Sustainability

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Prof. Dr. Lev Khazanovich

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Guest Editor

Department of Civil & Environmental Engineering, University of Pittsburgh, Pittsburgh, PA 15261, USA
Interests: pavement design, analysis, and evaluation; including performance prediction modeling and non-destructive testing

Dr. Sushobhan Sen

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Guest Editor

Indian Institute of Technology Gandhinagar, Palaj, Gandhinagar 382355, Gujarat, India
Interests: climate change adaptation of urban infrastructure, with a specific focus on pavements

Special Issue Information

Dear Colleagues,

Road networks form the backbone of economic progress but also come at a tremendous cost. Once a road is built, it must undergo periodic maintenance and improvements to maintain service quality. Over time, these activities can have a significant economic and ecological footprint.

This Special Issue aims to collect the latest and most impactful research in developing sustainable methods for the maintenance and improvement of pavements. Some of the areas within the scope of the Special Issue are as follows:

Use of sustainable or recycled materials;
Sustainable construction practices;
Maintenance, rehabilitation, and preservation of asphalt and concrete pavements;
Life cycle cost assessment;
Life cycle assessment;
Environmental Product Declarations;
Applications of AI and ML for the sustainability of pavements;
Climate-change-related aspects;
Developments in analysis and modeling of pavement maintenance;
Other relevant topics.

We look forward to receiving your contributions.

Prof. Dr. Lev Khazanovich
Dr. Sushobhan Sen
Guest Editors

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. Sustainability is an international peer-reviewed open access semimonthly 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 2400 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

pavements design
pavements construction
pavements maintenance
pavements rehabilitation
pavements preservation

Published Papers (2 papers)

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Research

20 pages, 4226 KiB

Open AccessArticle

Enhancing Pavement Sustainability: Prediction of the Pavement Condition Index in Arid Urban Climates Using the International Roughness Index

by Mostafa M. Radwan, Ahmad Mousa and Elsaid Mamdouh Mahmoud Zahran

Sustainability 2024, 16(8), 3158; https://doi.org/10.3390/su16083158 - 10 Apr 2024

Viewed by 705

Abstract

Municipalities and transportation departments worldwide are striving to keep road pavement conditions acceptable, thus enhancing pavement sustainability. Although the pavement condition index (PCI) is widely used to assess distress conditions, traditional visual surveys used for PCI estimation can be laborious, expensive, and time-consuming. The international roughness index (IRI) can be measured more economically and conveniently than PCI; however, it does not directly indicate the surface condition of the pavement. In this study, a PCI–IRI correlation is proposed for urban roads located in the New Beni-Suef region, Egypt. For this purpose, a total of 44 km of urban roads was divided into homogenous sections. A visual distress survey was conducted to measure PCI considering typical distress patterns. The IRI values for the same sections were measured using an ultrasonic distance sensor mounted on an automobile. An exponential model was proposed to capture the relationship between IRI and PCI. With a coefficient of determination of 0.82, the exponential model seems to outperform reported IRI-PCI correlations. Model validation, along with a comparison to the existing models, supports its applicability to a wide range of roads. The proposed model provides a cost-effective means for accurately predicting PCI based on IRI, which is particularly useful for pavement maintenance management programs on limited budgets. Full article

(This article belongs to the Special Issue Sustainable Road Maintenance and Improvement)

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18 pages, 6867 KiB

Open AccessArticle

Pavement Distress Identification Based on Computer Vision and Controller Area Network (CAN) Sensor Models

by Cuthbert Ruseruka, Judith Mwakalonge, Gurcan Comert, Saidi Siuhi, Frank Ngeni and Kristin Major

Sustainability 2023, 15(8), 6438; https://doi.org/10.3390/su15086438 - 10 Apr 2023

Cited by 4 | Viewed by 2041

Abstract

Recent technological developments have attracted the use of machine learning technologies and sensors in various pavement maintenance and rehabilitation studies. To avoid excessive road damages, which cause high road maintenance costs, reduced mobility, vehicle damages, and safety concerns, the periodic maintenance of roads is necessary. As part of maintenance works, road pavement conditions should be monitored continuously. This monitoring is possible using modern distress detection methods that are simple to use, comparatively cheap, less labor-intensive, faster, safer, and able to provide data on a real-time basis. This paper proposed and developed two models: computer vision and sensor-based. The computer vision model was developed using the You Only Look Once (YOLOv5) algorithm for detecting and classifying pavement distresses into nine classes. The sensor-based model combined eight Controller Area Network (CAN) bus sensors available in most new vehicles to predict pavement distress. This research employed an extreme gradient boosting model (XGBoost) to train the sensor-based model. The results showed that the model achieved 98.42% and 97.99% area under the curve (AUC) metrics for training and validation datasets, respectively. The computer vision model attained an accuracy of 81.28% and an F1-score of 76.40%, which agree with past studies. The results indicated that both computer vision and sensor-based models proved highly efficient in predicting pavement distress and can be used to complement each other. Overall, computer vision and sensor-based tools provide cheap and practical road condition monitoring compared to traditional manual instruments. Full article

(This article belongs to the Special Issue Sustainable Road Maintenance and Improvement)

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