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Materials Informatics and Machine Learning in Pavement Engineering

A special issue of Materials (ISSN 1996-1944). This special issue belongs to the section "Construction and Building Materials".

Deadline for manuscript submissions: 20 October 2025 | Viewed by 727

Special Issue Editors

Lyles School of Civil Engineering, Purdue University, West Lafayette, IN, USA
Interests: material informatics; data mining; pavement sustainability
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Guest Editor
National Key Laboratory of Green and Long-Life Road Engineering in Extreme Environment, Shenzhen University, Shenzhen 518060, China
Interests: pavement materials; asphalt emissions; life cycle assessment; recycling wastes in roads

Special Issue Information

Dear Colleagues,

We are pleased to announce this Special Issue, entitled "Material Informatics and Machine Learning in Pavement Engineering". This Special Issue aims to highlight innovative research at the intersection of material science, informatics, machine learning, and infrastructure engineering. We invite original research articles, reviews, and case studies that explore the use of material informatics and machine learning for the design, analysis, and maintenance of pavement systems. Topics of interest for this Special Issue include, but are not limited to, the predictive modeling of material properties, optimization of pavement materials, data-driven approaches for infrastructure health monitoring, and the integration of machine learning algorithms in material selection and performance prediction. This Special Issue seeks to provide a platform for interdisciplinary collaboration and to advance the application of cutting-edge informatics and analytic tools in enhancing the durability, sustainability, and safety of road pavement infrastructures.

Dr. Jin Li
Dr. Gengren Hao
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.

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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

  • material informatics
  • data analytics
  • machine learning
  • pavement engineering
  • construction materials
  • material design
  • performance prediction
  • inverse design

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Published Papers (2 papers)

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Research

19 pages, 5562 KiB  
Article
Research on the Milling Characteristics of SBS Modified Asphalt Pavement with Different Service Years Using the Discrete Element Method
by Xiujun Li, Zhipeng Zhang, Hao Liu, Hao Feng, Heng Zhang, Fangzhi Shi and Zhi Gou
Materials 2025, 18(14), 3226; https://doi.org/10.3390/ma18143226 - 8 Jul 2025
Abstract
The service years of the milled pavement are varied in numerous SBS modified asphalt pavement milling assignments. To investigate the milling characteristics of SBS (styrene–butadiene–styrene) modified asphalt pavements with different service years, the values of the bonding parameters were calibrated and verified and [...] Read more.
The service years of the milled pavement are varied in numerous SBS modified asphalt pavement milling assignments. To investigate the milling characteristics of SBS (styrene–butadiene–styrene) modified asphalt pavements with different service years, the values of the bonding parameters were calibrated and verified and then used to build three simulation models for the milling of old asphalt pavements with service years of 2~3 years, 7~8 years, and 11~12 years, respectively. The milling characteristics of SBS modified asphalt pavements with different service years were investigated using the moving speed v and rotating speed ω of the milling rotor as test factors, and the particle bonding ratio (Rb) and rotor average force (Fa) as test indexes. The results demonstrate that the following: The regularity of the effects of milling rotor moving speed and rotating speed on the particle bonding ratio and milling rotor average forces remained consistent overall as the pavement age increased. For the same milling parameters, the particle bonding ratio and the rotor average force are reduced. From 2~3 years old pavements to 7~8 years old pavements, the overall reduction in the particle bonding ratio indicator is about 12%, and the average force on the milling rotor is about 24%. From 7~8 years old pavements to 11~12 years old pavements, the overall reduction in the particle bonding ratio indicator is about 3%, and the average force on the milling rotor is about 15%. Full article
(This article belongs to the Special Issue Materials Informatics and Machine Learning in Pavement Engineering)
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16 pages, 1992 KiB  
Article
Fuzzy-Modulus-Based Layered Elastic Analysis of Asphalt Pavements for Enhanced Fatigue Life Prediction
by Artur Zbiciak, Denys Volchok, Zofia Kozyra, Rafał Michalczyk and Nassir Al Garssi
Materials 2025, 18(13), 3034; https://doi.org/10.3390/ma18133034 - 26 Jun 2025
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Abstract
The paper presents a novel approach to evaluating the fatigue performance of asphalt pavements using fuzzy set theory to model the uncertainty in the elastic moduli of asphalt layers. The method integrates fuzzy numbers with an analytical multilayer elastic pavement model. By applying [...] Read more.
The paper presents a novel approach to evaluating the fatigue performance of asphalt pavements using fuzzy set theory to model the uncertainty in the elastic moduli of asphalt layers. The method integrates fuzzy numbers with an analytical multilayer elastic pavement model. By applying α-cut representation and defuzzification techniques, the model delivers fuzzy estimations of critical strain responses and associated fatigue lives under traffic loading. The proposed methodology captures uncertainty in material properties more realistically than conventional deterministic approaches. The effectiveness of this technique is demonstrated through the Asphalt Institute’s fatigue models for tensile and compressive strains. The results provide fuzzy bounds for fatigue life parameters and enable robust pavement design under material uncertainty. By incorporating fuzzy-modulus-based parameters into layered elastic half-space models, the proposed method significantly improves the predictive reliability of pavement performance. Full article
(This article belongs to the Special Issue Materials Informatics and Machine Learning in Pavement Engineering)
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