Journal of Road Engineering最新文献

Recently, researchers in the road field are focusing on the development of green asphalt materials with lower emission of volatile organic compounds (VOCs). The characterization methodology of asphalt VOCs and the influencing factors on VOCs release have always been the basic issue of asphalt VOCs emission reduction research. Researchers have proposed a variety of asphalt VOCs characterization methodologies, which also have mutually irreplaceable characteristics. Asphalt VOCs volatilization is affected by many factors. In this study, asphalt VOCs characterization methodologies were summarized, including their advantages, disadvantages, characteristics and applicable requirements. Subsequently, the influencing factors of VOCs release, such as asphalt types and environment conditions, are summarized to provide theoretical support for the emission reduction research. The classification and mechanism of newly-development asphalt VOCs emission reduction materials are reviewed. The reduction efficiencies are also compared to select better materials and put forward the improvement objective of new materials and new processes. In addition, the prospects about development of VOCs release mechanism of asphalt materials during the full life cycle and feasibility research of high-efficiency composite emission reduction materials in the future were put forward.

The goals of this study are to assess the viability of waste tire-derived char (WTDC) as a sustainable, low-cost fine aggregate surrogate material for asphalt mixtures and to develop the statistically coupled neural network (SCNN) model for predicting volumetric and Marshall properties of asphalt mixtures modified with WTDC. The study is based on experimental data acquired from laboratory volumetric and Marshall properties testing on WTDC-modified asphalt mixtures (WTDC-MAM). The input variables comprised waste tire char content and asphalt binder content. The output variables comprised mixture unit weight, total voids, voids filled with asphalt, Marshall stability, and flow. Statistical coupled neural networks were utilized to predict the volumetric and Marshall properties of asphalt mixtures. For predictive modeling, the SCNN model is employed, incorporating a three-layer neural network and preprocessing techniques to enhance accuracy and reliability. The optimal network architecture, using the collected dataset, was a 2:6:5 structure, and the neural network was trained with 60% of the data, whereas the other 20% was used for cross-validation and testing respectively. The network employed a hyperbolic tangent (tanh) activation function and a feed-forward backpropagation. According to the results, the network model could accurately predict the volumetric and Marshall properties. The predicted accuracy of SCNN was found to be as high value ​>98% and low prediction errors for both volumetric and Marshall properties. This study demonstrates WTDC's potential as a low-cost, sustainable aggregate replacement. The SCNN-based predictive model proves its efficiency and versatility and promotes sustainable practices.

Pavement infrastructure is vital in providing services and links between various sectors of society. Therefore, the preservation and maintenance of these roads are critical to attaining a pavement network in good condition throughout its service life. Various performance indicators like the international roughness index (IRI), pavement condition index (PCI), and present serviceability rating (PSR) have been used by the state department of transportation (DOT) and highway agencies for evaluating pavement surface conditions and planning future maintenance strategies. Limited data availability, multiple distresses depending on region, lack of correlation of these condition indices to maintenance strategies, and data collection limitations pose a challenge for applying these indices to local conditions. This paper compares condition indices of different states for rigid pavements. Further, using a specific condition index for local conditions is also highlighted. For this purpose, five states and their corresponding condition indices were evaluated and compared to the Michigan DOT distress index (DI). These states include Virginia, Minnesota, North Dakota, Louisiana, and Oregon. The corresponding distresses of each condition index were converted to make them compatible with the MDOT DI. This study used the MDOT's pavement management system (PMS) database to evaluate each condition index for 433 rigid pavement sections. Each distress index was plotted against MDOT DI and compared using a paired t-test. Results show that the condition indices of Virginia and Minnesota are comparable to DI in terms of the Spearman correlation value. The t-test results show that except for Virgina, condition indices from other states statistically differ from DI. Therefore, one can't use those directly for local conditions in Michigan. This paper presents the evaluation and data requirements for each condition index and its impact on selecting a maintenance treatment.

To comprehensively assess the current state-of-art in asphalt foaming technology, the following four key aspects have been reviewed systematically: foaming principles, test methods, evaluation indicators, and influencing factors. Key findings reveal that asphalt foaming was primarily driven by the vaporization of water, with deterioration processes including bubble collapse and liquid film drainage. However, the current understanding of asphalt foaming principles remains limited, primarily due to difficulties in capturing and precisely measuring its microscopic behaviors during asphalt foaming process. Volume changes provided an intuitive means to evaluate the expansion capacity of asphalt and its foaming stability. Bubble evolution characteristics of foamed asphalt offered promising insights into its foaming performance. Traditional ruler and stopwatch-based assessments were being superseded by automated techniques like laser and ultrasonic ranging. Nevertheless, the current measuring equipment still lacks the capability to comprehensively evaluate the foaming effect of asphalt across various dimensions. Asphalt temperature and foaming water consumption significantly affected asphalt foaming performance, and the inclusion of foaming agents typically led to a notable increase in the half life of foamed asphalt. However, the interaction between foaming agents and asphalt, as well as the underlying mechanisms affecting the foaming effect, are still unclear and require further exploration. Future research should primarily focus on the correlation between asphalt foaming effect and mixture performance, aiming to guide the practical engineering application of foamed asphalt mixtures and enlarge the advantages of such low-emission and sustainable mixtures.

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.