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Journals
Infrastructures
Special Issues
Pavement Design and Pavement Management
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Pavement Design and Pavement Management

A special issue of Infrastructures (ISSN 2412-3811).

Deadline for manuscript submissions: 31 December 2024 | Viewed by 4371

Special Issue Editor

Prof. Dr. Adelino Jorge Lopes Ferreira

E-Mail Website
Guest Editor
Department of Civil Engineering, University of Coimbra, 3030-788 Coimbra, Portugal
Interests: pavement design; pavement maintenance management; maintenance and rehabilitation of pavements; pavement energy harvesting; pavement management systems; transport infrastructure management; road safety management systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Driven by the increasing global awareness of sustainability values and the effects of climate change, governments, transport infrastructure management agencies, and private infrastructure owners are all determined to make their businesses more sustainable. Within this context, road and airport pavements are a type of transport infrastructure particularly meaningful for consideration due to the existing long service life requirements, the considerable consumption of energy and non-renewable resources, and the significant generation of emissions and waste during their construction, maintenance, rehabilitation, and operation. Sustainable practices regarding pavement design, the selection of paving materials, the production of mixtures, and construction activities are important, but there are other sustainability opportunities that result from considering, in an integrated way, the whole pavement’s life cycle, the recent advances in computational optimization and simulation techniques, and the availability of more computational power.

We welcome the submission of articles that could help to enhance stakeholders' capacities to make strategic and more informed decisions regarding the design, construction, maintenance, rehabilitation, and operation of road and airport pavements, which would ultimately enhance the sustainability of transport systems. Specifically, the studies included in this Special Issue are expected to address cutting-edge research and developments in the following subject areas:

  • Pavement design methodologies;
  • Construction techniques and strategies;
  • Rehabilitation, preservation, and maintenance strategies;
  • Pavement management systems;
  • Pavement maintenance optimization models;
  • Pavement performance prediction models;
  • User costs models;
  • Life-cycle cost analysis;
  • Life-Cycle Assessment (LCA);
  • Social life-cycle assessment;
  • Multicriteria decision making methods;
  • Artificial Intelligence (AI) applied to pavements;
  • Operational research methods applied to pavements;
  • Other advanced analytical and computational techniques applied to pavements.

Prof. Dr. Adelino Jorge Lopes Ferreira
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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. Infrastructures 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 1800 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

  • roads
  • airports
  • pavement design
  • pavement management
  • maintenance and rehabilitation of pavements
  • sustainability
  • Life-Cycle Assessment (LCA)
  • life-cycle cost analysis
  • life-cycle sustainability assessment
  • optimization models
  • Artificial Intelligence
  • simulation models
  • operational research methods

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Published Papers (3 papers)

