Journal of Road Engineering最新文献

{"title":"State of the art review on inorganic powders modified asphalt materials: Reducing the temperature of asphalt pavement","authors":"Chaohui Wang ,&nbsp;Tonghao Zhang ,&nbsp;Anquan Li ,&nbsp;Dawei Zhao ,&nbsp;Luqing Liu ,&nbsp;Qian Chen","doi":"10.1016/j.jreng.2025.01.001","DOIUrl":"10.1016/j.jreng.2025.01.001","url":null,"abstract":"<div><div>To reduce the temperature diseases of asphalt pavement, improve the service quality of road and extend service life, the research of inorganic powders that reduce the temperature of asphalt pavements was systematically sorted out. The common types, physicochemical properties and application methods of inorganic powders were defined. The road performances of modified asphalt and its mixture were evaluated. The modification mechanism of inorganic powders in asphalt was analyzed. On this basis, the cooling effect and cooling mechanism of inorganic powders was revealed. The results indicate that inorganic powders are classified into hollow, porous, and energy conversion types. The high-temperature performance of inorganic powders modified asphalt and its mixture is significantly improved, while there is no significant change in low-temperature performance and water stability. The average increase in rutting resistance factor (<em>G</em>∗/sin(<em>δ</em>)) and dynamic stability is 40%–72% and 30%–50%, respectively. The modification mechanism of inorganic powders in asphalt is physical blending. The thermal conductivity of hollow and porous inorganic powders modified asphalt mixture decreases by 30.05% and 43.14%, respectively. The temperature of hollow, porous and energy conversion inorganic powders modified asphalt mixture at 5 ​cm decreases by 2.3 °C–3.5 ​°C, 0.8 °C–3.7 ​°C and 4.1 °C–4.7 °C, respectively. Hollow and porous inorganic powders block heat conduction, while energy conversion inorganic powders achieve cooling through their functional properties.</div></div>","PeriodicalId":100830,"journal":{"name":"Journal of Road Engineering","volume":"5 2","pages":"Pages 279-296"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144205680","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Advanced machine learning techniques for predicting mechanical properties of eco-friendly self-compacting concrete","authors":"Arslan Qayyum Khan ,&nbsp;Syed Ghulam Muhammad ,&nbsp;Ali Raza ,&nbsp;Amorn Pimanmas","doi":"10.1016/j.jreng.2024.12.002","DOIUrl":"10.1016/j.jreng.2024.12.002","url":null,"abstract":"<div><div>This study evaluates the performance of advanced machine learning (ML) models in predicting the mechanical properties of eco-friendly self-compacting concrete (SCC), with a focus on compressive strength, V-funnel time, L-box ratio, and slump flow. The motivation for this study stems from the increasing need to optimize concrete mix designs while minimizing environmental impact and reducing the reliance on costly physical testing. Six ML models-backpropagation neural network (BPNN), random forest regression (RFR), <em>K</em>-nearest neighbors (KNN), stacking, bagging, and eXtreme gradient boosting (XGBoost)-were trained and validated using a comprehensive dataset of 239 mix design parameters. The models' predictive accuracies were assessed using the coefficient of determination, mean squared error, root mean squared error, and mean absolute error. XGBoost consistently outperformed other models, achieving the coefficient of determination values of 0.999, 0.933, and 0.935 for compressive strength in the training, validation, and testing datasets, respectively. Sensitivity analysis revealed that cement, silica fume, coarse aggregate, and superplasticizer positively influenced compressive strength, while water content had a negative impact. These findings highlight the potential of ML models, particularly XGBoost and RFR, in optimizing SCC mix designs, reducing reliance on physical testing, and enhancing sustainability in construction. The application of these models can lead to more efficient and eco-friendly concrete mix designs, benefiting real-world construction projects by improving quality control and reducing costs.