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AI-Based Material Design, Performance Evaluation and Construction Quality Control of Asphalt Pavement

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

Deadline for manuscript submissions: 20 March 2026 | Viewed by 1564

Special Issue Editors

Dr. Xunhao Ding

E-Mail Website
Guest Editor
School of Transportation, Southeast University, 2 Sipailou, Nanjing 210096, China
Interests: multi-physical/multi-scale characterization of pavement materials; genome encoding and AI-driven design of materials; intelligent construction and monitoring of structure
Special Issues, Collections and Topics in MDPI journals
Dr. Yanshun Jia

E-Mail Website
Guest Editor
School of Traffic and Transportation, Shijiazhuang Tiedao University, Shijiazhuang 050043, China
Interests: multiscale mechanical design of asphalt/cement-based material; intelligent monitoring; resource utilization design of solid waste materials; reliability assessment of material design
Special Issues, Collections and Topics in MDPI journals
Dr. Wensheng Wang

E-Mail Website
Guest Editor
College of Transportation, Jilin University, Changchun 130022, China
Interests: road materials and pavement design; nondestructive testing technology; structural health monitoring; bridge and infrastructure engineering
Special Issues, Collections and Topics in MDPI journals
Dr. Xiong Xiao

E-Mail Website
Guest Editor Assistant
School of Civil Engineering, Central South University Railway Campus, Changsha 410075, China
Interests: structural durability; bayesian updating; structural reliability; value of information; decision making
Dr. Bo Li

E-Mail Website
Guest Editor
College of Civil Science and Engineering, Yangzhou University, Yangzhou 225127, China
Interests: asphalt pavement recycling; sustainable pavement materials; mechnical performance evaluation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Amid the dual challenges of accelerated aging of asphalt pavement and the deepening implementation of the "dual-carbon" strategy (carbon peak and carbon neutrality), the development of pavement materials that combine ‌green and low-carbon attributes, environmental adaptability, and long-term durability‌ has became a critical solution to mitigating escalating challenges posed by heavy traffic loads and extreme weather conditions in complex service environments. Current research on ‌asphalt pavement materials‌ mainly relies on scientists' continuous exploration of complex theories and the gradual accumulation of experimental data, a process often characterized by ‌long cycles and low efficiency‌, severely hindering the rapid development and practical application of ‌performance-oriented new-material design‌. With the rapid advancement of ‌artificial intelligence (AI)‌, guided by the ‌Materials Genome Initiative (MGI)‌ framework and empowered by ‌high-throughput computing and AI-driven approaches‌, it is now possible to overcome the ‌spatiotemporal limitations of traditional trial-and-error material design methods in pavement‌. This enables ‌precise analysis and inverse design of composition-structure-performance relationships in materials‌. Such an ‌AI-aided paradigm‌, which ‌integrates data-driven approaches with fundamental physical mechanisms‌, dramatically improves both ‌the efficiency of performance-targeted pavement material design‌ and its ‌inherent adaptive capabilities‌.

This Special Issue, entitled “AI-Based Material Design, Performance Evaluation and Construction Quality Control of Asphalt Pavement”, aims to gather original research papers related to the performance prediction and intelligent design of bituminous materials. The scope of this Special Issue includes, but is not limited to, the following topics:

  • High-throughput computing and evaluation of asphalt/cement-based material;
  • Genome encoding and AI-driven design of asphalt/cement-based material;
  • Multi-physical/multi-scale characterization of asphalt/cement-based material;
  • Mechanical inversion and reverse design of asphalt/cement-based material;
  • ‌Intelligent construction and quality assessment of pavement structure;
  • Intelligent monitoring and risk assessment technology of pavement structure;
  • Green and sustainable materials design and durability assessment of pavements.

You may choose our Joint Special Issue in Applied Sciences.

Dr. Xunhao Ding
Dr. Yanshun Jia
Dr. Wensheng Wang
Guest Editors

Dr. Xiong Xiao
Guest Editor Assistant

Dr. Bo Li
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. 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

  • asphalt/cement-based material
  • high-throughput computing
  • genome interpretation of materials
  • AI-driven inverse design of materials
  • pavement performance evaluation
  • green and sustainable materials
  • intelligent construction and monitoring

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

Published Papers (3 papers)