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Research

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24 pages, 6430 KiB  
Article
A Sequence-Based Hybrid Ensemble Approach for Estimating Trail Pavement Roughness Using Smartphone and Bicycle Data
by Yazan Ibrahim Alatoom, Zia U. Zihan, Inya Nlenanya, Abdallah B. Al-Hamdan and Omar Smadi
Infrastructures 2024, 9(10), 179; https://doi.org/10.3390/infrastructures9100179 - 8 Oct 2024
Cited by 1 | Viewed by 778
Abstract
Trail pavement roughness significantly impacts user experience and safety. Measuring roughness over large areas using traditional equipment is challenging and expensive. The utilization of smartphones and bicycles offers a more feasible approach to measuring trail roughness, but the current methods to capture data [...] Read more.
Trail pavement roughness significantly impacts user experience and safety. Measuring roughness over large areas using traditional equipment is challenging and expensive. The utilization of smartphones and bicycles offers a more feasible approach to measuring trail roughness, but the current methods to capture data using these have accuracy limitations. While machine learning has the potential to improve accuracy, there have been few applications of real-time roughness evaluation. This study proposes a hybrid ensemble machine learning model that combines sequence-based modeling with support vector regression (SVR) to estimate trail roughness using smartphone sensor data mounted on bicycles. The hybrid model outperformed traditional methods like double integration and whole-body vibration in roughness estimation. For the 0.031 mi (50 m) segments, it reduced RMSE by 54–74% for asphalt concrete (AC) trails and 50–59% for Portland cement concrete (PCC) trails. For the 0.31 mi (499 m) segments, RMSE reductions of 37–60% and 49–56% for AC and PCC trails were achieved, respectively. Additionally, the hybrid model outperformed the base random forest model by 17%, highlighting the effectiveness of combining ensemble learning with sequence modeling and SVR. These results demonstrate that the hybrid model provides a cost-effective, scalable, and highly accurate alternative for large-scale trail roughness monitoring and assessment. Full article
(This article belongs to the Special Issue Pavement Design and Pavement Management)
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16 pages, 3592 KiB  
Article
Deep Learning for Pavement Condition Evaluation Using Satellite Imagery
by Prathyush Kumar Reddy Lebaku, Lu Gao, Pan Lu and Jingran Sun
Infrastructures 2024, 9(9), 155; https://doi.org/10.3390/infrastructures9090155 - 9 Sep 2024
Viewed by 1063
Abstract
Civil infrastructure systems cover large land areas and need frequent inspections to maintain their public service capabilities. Conventional approaches of manual surveys or vehicle-based automated surveys to assess infrastructure conditions are often labor-intensive and time-consuming. For this reason, it is worthwhile to explore [...] Read more.
Civil infrastructure systems cover large land areas and need frequent inspections to maintain their public service capabilities. Conventional approaches of manual surveys or vehicle-based automated surveys to assess infrastructure conditions are often labor-intensive and time-consuming. For this reason, it is worthwhile to explore more cost-effective methods for monitoring and maintaining these infrastructures. Fortunately, recent advancements in satellite systems and image processing algorithms have opened up new possibilities. Numerous satellite systems have been employed to monitor infrastructure conditions and identify damages. Due to the improvement in the ground sample distance (GSD), the level of detail that can be captured has significantly increased. Taking advantage of these technological advancements, this research evaluated pavement conditions using deep learning models for analyzing satellite images. We gathered over 3000 satellite images of pavement sections, together with pavement evaluation ratings from the TxDOT’s PMIS database. The results of our study show an accuracy rate exceeding 90%. This research paves the way for a rapid and cost-effective approach for evaluating the pavement network in the future. Full article
(This article belongs to the Special Issue Pavement Design and Pavement Management)
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Review

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32 pages, 2648 KiB  
Review
Machine Learning Applications in Road Pavement Management: A Review, Challenges and Future Directions
by Tiago Tamagusko, Matheus Gomes Correia and Adelino Ferreira
Infrastructures 2024, 9(12), 213; https://doi.org/10.3390/infrastructures9120213 - 21 Nov 2024
Viewed by 814
Abstract
Effective road pavement management is vital for maintaining the functionality and safety of transportation infrastructure. This review examines the integration of Machine Learning (ML) into Pavement Management Systems (PMS), presenting an analysis of state-of-the-art ML techniques, algorithms, and challenges for application in the [...] Read more.
Effective road pavement management is vital for maintaining the functionality and safety of transportation infrastructure. This review examines the integration of Machine Learning (ML) into Pavement Management Systems (PMS), presenting an analysis of state-of-the-art ML techniques, algorithms, and challenges for application in the field. We discuss the limitations of conventional PMS and explore how Artificial Intelligence (AI) algorithms can overcome these shortcomings by improving the accuracy of pavement condition assessments, enhancing performance prediction, and optimizing maintenance and rehabilitation decisions. Our findings indicate that ML significantly advances PMS capabilities by refining data collection processes and improving decision-making, thereby addressing the intricacies of pavement deterioration. Additionally, we identify technical challenges such as ensuring data quality and enhancing model interpretability. This review also proposes directions for future research to overcome these hurdles and to help stakeholders develop more efficient and resilient road networks. The integration of ML not only promises substantial improvements in managing pavements but is also in line with the increasing demands for smarter infrastructure solutions. Full article
(This article belongs to the Special Issue Pavement Design and Pavement Management)
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