</div></div>","PeriodicalId":100830,"journal":{"name":"Journal of Road Engineering","volume":"5 2","pages":"Pages 213-229"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144205704","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deploying machine learning for long-term road pavement moisture prediction: A case study from Queensland, Australia","authors":"Ayesh Dushmantha ,&nbsp;Ruixuan Zhang ,&nbsp;Yilin Gui ,&nbsp;Jinjiang Zhong ,&nbsp;Chaminda Gallage","doi":"10.1016/j.jreng.2024.12.007","DOIUrl":"10.1016/j.jreng.2024.12.007","url":null,"abstract":"<div><div>Moisture accumulation within road pavements, particularly in unbound granular materials with or without thin sprayed seals, presents significant challenges in high-rainfall regions such as Queensland. This infiltration often leads to various forms of pavement distress, eventually causing irreversible damage to the pavement structure. The moisture content within pavements exhibits considerable dynamism and directly influenced by environmental factors such as precipitation, air temperature, and relative humidity. This variability underscores the importance of monitoring moisture changes using real-time climatic data to assess pavement conditions for operational management or incorporating these effects during pavement design based on historical climate data. Consequently, there is an increasing demand for advanced, technology-driven methodologies to predict moisture variations based on climatic inputs. Addressing this gap, the present study employs five traditional machine learning (ML) algorithms, <em>K</em>-nearest neighbors (KNN), regression trees, random forest, support vector machines (SVMs), and gaussian process regression (GPR), to forecast moisture levels within pavement layers over time, with varying algorithm complexities. Using data collected from an instrumented road in Brisbane, Australia, which includes pavement moisture and climatic factors, the study develops predictive models to forecast moisture content at future time steps. The approach incorporates current moisture content, rather than averaged values, along with seasonality (both daily and annual), and key climatic factors to predict next step moisture. Model performance is evaluated using <em>R</em>², MSE, RMSE, and MAPE metrics. Results show that ML algorithms can reliably predict long-term moisture variations in pavements, provided optimal hyperparameters are selected for each algorithm. The best-performing algorithms include KNN (the number of neighbours equals to 15), medium regression tree, medium random forest, coarse SVM, and simple GPR, with medium random forest outperforming the others. The study also identifies the optimal hyperparameter combinations for each algorithm, offering significant advancements in moisture prediction tools for pavement technology.</div></div>","PeriodicalId":100830,"journal":{"name":"Journal of Road Engineering","volume":"5 2","pages":"Pages 184-201"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144205790","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluation of HMA and WMA RAP mixture using hydrogenated castor oil flakes","authors":"Soumya Ranjan Baral,&nbsp;Anwesha Rath,&nbsp;Hemanta Kumar Behera,&nbsp;Sudhanshu Sekhar Das","doi":"10.1016/j.jreng.2024.10.002","DOIUrl":"10.1016/j.jreng.2024.10.002","url":null,"abstract":"<div><div>In this study, reclaimed asphalt pavement (RAP) used in different percentages in hot mix asphalt (HMA) and warm mix asphalt (WMA) were tested for moisture, fracture and rutting resistance adding hydrogenated castor oil flakes (HCOF) as rejuvenating agent. Volumetric and Marshall parameters were evaluated for both types of mixtures. Addition of 5% of HCOF by weight of binder content in RAP found to restore properties of aged binder. WMA mix was made by adding 0.1% Zycotherm by weight of optimum binder content. Moisture, rutting and fracture damage performance were assessed utilizing indirect tensile strength, wheel tracking and semi-circular bending tests. The mix's tensile strength ratio increased by 2.3% in the HMA with RAP (HMA-R) mix compared to the WMA with RAP (WMA-R) mix at 10% RAP content. HMA mixes provide better resistance to rutting compared to WMA. However, 40% of RAP content HMA-R and WMA-R using HCOF rejuvenator shows greater rutting performance compared to other RAP mix. HMA-R mix fracture resistance increased by 18.14% compared to WMA-R mix when RAP content increases to 40%. Regression analyses were carried out to validate the strain energy found from fracture damage analysis of both HMA-R and WMA-R with <em>R</em>² value more than 0.9. HMA-R protected moisture and fracture damage better than WMA-R. The rejuvenating efficiency of HCOF was further validated using Fourier transform infrared and microscopic analysis.