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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: 30 September 2025 | Viewed by 9466

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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Further information on MDPI's Special Issue policies can be found here.

Published Papers (5 papers)

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Research

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24 pages, 11970 KiB  
Article
Structural Stability of Cycle Paths—Introducing Cycle Path Deflection Bowl Parameters from FWD Measurements
by Martin Larsson, Anna Niska and Sigurdur Erlingsson
Infrastructures 2025, 10(1), 7; https://doi.org/10.3390/infrastructures10010007 - 31 Dec 2024
Viewed by 748
Abstract
A recurrent challenge on cycle paths are edge cracks, which affect the traffic safety and accessibility of cyclists and produce high maintenance costs. Being both structurally thinner and narrower structures than roads, the cycle paths are extra prone to this problem. A few [...] Read more.
A recurrent challenge on cycle paths are edge cracks, which affect the traffic safety and accessibility of cyclists and produce high maintenance costs. Being both structurally thinner and narrower structures than roads, the cycle paths are extra prone to this problem. A few passages of heavy vehicles in unfavourable conditions might be enough to break the edge. The load-bearing capacity of eight municipal cycle paths in Linköping, Sweden, were assessed by falling weight deflectometer (FWD) and light falling weight deflectometer (LWD) measurements during a year-long cycle. A set of alternative Deflection Bowl Parameters (DBPs), better adapted to the structural design of cycle paths, were suggested and evaluated. The results of the FWD measurements showed that these suggested DBPs are a promising approach to evaluate the load-bearing capacity of cycle paths. From the results of the LWD measurements, it was found that the load-bearing capacity varies considerably with lateral position. The conclusion is that it might be more fruitful to measure the load-bearing capacity by LWD close to the edge, rather than the traditional approach of FWD measurements along the centre line of the cycle path. Full article
(This article belongs to the Special Issue Pavement Design and Pavement Management)
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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 1173
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
Cited by 1 | Viewed by 1680
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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25 pages, 1049 KiB  
Review
Comprehensive Analysis of Sustainability Rating Systems for Road Infrastructure
by Rajab Ali Mehraban, Lucia Tsantilis, Pier Paolo Riviera and Ezio Santagata
Infrastructures 2025, 10(1), 17; https://doi.org/10.3390/infrastructures10010017 - 11 Jan 2025
Viewed by 1038
Abstract
Sustainability rating systems (SRSs) have emerged as indispensable frameworks for advancing the environmental, social, and economic sustainability of road infrastructure. Despite their growing adoption, their integration as authoritative tools within infrastructure planning and development remains limited. This study provides a comprehensive evaluation of [...] Read more.
Sustainability rating systems (SRSs) have emerged as indispensable frameworks for advancing the environmental, social, and economic sustainability of road infrastructure. Despite their growing adoption, their integration as authoritative tools within infrastructure planning and development remains limited. This study provides a comprehensive evaluation of eight leading SRSs—CEEQUAL, Greenroads, GreenLITES, GreenPave, I-LAST, INVEST, BE2ST-in-Highways, and Envision—focusing on their structural frameworks, criteria weightings, adherence to the three pillars of sustainability, and alignment with international benchmarks such as ISO, EN, and ASTM standards. By considering the three pillars of sustainability, the analysis of the eight SRSs reveals a disproportionate focus on environmental well-being (43%) and social well-being (42%), with economic well-being receiving minimal emphasis (15%). Furthermore, this study identifies notable deficiencies in the integration of critical international standards, including ISO, EN, and ASTM, which constrains the comprehensiveness and global applicability of these frameworks. Key findings suggest that the current SRSs inadequately address the principles of a circular economy, risk management, and social equity, highlighting areas for methodological enhancement. This review offers critical insights for researchers, policy makers, and practitioners seeking to refine sustainability rating systems for road infrastructure. By consolidating existing knowledge and proposing methodological advancements, this study contributes to the evolution of SRSs into comprehensive, globally relevant tools for sustainable infrastructure development. Full article
(This article belongs to the Special Issue Pavement Design and Pavement Management)
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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
Cited by 1 | Viewed by 2888
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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