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Research and Application in Sustainable Asphalt Pavements and Road Construction

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Dear Colleagues,

Asphalt pavements are widely utilized in roads all around the world, as this type of infrastructure is a key means of communication for the development of society. However, the large quantity of natural resources associated with its construction, conservation and rehabilitation demands for the research of more sustainable materials and technologies to be applied in this field. In this sense, most institutions and companies are seeking innovative solutions to reduce the consumption of energy and natural resources, while using alternative materials or reusing wastes or by-products to minimize economic, environmental and social impacts. In this context, this Special Issue aims to gather papers that address the research and application of innovative materials and technologies for sustainable asphalt pavements and road construction. This Special Issue will provide a collection of noteworthy investigations and case studies related to sustainable solutions for asphalt pavements and road construction.

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

Asphalt mixtures at low temperatures;
Reclaimed asphalt;
Recycled polymer-modified binders;
Recycled materials for road construction;
Life cycle assessment;
Circular economy;
Testing and monitoring road construction.
I look forward to receiving your contributions.

Dr. Miguel Sol-Sánchez
Prof. Dr. Fernando Moreno-Navarro
Guest Editors

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30 pages, 6958 KiB

Open AccessArticle

Predicting the Influence of Pulverized Oil Palm Clinker as a Sustainable Modifier on Bituminous Concrete Fatigue Life: Advancing Sustainable Development Goals through Statistical and Predictive Analysis

by Nura Shehu Aliyu Yaro, Muslich Hartadi Sutanto, Noor Zainab Habib, Aliyu Usman, Liza Evianti Tanjung, Muhammad Sani Bello, Azmatullah Noor, Abdullahi Haruna Birniwa and Ahmad Hussaini Jagaba

Sustainability 2024, 16(16), 7078; https://doi.org/10.3390/su16167078 - 18 Aug 2024

Viewed by 721

Abstract

Currently, the viscoelastic properties of conventional asphalt cement need to be improved to meet the increasing demands caused by larger traffic loads, increased stress, and changing environmental conditions. Thus, using modifiers is suggested. Furthermore, the Sustainable Development Goals (SDGs) promote using waste materials and new technologies in asphalt pavement technology. The present study aims to fill this gap by investigating the use of pulverized oil palm industry clinker (POPIC) as an asphalt–cement modifier to improve the fatigue life of bituminous concrete using an innovative prediction approach. Thus, this study proposes an approach that integrates statistically based machine learning approaches and investigates the effects of applied stress and temperature on the fatigue life of POPIC-modified bituminous concrete. POPIC-modified bituminous concrete (POPIC-MBC) is produced from a standard Marshall mix. The interactions between POPIC concentration, stress, and temperature were optimized using response surface methodology (RSM), resulting in 7.5% POPIC, 11.7 °C, and 0.2 MPa as the optimum parameters for fatigue life. To improve the prediction accuracy and robustness of the results, RSM and ANN models were used and analyzed using MATLAB and JMP Pro, respectively. The performance of the developed model was assessed using the coefficient of determination (R2), root mean square error (RMSE), and mean relative error (MRE). The study found that using RSM, MATLAB, and JMP Pro resulted in a comprehensive analysis. MATLAB achieved an R² value of 0.9844, RMSE of 3.094, and MRE of 312.427, and JMP Pro achieved an R² value of 0.998, RMSE of 1.245, and MRE of 126.243, demonstrating higher prediction accuracy and superior performance than RSM, which had an R² value of 0.979, RMSE of 3.757, and MRE of 357.846. Further validation with parity, Taylor, and violin plots demonstrates that both models have good prediction accuracy, with the JMP Pro ANN model outperforming in terms of accuracy and alignment. This demonstrates the machine learning approach’s efficiency in analyzing the fatigue life of POPIC-MBC, revealing it to be a useful tool for future research and practical applications. Furthermore, the study reveals that the innovative approach adopted and POPIC modifier, obtained from biomass waste, meets zero-waste and circular bioeconomy goals, contributing to the UN’s SDGs 9, 11, 12, and 13. Full article

(This article belongs to the Special Issue Research and Application in Sustainable Asphalt Pavements and Road Construction)

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