</div></div>","PeriodicalId":100830,"journal":{"name":"Journal of Road Engineering","volume":"5 2","pages":"Pages 297-312"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144205681","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"RAP agglomeration and partial blending of recycled hot mix asphalt: A literature review","authors":"Xinman Ai,&nbsp;Zhongshi Pei,&nbsp;Ke Xu,&nbsp;Wenyi Zhou,&nbsp;Ying Wang,&nbsp;Decheng Feng,&nbsp;Junyan Yi","doi":"10.1016/j.jreng.2024.12.006","DOIUrl":"10.1016/j.jreng.2024.12.006","url":null,"abstract":"<div><div>Current mix design practices typically assume total blending and use the white curve of reclaimed asphalt pavement (RAP) to determine the gradation and optimum asphalt content (OAC) of recycled hot mix asphalt (HMA), often overlooking the effects of RAP agglomeration and partial blending. This oversight can result in unsatisfactory performance, particularly when higher RAP content is used. Therefore, this paper reviews and discusses strategies for adjusting the mix design of recycled HMA to enhance its in-service performance. The discussion begins with RAP particle agglomeration, a significant phenomenon that significantly impacts the aggregate gradation of recycled HMA. Subsequently, detection methods to clarify the blending between virgin and RAP binders are described. Partial blending between RAP and virgin binders is common, and various indexes have been proposed to quantify the blending degree. Finally, the adjusted mix design method of recycled HMA is presented, emphasizing gradation optimization and corrected OAC. Gradation optimization should account for RAP agglomeration, while the corrected OAC should consider particle blending. Recycled HMA using the adjusted mix design exhibits improved crack resistance and fatigue life without substantially impairing rutting performance. This review aims to help both academics and highway agencies maximize the utilization of RAP materials within sustainable pavement frameworks.</div></div>","PeriodicalId":100830,"journal":{"name":"Journal of Road Engineering","volume":"5 2","pages":"Pages 230-243"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144205674","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Application of isocyanate-based materials in asphalt pavement: A review","authors":"Xing Gong ,&nbsp;Quantao Liu ,&nbsp;Haiqin Xu ,&nbsp;Huan Wang ,&nbsp;Shaopeng Wu","doi":"10.1016/j.jreng.2024.12.005","DOIUrl":"10.1016/j.jreng.2024.12.005","url":null,"abstract":"<div><div>Isocyanate and its products are playing an increasingly important role in the high-performance development of asphalt pavement, but researchers have always focused on polyurethane (PU), one of the isocyanate products, and neglected the other roles of isocyanate-based materials in asphalt pavement. The application of isocyanate-based materials in asphalt pavement is still in the exploratory stage, and the research direction is not clear. It is necessary to summarize and propose research directions for the application of isocyanate-based materials in asphalt pavement. Therefore, this paper reviews the application of isocyanate-based materials in asphalt pavement, classifies the products synthesized from isocyanate for asphalt binder, introduces the application effects of different isocyanate-based materials in asphalt binder, and analyzes the limitations of each material. Meanwhile, the other roles of isocyanate-based materials in asphalt pavement, such as coating materials and adhesive materials, are summarized. Finally, the development direction of isocyanate-based materials in asphalt pavement is prospected. Isocyanate-based materials are expected to significantly increase the service life of asphalt pavement because of their excellent properties. With the advancement of technology, the application of isocyanate-based materials will become more and more common, promoting the sustainable development of road construction. This paper can provide a reference for the development and application of isocyanate-based materials in asphalt pavement.</div></div>","PeriodicalId":100830,"journal":{"name":"Journal of Road Engineering","volume":"5 2","pages":"Pages 261-278"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144205679","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Soft computing applications in asphalt pavement: A comprehensive review of data-driven techniques using response surface methodology and machine learning","authors":"Nura Shehu Aliyu Yaro ,&nbsp;Muslich Hartadi Sutanto ,&nbsp;Mohd Rosli Hainin ,&nbsp;Noor Zainab Habib ,&nbsp;Aliyu Usman ,&nbsp;Muhammad Sani Bello ,&nbsp;Surajo Abubakar Wada ,&nbsp;Abiola Usman Adebanjo ,&nbsp;Ahmad Hussaini Jagaba","doi":"10.1016/j.jreng.2024.12.003","DOIUrl":"10.1016/j.jreng.2024.12.003","url":null,"abstract":"<div><div>The asphalt pavement industry is transforming because of the growing