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

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

Deadline for manuscript submissions: 20 August 2026 | Viewed by 6728

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Special Issue Editors

Dr. Xunhao Ding

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School of Transportation, Southeast University, 2 Sipailou, Nanjing 210096, China
Interests: intelligent road perception and novel sensor development; structural performance monitoring and disaster risk early warning; multiscale characterization and modeling analysis of pavement materials
Special Issues, Collections and Topics in MDPI journals

Dr. Yanshun Jia

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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

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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

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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

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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

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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.

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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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Published Papers (8 papers)

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Research

31 pages, 24728 KB

Open AccessArticle

Interpretable Machine Learning for Predicting Splitting Strength of Asphalt Concrete: Insights from SHAP Analysis

by Jianglei Xing, Xiao Tan, Yihao Li, Dongzhao Jin, Pengwei Guo, Yuhuan Wang and Huiya Niu

Materials 2026, 19(8), 1636; https://doi.org/10.3390/ma19081636 - 19 Apr 2026

Viewed by 636

Abstract

This paper proposes an interpretable machine learning approach for predicting the splitting strength of asphalt concrete and supporting data-driven mixture design. A database consisting of 296 samples was constructed, and 14 input variables related to asphalt properties, aggregate gradation, and fiber characteristics were selected for modeling. Eight machine learning models, namely TabPFN, ANN, SVR, RF, XGBoost, LightGBM, FLAML, and FT-Transformer, were developed and compared. The results show that all eight models achieved satisfactory predictive capability, whereas TabPFN overall achieved the best performance in the Monte Carlo cross-validation, with the lowest average RMSE of 0.34 ± 0.10, the lowest average MAE of 0.21 ± 0.02, a relatively low average MAD of 0.14 ± 0.03, the highest average R² of 0.85 ± 0.08, and the highest composite score of 0.81. SHAP analysis further indicated that splitting strength prediction was mainly governed by a limited number of dominant variables, among which Ag9.5, AC, Du, FT, and Ag4.75 were the most effective parameters. In addition, favorable parameter ranges for improving splitting strength were quantified, such as Ag9.5 < 66.8%, AC < 5.4 wt.%, Du > 134.7 cm and Ag4.75 < 45.0%. Finally, a graphic user interface platform integrating prediction and SHapley Additive exPlanations-based interpretation was developed to improve the accessibility and practical applicability of the proposed framework. 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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17 pages, 7137 KB

Open AccessArticle

Periodic Noise Characteristics and Acoustic Control in Long Highway Tunnels: An FEM Study with In Situ Validation

by Ruifeng Ding, Xingyu Gu, Chenlin Liao, Hongchang Wang, Zengbin Xu, Kaiwen Lei and Jiwang Jiang

Materials 2026, 19(8), 1548; https://doi.org/10.3390/ma19081548 - 13 Apr 2026

Viewed by 439

Abstract

Noise in long highway tunnels and underground interchanges poses a significant environmental concern, affecting both drivers and nearby residents. This research develops an acoustic finite element model of a long tunnel in Leuven Measurement Systems (LMS) Virtual Lab to characterize the tunnel noise field, and the effectiveness of different noise mitigation measures was also evaluated and optimized accordingly. The model is validated against in situ monitoring data, with deviations controlled within 3 dB(A) and strong agreement confirmed by the Kappa consistency test. Both simulations and measurements show that sound pressure levels (SPLs) are generally highest near the tunnel center and lower toward the portal, exhibiting periodic fluctuations rather than a monotonic decrease. The dominant noise energy is concentrated between 125 Hz and 500 Hz. SPLs at 1.8 m above the road surface are noticeably higher than at 1.2 m and 1.5 m, indicating greater noise exposure for drivers of large vehicles compared with smaller vehicles. Noise reduction performance is further assessed for different lining materials and pavement types. Installing sound-absorbing panels in the tunnel midsection provides effective attenuation, with expanded perlite panels, single-layer metal micro-perforated panels, and FC quiet perforated panels (FC-PP) performing best, while porous asphalt shows superior noise reduction compared with conventional dense-graded asphalt pavements. 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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17 pages, 3299 KB

Open AccessArticle

Determining Optimal Dosage of High-Modulus Asphalt Binders Through Comprehensive Rheological Assessment Across Full Temperature Range

by Yijun Wang, Bolan Ye, Qisheng Wang, Qifeng Bai and Jiwang Jiang

Materials 2026, 19(6), 1155; https://doi.org/10.3390/ma19061155 - 16 Mar 2026

Viewed by 452

Abstract

High-modulus asphalt binders are increasingly used to improve rutting resistance and enable pavement thickness reduction. Conventional binder indices do not always capture the stress-dependent response of high-modulus systems under heavy loading, and quantitative rules for selecting a high-modulus additive dosage are still limited. This study develops a full-temperature-range evaluation and dosage determination framework for high-modulus additive-modified asphalt binders (HMABs) produced on an SBS-modified base binder. Four binders were prepared with high-modulus additive dosages of 0%, 17%, 22% and 28% with a binder mass basis. High-temperature performance was evaluated by PG grading and an enhanced MSCR protocol that included 0.1, 3.2, 6.4 and 12.8 kPa. MSCR temperatures were selected based on PG results. Intermediate-temperature performance was evaluated using LAS at 25 °C with VECD-based fatigue analysis on RTFO + PAV-aged binders. Low-temperature cracking was evaluated using ABCD on PAV-aged binders at −36 °C. The results show that the high-temperature PG increased with dosage, but the 22% and 28% binders fell into the same grade, indicating limited dosage discrimination by the PG test. The enhanced MSCR test captured clearer dosage differences under higher stresses. Non-recoverable compliance decreased markedly with dosage, and stress sensitivity showed an overall decreasing trend; 6.4 kPa provided higher dosage sensitivity and lower variability than 3.2 kPa. LAS test shows a non-monotonic fatigue response in which peak shear stress and predicted fatigue life increased up to 22% and then declined at 28%. At 2.5% and 5% strain, the 22% binder increased predicted fatigue life by about 273% and 83% relative to the base binder, while at 10% strain, it was about 11% lower. ABCD results show an upward shift in critical cracking temperature and a clear reduction in fracture stress at high dosages, indicating increasing low-temperature fracture risk. Therefore, high-modulus additives markedly improve high-temperature stability but introduce full-temperature trade-offs. The proposed full-temperature-range examined framework improves performance discrimination and supports dosage selection. A target dosage of 22% is recommended, and 17~22% is suggested as an engineering-controllable range for a balanced full-temperature performance, while 28% should be treated as an upper-bound option, primarily for warm regions where rutting dominates. 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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29 pages, 3431 KB

