into infrastructures that including pavements, bridges, tunnels, and accessory structures. The utilization of solid wastes is expected to offer sustainable solutions to waste recycling while enhancing the performance of roads. This review provides an extensive analysis of the recycling of three main types of solid wastes for road engineering purposes: industrial solid waste, infrastructure solid waste, and municipal life solid waste. Industrial solid wastes suitable for road engineering generally include coal gangue, fly ash, blast furnace slag, silica fume, and steel slag, etc. Infrastructure solid wastes recycled in road engineering primarily consist of construction & demolition waste, reclaimed asphalt pavements, and recycled cement concrete. Furthermore, recent exploration has extended to the utilization of municipal life solid wastes, such as incinerated bottom ash, glass waste, electronics waste, plastic waste, and rubber waste in road engineering applications. These recycled solid wastes are categorized into solid waste aggregates, solid waste cements, and solid waste fillers, each playing distinct roles in road infrastructure. Roles of solid waste acting aggregates, cements, and fillers in road infrastructures were fully investigated, including their pozzolanic properties, integration effects to virgin materials, modification or enhancement solutions, engineering performances. Utilization of these materials not only addresses the challenge of waste management but also offers environmental benefits aiming carbon neutral and contributes to sustainable infrastructure development. However, challenges such as variability in material properties, environmental impact mitigation, secondary pollution to environment by leaching, and concerns regarding long-term performance need to be further addressed. Despite these challenges, the recycled solid wastes hold immense potential in revolutionizing road construction practices and fostering environmental stewardship. This review delves into a bird’s-eye view of the utilization of recycled solid wastes in road engineering, highlighting advances, benefits, challenges, and future prospects.

Rigid-flexible composite pavement has gained significant popularity in recent decades. This paper provides a comprehensive review of the research progress concerning rigid-flexible composite pavement, aiming to promote its application and address key issues while identifying future directions. The design theory and methodology of rigid-flexible composite pavement are discussed, followed by a description of its structural and mechanical behavior characteristics. The load stress, temperature stress, and their interactive effects between the asphalt layer and the rigid base were analyzed. It is clarified that the asphalt layer serves a dual role as both a “functional layer” and a “structural layer”. Typical distresses of rigid-flexible composite pavement, which primarily occur in the asphalt layer, were discussed. These distresses include reflective cracking, top-down cracking, rutting, and compressive-shear failure. Generally, the integrity of the rigid base and the interlaminar bonding conditions significantly impact the performance and distress of the asphalt layer. The technology for enhancing the performance of rigid-flexible composite pavement is summarized in three aspects: asphalt layer properties, rigid base integrity, and interlaminar bonding condition. The study concludes that developing high-performance pavement materials based on their structural behaviors is an effective approach to improve the performance and durability of rigid-flexible composite pavement. The integrated design of structure and materials represents the future direction of road design.

Many industrial sectors exploit fossil sources to develop useful and necessary materials for our needs, such as bituminous paving materials. Bitumen, a key component of asphalt mixtures, is derived from oil refining and its properties are influenced by the crude oil source and refining process, resulting in a significant carbon footprint. With growing awareness of resource depletion and environmental concerns, pavement researchers are exploring sustainable alternatives to reduce dependence on fossil sources. This includes a rising trend in using renewable materials like biomasses to produce bio-based binders as substitutes for bitumen, aiming for a more sustainable approach. Biomasses, including vegetal and animal wastes, and waste cooking oils, as substitutes for crude oil in the production of bio-binders. Through thermochemical conversion (TCC), such as pyrolysis, biomasses can be converted into bio-char and bio-oils, which can replace fossil-based components in binders. Researchers have utilized these bio-products to reduce the dependency on fossil fuels in binders. However, there are no set minimum requirements for bio-components in bio-based binders. As the percentage of replaced bitumen increases, various types of binders are produced, including modified bitumen, extended bitumen, and alternative binders, where the fossil replacement is gradual. Overall rheological tests on bio-binders, reveal that those containing bio-char exhibit increased viscosity, stiffness, rutting resistance, and sometimes antioxidant properties. Conversely, bio-binders with bio-oils as bitumen substitutes show poorer performance at high temperatures but improved behavior at low temperatures. These results suggest that bio-binders could provide versatile solutions for various climatic and loading conditions in road construction. However, the development of pavement mixtures based on bio-binders has not been studied in depth and requires further attention to unlock its full potential. As sustainability considerations, including life cycle assessments (LCA) and life cycle cost analyses (LCC), are crucial aspects for future studies. It is essential not only to collect data on the performance characteristics of bio-binders but also to understand their environmental impact and recyclability. In-depth evaluations using methods such as LCA and LCC will provide valuable insights into the overall sustainability and long-term viability of these products.

Road transportation plays a crucial role in society and daily life, as the functioning and durability of roads can significantly impact a nation's economic development. In the whole life cycle of the road, the emergence of disease is unavoidable, so it is necessary to adopt relevant technical means to deal with the disease. This study comprehensively reviews the advancements in computer vision, artificial intelligence, and mobile robotics in the road domain and examines their progress and applications in road detection, diagnosis, and treatment, especially asphalt roads. Specifically, it analyzes the research progress in detecting and diagnosing surface and internal road distress and related techniques and algorithms are compared. In addition, also introduces various road governance technologies, including automated repairs, intelligent construction, and path planning for crack sealing. Despite their proven effectiveness in detecting road distress, analyzing diagnoses, and planning maintenance, these technologies still confront challenges in data collection, parameter optimization, model portability, system accuracy, robustness, and real-time performance. Consequently, the integration of multidisciplinary technologies is imperative to enable the development of an integrated approach that includes road detection, diagnosis, and treatment. This paper addresses the challenges of precise defect detection, condition assessment, and unmanned construction. At the same time, the efficiency of labor liberation and road maintenance is achieved, and the automation level of the road engineering industry is improved.