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Machine Learning, Health Monitoring, and Numerical Simulation of Civil Engineering...
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Machine Learning, Health Monitoring, and Numerical Simulation of Civil Engineering Materials

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

Deadline for manuscript submissions: 20 December 2025 | Viewed by 811

Special Issue Editors

Dr. Shi Dong

E-Mail Website
Guest Editor
College of Transportation Engineering, Chang’an University, Xi’an 710064, China
Interests: intelligent perception and information fusion of road traffic; long-term performance prediction and maintenance decision-making for pavement; mechanisms of vehicle–road coupling interaction; digitalization of transportation infrastructure for the operation and maintenance stage
Special Issues, Collections and Topics in MDPI journals
Dr. Chao Xing

E-Mail Website
Guest Editor
School of Transportation Science and Engineering, Harbin Institute of Technology, Harbin 150000, China
Interests: multi-scale characterization and digital design of asphalt pavement materials; intelligent sensing technologies for asphalt pavements
Special Issues, Collections and Topics in MDPI journals
Dr. Zheng Tong

E-Mail Website
Guest Editor
School of Transportation, Southeast University, Nanjing 211189, China
Interests: prompt-guided large language models; evidential deep neural networks; decision-making under uncertainty; AI-enabled ground-penetrating radar detection
Special Issues, Collections and Topics in MDPI journals
Dr. Ziming Liu

E-Mail Website
Guest Editor
School of Highway, Chang’an University, Xi’an 710064, China
Interests: intelligent construction and maintenance of transportation infrastructure; integrated design of asphalt pavement structure and materials; long-term performance prediction of pavement
Dr. You Huang

E-Mail Website
Guest Editor Assistant
College of Transportation Engineering, Changsha University of Science and Technology, Changsha 410114, China
Interests: long-term performance of pavement structure and materials; transportation infrastructure resilience and risk prediction; automated truck platooning strategy; carbon footprint and decarbonization of road transportation
Dr. Ziye Ma

E-Mail Website
Guest Editor Assistant
Key Laboratory of Intelligent Construction and Maintenance of Civil Aviation Airports, Chang’an University, Xi’an 710064, China
Interests: advanced monitoring, detection, and maintenance technologies for highway and airport pavements, including modeling and data integrity restoration, sensor signal analysis, and surface texture and functionality evaluation

Special Issue Information

Dear Colleagues,

This Special Issue is centered on the interdisciplinary domain of sustainable operation and maintenance for intelligent transportation systems. Its overarching goal is to construct a comprehensive technological framework spanning from perception to decision-making, construction, and operation. In the realm of intelligent perception, this study leverages the integration of multiple traffic information sources and delves into the analysis of vehicle–road interaction mechanisms. By doing so, it aims to elucidate the dynamic load characteristics of vehicles and the intricate interactions between roads and vehicles. Such efforts lay a solid theoretical groundwork for evaluating the status of infrastructure. Regarding digital technology applications, a Building Information Modeling (BIM)-based digital operation and maintenance platform for intelligent transportation infrastructure has been developed. This platform, in combination with long-term performance prediction models and material analysis, enables the visual management of data throughout the entire lifecycle of transportation facilities. The proposed integrated design approach for the maintenance system represents an optimal solution for minimizing life cycle costs. In the area of intelligent construction technology, this research emphasizes the optimization of asphalt pavement material mixtures and the precise control of construction process parameters. Through the implementation of digital twin technology, real-time monitoring and adjustment of construction quality can be achieved. Moreover, this study introduces an innovative deep learning method to analyze damages under various conditions and environmental factors. This approach provides intelligent decision support for preventive maintenance strategies. By integrating cutting-edge information technologies, including intelligent perception, digital twins, and artificial intelligence, with traditional civil engineering practices, this research system propels the development of intelligent infrastructure towards the realm of “self-adaptive perception, self-decision-making, and self-optimization”.

