Advanced Techniques in Smart Road Construction and Maintenance: Multi-Scale Characterization and Real-Time Performance Evaluation

A special issue of Buildings (ISSN 2075-5309). This special issue belongs to the section "Building Materials, and Repair & Renovation".

Deadline for manuscript submissions: 30 April 2025 | Viewed by 549

Special Issue Editors


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Guest Editor
School of Traffic and Transportation Engineering, Changsha University of Science and Technology, Changsha 410114, China
Interests: long-life pavement structure design theory and methods; high-performance pavement materials; pavement service performance; pavement digital twins; intelligent road construction and maintenance
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Guest Editor
National Engineering Research Center of Highway Maintenance Technology, Changsha University of Science and Technology, Changsha 410114, China
Interests: Long-term performance of asphalt pavement; fatigue performance; reclaimed asphalt pavement; bio-oil modified asphalt; machine learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Smart roads represent new infrastructure that integrates traditional roads with next-generation information technologies. The growing demand for smart and safe travel presents new challenges for road infrastructure. Throughout the service life of asphalt pavements, factors such as construction quality, traffic loads, and environmental conditions complicate the accurate extraction and integration of performance data. These complex service environments also affect the behavior of asphalt pavements, reducing their lifespan and potentially leading to functional degradation.

Advanced technologies are essential for characterizing the performance of asphalt pavements and understanding their evolution under complex conditions. Enhancing the resistance of pavement materials to these challenges is crucial. Moreover, accurate condition monitoring and real-time performance evaluation are key to ensuring long-term road serviceability. This Special Issue aims to explore how multi-scale technologies and intelligent methods can analyze the properties of bitumen and asphalt materials, optimize road design, and enable real-time diagnostics, providing essential support for smart road development.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but need not be limited to) the following:

  • Intelligent road design and construction;
  • Multi-scale analysis of bitumen and asphalt mixtures;
  • Smart and multifunctional asphalt mixtures;
  • Real-time diagnosis and evaluation;
  • Advanced testing technologies for bitumen and asphalt mixtures;
  • Embedded sensing systems;
  • Service performance of asphalt pavement.

We look forward to receiving your contributions.

Prof. Dr. Songtao Lv
Dr. Xinghai Peng
Guest Editors

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Buildings is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • intelligent road construction
  • multifunctional pavement
  • real-time diagnosis and evaluation
  • environmentally friendly materials
  • polymer-modified asphalt
  • smart materials and structure
  • multi-source data fusion
  • embedded sensing systems
  • long-life pavement structure
  • numerical simulation

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Published Papers (1 paper)

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Research

17 pages, 4110 KiB  
Article
Optimization Design of Cotton-Straw-Fiber-Modified Asphalt Mixture Performance Based on Response Surface Methodology
by Guihua Hu, Xiaowei Chen, Zhonglu Cao and Lvzhen Yang
Buildings 2024, 14(11), 3670; https://doi.org/10.3390/buildings14113670 - 18 Nov 2024
Viewed by 385
Abstract
This research explored the application of cotton straw fiber in asphalt mixtures, aiming to optimize the asphalt mixtures’ performance. Firstly, 17 experiments were designed using Response Surface Methodology (RSM). Subsequently, the Box–Behnken Design (BBD) was used to examine how the asphalt content, fiber [...] Read more.
This research explored the application of cotton straw fiber in asphalt mixtures, aiming to optimize the asphalt mixtures’ performance. Firstly, 17 experiments were designed using Response Surface Methodology (RSM). Subsequently, the Box–Behnken Design (BBD) was used to examine how the asphalt content, fiber length, and cotton straw fiber content interacted to affect the modified asphalt mixes’ pavement performance. Based on the experimental findings, performance prediction models were created to direct optimization. The optimized design was then validated through pavement performance tests and bending fatigue tests. The findings revealed that cotton straw fiber content, length, and asphalt content significantly influence the performance of modified asphalt mixtures. The inclusion of cotton straw fibers enhanced various properties of the mixtures. When the fiber content was set at 0.3%, fiber length at 6 mm, and asphalt content at 5.3%, the response indicators, including Marshall stability, dynamic stability, flexural strength, and freeze–thaw strength ratio, were measured at 12.246 kN, 2452.396 times/mm, 12.30 MPa, and 92.76%, respectively. These results indicate that the cotton-straw-fiber-modified asphalt mixture achieved optimal performance while meeting regulatory requirements. Additionally, fatigue tests showed that the cotton-straw-fiber-modified asphalt mixture exhibited superior fatigue resistance compared with the SBS-modified asphalt mixture. The maximum error between the RSM predictions and the experimental measurements was within 10%, demonstrating the accuracy of the predictive models in estimating the impact of different factors on asphalt mixture performance. The application of RSM in designing and optimizing cotton-straw-fiber-modified asphalt mixtures proved to be highly effective, offering valuable insights for utilizing cotton straw fibers in road construction. Full article
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