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Research

21 pages, 3058 KB  
Article
Dynamic Identification Method for Highway Subgrade Soil Compaction Based on Embedded Attitude Sensors
by Zhizhou Su, Hao Li, Jiaye Hu, Bin Wu, Fengteng Liu, Peixin Tian and Xukai Ding
Materials 2025, 18(20), 4801; https://doi.org/10.3390/ma18204801 - 21 Oct 2025
Viewed by 166
Abstract
Compaction quality is a critical factor in ensuring the long-term performance of subgrade structures; however, traditional testing methods are limited by their destructive nature and delayed feedback. To address these shortcomings, this study proposes a dynamic identification method for subgrade compaction based on [...] Read more.
Compaction quality is a critical factor in ensuring the long-term performance of subgrade structures; however, traditional testing methods are limited by their destructive nature and delayed feedback. To address these shortcomings, this study proposes a dynamic identification method for subgrade compaction based on embedded attitude sensors. A customized sensor unit integrated with an inertial measurement module was embedded in soil samples to record triaxial acceleration and attitude angles during the compaction process. Signal processing techniques, including an improved wavelet-based denoising strategy, were employed to separate long-term compaction trends from transient impact disturbances. Attitude features such as cumulative angular change, angular velocity, root mean square values, and a comprehensive inclination index were extracted as predictive variables. Ridge regression, random forest, and XGBoost models were constructed to establish the mapping relationship between attitude features and compaction degree. Experimental results on clay, loam, and sand samples indicate that the yaw angle is most sensitive to vertical settlement, while pitch and roll angles provide complementary information on lateral and rotational behaviors. Comparative analysis of filtering methods shows that the transient masking interpolation (TMI) approach outperforms the traditional asymmetric wavelet thresholding (AWT) method in effectively preserving baseline trends. Among the regression models, XGBoost demonstrated the best predictive performance, achieving an R2 exceeding 0.995 at high compaction levels. The proposed method has been experimentally demonstrated as a laboratory-scale proof of concept, showing strong potential for future real-time field application, offering a novel technological pathway for intelligent quality control in road construction. Full article
(This article belongs to the Special Issue AI-Based Material Design, Performance Evaluation and Construction Quality Control of Asphalt Pavement)
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22 pages, 7612 KB  
Article
A Method for Identifying Hydration Stages of Concrete Based on Embedded Piezo-Ultrasonic Active Sensing Technology
by Min Xiao, Yaoting Zhu, Wei Min, Feilong Ye, Yongwei Li, Xunhao Ding and Tao Ma
Materials 2025, 18(20), 4722; https://doi.org/10.3390/ma18204722 - 15 Oct 2025
Viewed by 285
Abstract
The structural evolution of concrete during different hydration stages critically influences subsequent strength, and continuous monitoring throughout this process has become a research focus in materials science. This study proposes an embedded ultrasonic active sensing technique based on piezoelectric ceramics (PZT) to identify [...] Read more.
The structural evolution of concrete during different hydration stages critically influences subsequent strength, and continuous monitoring throughout this process has become a research focus in materials science. This study proposes an embedded ultrasonic active sensing technique based on piezoelectric ceramics (PZT) to identify key structural transition stages during concrete curing. To this end, a piezoelectric ultrasonic sensor was fabricated and its comprehensive performance was systematically evaluated. Subsequently, compressive strength and penetration resistance tests were conducted, and the evolution of piezoelectric signal amplitude and wavelet packet energy (WPE) during hydration was analyzed. Furthermore, a root mean square deviation index based on WPE (WPE-RMSD) was introduced to identify structural transitions throughout the hydration process. The results demonstrate that the developed sensor exhibits stable electrical, mechanical, and waterproof performance. Both signal amplitude and WPE effectively captured the hydration process of concrete, with WPE showing higher sensitivity. The WPE-RMSD index exhibited good temporal continuity, covering the entire process from early hydration disturbance to late-stage structural densification (28 d), and proved particularly effective in identifying critical stages such as final setting and the medium-age period (7 d). This study provides a novel in situ monitoring approach for the classification and identification of hydration stages in concrete. Full article
(This article belongs to the Special Issue AI-Based Material Design, Performance Evaluation and Construction Quality Control of Asphalt Pavement)
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19 pages, 3221 KB  
Article
GPR Feature Enhancement of Asphalt Pavement Hidden Defects Using Computational-Efficient Image Processing Techniques
by Shengjia Xie, Jingsong Chen, Ming Cai, Zhiqiang Cheng, Siqi Wang and Yixiang Zhang
Materials 2025, 18(18), 4400; https://doi.org/10.3390/ma18184400 - 20 Sep 2025
Viewed by 391
Abstract
Hyperbolic reflection features from ground-penetrating radar (GPR) data have been recognized as essential indicators for detecting hidden defects in the asphalt pavement. Computer vision and deep learning algorithms have been developed to detect and enhance the hyperbolic features of hidden defects. However, migrating [...] Read more.
Hyperbolic reflection features from ground-penetrating radar (GPR) data have been recognized as essential indicators for detecting hidden defects in the asphalt pavement. Computer vision and deep learning algorithms have been developed to detect and enhance the hyperbolic features of hidden defects. However, migrating existing hyperbolic feature detection methods using raw GPR data results in inaccurate predictions. Pre-processing raw GPR data using straightforward image processing methods could enhance the reflection features for fast and accurate hyperbolic detection during real-time GPR measurements. This study proposed accessible and straightforward image processing methods as GPR data preprocessing steps (such as the Sobel edge detector and histogram equalization) to assist existing computer vision algorithms for reflection feature enhancement during the GPR survey. Field tests were conducted, and several image processing methods with existing standard image processing libraries were applied. The proposed regions of the identified hyperbola signal-to-noise ratio (RIHSNR) were used to quantify the enhancement performance of hyperbolic feature detectability. Applying Sobel edge detection and Otsu’s thresholding to GPR data significantly improves detection accuracy and speed: mAP@0.5 rises from 0.65 to 0.85 for Faster R-CNN and from 0.72 to 0.88 for CBAM-YOLOv8 using the proposed computer vision methods as preprocessing steps. At the same time, inference time drops to 30 ms and 25 ms, respectively. Full article
(This article belongs to the Special Issue AI-Based Material Design, Performance Evaluation and Construction Quality Control of Asphalt Pavement)
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