Dr. Shi Dong
Dr. Chao Xing
Dr. Zheng Tong
Dr. Ziming Liu
Guest Editors

Dr. You Huang
Dr. Ziye Ma
Guest Editor Assistants

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. Materials 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 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

  • digitalization of transportation infrastructure for the operation and maintenance stage
  • long-term performance prediction of pavement
  • intelligent construction and maintenance of transportation infrastructure
  • mechanism of vehicle–road coupling interaction
  • intelligent operation and maintenance
  • intelligent design of road engineering

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Published Papers (2 papers)

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Research

24 pages, 5824 KiB  
Article
Evaluation of Highway Pavement Structural Conditions Based on Measured Crack Morphology by 3D GPR and Finite Element Modeling
by Zhonglu Cao, Dianguang Cao, Haolei Chang, Yaoguo Fu, Xiyuan Shen, Weiping Huang, Huiping Wang, Wanlu Bao, Chao Feng, Zheng Tong, Xiaopeng Lin and Weiguang Zhang
Materials 2025, 18(14), 3336; https://doi.org/10.3390/ma18143336 - 16 Jul 2025
Viewed by 263
Abstract
Structural cracks are internal distresses that cannot be observed from pavement surfaces. However, the existing evaluation methods for asphalt pavement structures lack the consideration of these cracks, which are crucial for accurate pavement assessment and effective maintenance planning. This study develops a novel [...] Read more.
Structural cracks are internal distresses that cannot be observed from pavement surfaces. However, the existing evaluation methods for asphalt pavement structures lack the consideration of these cracks, which are crucial for accurate pavement assessment and effective maintenance planning. This study develops a novel framework combining a three-dimensional (3D) ground penetrating radar (GPR) and finite element modeling (FEM) to evaluate the severity of structural cracks. First, the size and depth development of structural cracks on a four-layer asphalt pavement were determined using the 3D GPR. Then, the range of influence of the structural crack on structural bearing capacity was analyzed based on 3D FEM simulation model. Structural cracks have a distance-dependent diminishing influence on the deflection in the horizontal direction, with the most pronounced effects within a 20-cm width zone surrounding the cracks. Finally, two indices have been proposed: the pavement structural crack index (PSCI) to assess the depth of crack damage and the structural crack reflection ratio (SCRR) to evaluate surface reflection. Besides, PSCI and SCRR are used to classify the severities of structural cracks: none, low, and high. The threshold between none/low damage is a structural crack damage rate of 0.19%, and the threshold between low/high damage is 0.663%. An experiment on a 132-km expressway indicated that the proposed method achieved 94.4% accuracy via coring. The results also demonstrate the strong correlation between PSCI and pavement deflection (R2 = 0.92), supporting performance-based maintenance strategies. The results also demonstrate the correlation between structural and surface cracks, with 65.8% of the cracked sections having both structural and surface cracks. Full article
(This article belongs to the Special Issue Machine Learning, Health Monitoring, and Numerical Simulation of Civil Engineering Materials)
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29 pages, 6644 KiB  
Article
A New Design Methodology of Asphalt Mixture Dynamic Modulus Based on Pavement Response
by You Huang, Boxiong Feng, Xin Yang, Minxiang Cheng and Zhaohui Liu
Materials 2025, 18(13), 3184; https://doi.org/10.3390/ma18133184 - 5 Jul 2025
Viewed by 275
Abstract
The design of asphalt mixture has, for a long time, been an empirical and proof process, causing the mismatch between material design and pavement structure design. To enhance the rationality of asphalt pavement design, this study seeks a path to bridge the gap [...] Read more.
The design of asphalt mixture has, for a long time, been an empirical and proof process, causing the mismatch between material design and pavement structure design. To enhance the rationality of asphalt pavement design, this study seeks a path to bridge the gap between asphalt mixture modulus and structural behavior. Firstly, pavement models with different base rigidities, including cement concrete base, cement-treated granular base, and granular base, were constructed to calculate the pavement responses under different dynamic modulus master curve parameters. The influence of master curve parameters on critical pavement responses was identified by the response surface method (RSM). Furthermore, a Whale Optimization Algorithm–Back Propagation (WOA-BP) artificial-neural-network-based pavement response prediction model was established. Then, a database mapping over 100 thousand pavement responses and dynamic modulus master curve parameters was built for determining the dynamic modulus master curve parameters by optimizing the pavement responses. The results show that the impacts of dynamic modulus master curve parameters on critical pavement responses depend on pavement structures. In general, parameter δ has the greatest impact, followed by α, while the effects of β and γ are relatively small. The Artificial Neural Network (ANN) performance prediction model, optimized by the WOA algorithm, has a high accuracy. The methodology for determining the dynamic modulus master curve parameter based on the critical response of pavement was successfully implemented. The findings can bridge the gap between material design and structure design of asphalt pavement and provide a basis for more accurate and reasonable asphalt pavement design. Full article
(This article belongs to the Special Issue Machine Learning, Health Monitoring, and Numerical Simulation of Civil Engineering Materials)
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