influence of artificial intelligence and industrial digitization. As a result of this shift, there is a stronger emphasis on advanced statistical approaches like optimization tools like response surface methodology (RSM) and machine learning (ML) techniques. The goal of this paper is to provide a scientometric and systematic review of the application of RSM and ML applications in data-driven approaches such as optimizing, modeling, and predicting asphalt pavement performance to achieve sustainable asphalt pavements in support of numerous sustainable development goals (SDGs). These include Goals 9 (sustainable infrastructure), 11 (urban resilience), 12 (sustainable construction strategies), 13 (climate action through optimized materials), and 17 (multidisciplinary interaction). A thorough search of the ScienceDirect, Web of Science, and Scopus databases from 2010 to 2023 yielded 1249 relevant records, with 125 studies closely examined. Over the last thirteen years, there has been significant research growth in RSM and ML applications, particularly in ML-based pavement optimization. The study shows that the topic has a global presence, with notable contributions from Asia, North America, Europe, and other continents. Researchers have concentrated on utilizing sophisticated ML models such as support vector machines (SVM), artificial neural networks (ANN), and Bayesian networks for prediction. Also, the integration of RSM and ML provides a faster and more efficient method for analyzing large datasets to optimize asphalt pavement performance variables. Key contributors include the United States, China, and Malaysia, with global efforts focused on sustainable materials and approaches to reduce impact on the environment. Furthermore, the review demonstrates the integrated use of RSM and ML as transformative tools for improving sustainability, which contributes significantly to SDGs 9, 11, 12, 13, and 17. Providing valuable insights for future research and guiding decision-making for soft computing applications for asphalt pavement projects.</div></div>","PeriodicalId":100830,"journal":{"name":"Journal of Road Engineering","volume":"5 2","pages":"Pages 129-163"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144205789","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Learning models for predicting pavement friction based on non-contact texture measurements: Comparative assessment","authors":"Xiuquan Lin ,&nbsp;You Zhan ,&nbsp;Zilong Nie ,&nbsp;Joshua Qiang Li ,&nbsp;Xinyu Zhu ,&nbsp;Allen A. Zhang","doi":"10.1016/j.jreng.2024.11.003","DOIUrl":"10.1016/j.jreng.2024.11.003","url":null,"abstract":"<div><div>Ensuring highway safety relies heavily on pavement friction resistance. To enable network-level pavement skid resistance monitoring and management, this study proposes a non-contact three-dimensional laser surface testing method to obtain detailed aggregate surface data. The existing contact-based skid resistance measurement methods suffer from poor reproducibility and repeatability, hindering their application for network-level management. In this research, traditional multiple linear regression and four machine learning methods, support vector machine (SVM), random forest (RF), gradient boosting decision tree (GBDT), and convolutional neural network (CNN), are utilized to evaluate and predict pavement frictional performance. To assess the proposed methods, data from 45 pavement sites in Oklahoma, including 6 major preventive maintenance (PM) treatments and 7 typical types of aggregates, are collected. Parallel data acquisition is conducted at highway speeds using a grip tester and a high-speed texture profiler to measure pavement skid resistance and surface macro-texture, respectively. Aggregate properties are captured in 3D using a portable ultra-high-resolution 3D laser imaging scanner, leading to the calculation of four types of 3D aggregate parameters characterizing the micro-texture of aggregate surfaces. The relationship between pavement surface friction and texture is explored using machine learning models. The results reveal that the random forest and gradient boosting decision tree models exhibit the highest accuracy, SVM and CNN perform moderately, while the traditional linear regression method fares the worst. By assessing the importance of the 38 parameter variables, the most critical 21 variables were selected for model development. Test results demonstrate that the GBDT model exhibits the best predictive performance, with an explanatory capability of 87.4​% for road friction performance. The findings demonstrate the feasibility of replacing contact-based pavement friction evaluation with non-contact texture measurements, offering promising prospects for a network-level pavement skid resistance monitoring and management system.