Open AccessArticle

Evolution Mechanism of Volume Parameters and Gradation Optimization Method for Asphalt Mixtures Based on Dual-Domain Fractal Theory

by Bangyan Hu, Zhendong Qian, Fei Zhang and Yu Zhang

Materials 2026, 19(3), 488; https://doi.org/10.3390/ma19030488 - 26 Jan 2026

Viewed by 568

Abstract

The primary objective of this study is to bridge the gap between descriptive geometry and mechanistic design by establishing a dual-domain fractal framework to analyze the internal architecture of asphalt mixtures. This research quantitatively assesses the sensitivity of volumetric indicators—namely air voids (VV), voids in mineral aggregate (VMA), and voids filled with asphalt (VFA)—by employing the coarse aggregate fractal dimension (D_c), the fine aggregate fractal dimension (D_f), and the coarse-to-fine ratio (k) through Grey Relational Analysis (GRA). The findings demonstrate that whereas D_f and k substantially influence macro-volumetric parameters, the mesoscopic void fractal dimension (D_V) remains structurally unchanged, indicating that gradation predominantly dictates void volume rather than geometric intricacy. Sensitivity rankings create a prevailing hierarchy: Process Control (Compaction) > Skeleton Regulation (D_c) > Phase Filling (P_b) > Gradation Adjustment (k, D_f). D_c is recognized as the principal regulator of VMA, while binder content (Pb) governs VFA. A “Robust Design” methodology is suggested, emphasizing D_c to stabilize the mineral framework and reduce sensitivity to construction variations. A comparative investigation reveals that the optimized gradation (OG) achieves a more stable volumetric condition and enhanced mechanical performance relative to conventional empirical gradations. Specifically, the OG group demonstrated a substantial 112% enhancement in dynamic stability (2617 times/mm compared to 1230 times/mm) and a 75% increase in average film thickness (AFT), while ensuring consistent moisture and low-temperature resistance. In conclusion, this study transforms asphalt mixture design from empirical trial-and-error to a precision-engineered methodology, providing a robust instrument for optimizing the long-term durability of pavements in extreme cold and arid environments. 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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18 pages, 2995 KB

Open AccessArticle

Oil Effect on Improving Cracking Resistance of SBSMA and Correlations Among Performance-Related Parameters of Binders and Mixtures

by Ronghua Gu, Jing Xu, Weihua Wan, Kai Zhang, Yaoting Zhu and Xiaoyong Tan

Materials 2025, 18(23), 5443; https://doi.org/10.3390/ma18235443 - 3 Dec 2025

Cited by 1 | Viewed by 494

Abstract

Asphalt binders that perform exceptionally well in resisting both rutting and cracking are highly desirable for withstanding the combined effects of extreme low temperatures and heavy vehicle loads. This work highlights the benefits of softening oils on the cracking performance of styrene–butadiene–styrene-modified asphalt (SBSMA). Additionally, the inherent correlations between cracking-performance parameters of binders and mixtures were thoroughly analyzed. A bio-based oil (bio-oil) and a petroleum-based oil (re-refined engine oil bottom, REOB) were selected as the softening oils. The benefit provided by softening oils was evaluated using various rheological indices, while the adverse effects of oxidative aging on cracking resistance were also considered. The cracking properties at intermediate temperatures were characterized by the modified Glover–Rowe (M G–R) parameter, δ_{8967 kPa}, and fatigue life (N_f). The low-temperature cracking properties of binders were evaluated by stiffness and m-value. The indirect tensile asphalt cracking (IDEAL-CT) test was conducted utilizing the CT-index and post-peak slope to estimate the fracture properties of the mixtures. The oxidative aging of binder and mixture samples was simulated and carried out based on lab aging methods; meanwhile, the carbonyl index obtained from the Fourier transform infrared (FTIR) scanning was used to track and evaluate the aging level of binders. The results show that the cracking performance could be greatly improved by the application of softening oils. Meanwhile, the bio-oils were found to operate with much higher efficiency than REOB, since the oil modification index (OMI) result showed that bio-oils exhibited four to six times the efficiency of REOB, in terms of improving the stress relaxation property. The correlations proved that the cracking-related parameters shared an inherent relationship with R² above 0.85, while these parameters consistently declined as the binder aged. The cracking performance of the mixtures at intermediate temperatures was mainly governed by the fatigue life of the binder, whereas thermal cracking performance was highly associated with the binder’s relaxation property. 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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21 pages, 3058 KB

Open AccessArticle

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 783

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 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

Open AccessArticle

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

Cited by 1 | Viewed by 956

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 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

Open AccessArticle

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

Cited by 1 | Viewed by 1074

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 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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