</div></div>","PeriodicalId":100830,"journal":{"name":"Journal of Road Engineering","volume":"5 2","pages":"Pages 202-212"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144205673","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hydrogen peroxide activation of waste tire crumb rubber for improving compatibility with bitumen: Laboratory and molecular dynamics insights","authors":"Nie Tian,&nbsp;Piergiorgio Tataranni,&nbsp;Cesare Sangiorgi","doi":"10.1016/j.jreng.2024.12.004","DOIUrl":"10.1016/j.jreng.2024.12.004","url":null,"abstract":"<div><div>Enhancing rubber-bitumen compatibility is crucial to improve pavement performance and durability. To investigate the compatibility improvement between H₂O₂-activated waste crumb rubber (AWCR) and bitumen, coarse and fine waste crumb rubber (WCR) were treated and analyzed through multi-scale characterization and molecular simulation. Microstructure and chemical changes of WCR and AWCR were analyzed with scanning electron microscope (SEM), contact angle tests and Fourier transform infrared spectroscopy (FTIR). Compatibility was also indirectly evaluated through modified boiling tests and storage stability tests. Besides, molecular dynamics was used to explore the interaction between WCR/AWCR and bitumen. SEM, contact angle, and FTIR results showed bond breakage of C<img>C and C–C and increased polar groups like –OH and –COOH in AWCR, resulting in a rougher texture and higher surface energy. Compared with WCR, AWCR showed a lower bitumen stripping rate after boiling, and the binder with AWCR also had a lower softening point difference and segregation rate after storage. Molecular dynamics simulations further confirmed that AWCR has a closer solubility parameter and higher binding energy to bitumen than WCR, reflected in a relatively slower diffusion rate. This study provides comprehensive evidence for an eco-friendly method of WCR surface treatment for more efficient recycling of tire rubber in asphalt pavements.</div></div>","PeriodicalId":100830,"journal":{"name":"Journal of Road Engineering","volume":"5 2","pages":"Pages 244-260"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144205675","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Research progress of intelligent testing technology and evaluation methods for subgrade engineering","authors":"Guojun Cai ,&nbsp;Hongliang Tian ,&nbsp;Lulu Liu ,&nbsp;Xiaoyan Liu ,&nbsp;Songyu Liu","doi":"10.1016/j.jreng.2025.03.001","DOIUrl":"10.1016/j.jreng.2025.03.001","url":null,"abstract":"<div><div>Subgrade engineering is a fundamental aspect of infrastructure construction in China. As the primary structural element responsible for bearing and distributing traffic loads, the subgrade must not only withstand the substantial pressures exerted by vehicles, trains, and other forms of transportation, but also efficiently transfer these loads to the underlying foundation, ensuring the stability and longevity of the roadway. In recent years, advancements in subgrade engineering technology have propelled the industry towards smarter, greener, and more sustainable practices, particularly in the areas of intelligent monitoring, disaster management, and innovative construction methods. This paper reviews the application and methodologies of intelligent testing equipment, including cone penetration testing (CPT) devices, soil resistivity testers, and intelligent rebound testers, in subgrade engineering. It examines the operating principles, advantages, limitations, and application ranges of these tools in subgrade testing. Additionally, the paper evaluates the practical use of advanced equipment from both domestic and international perspectives, addressing the challenges encountered by various instruments in real-world applications. These devices enable precise, comprehensive testing and evaluation of subgrade conditions at different stages, providing real-time data analysis and intelligent early warnings. This supports effective subgrade health management and maintenance. As intelligent technologies continue to evolve and integrate, these tools will increasingly enhance the accuracy, efficiency, and sustainability of subgrade monitoring.</div></div>","PeriodicalId":100830,"journal":{"name":"Journal of Road Engineering","volume":"5 2","pages":"Pages 164-183"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144205682","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}