Volume 26 Issue 8
Aug.  2026
Turn off MathJax
Article Contents
Article Navigation >  Journal of Traffic and Transportation Engineering  > 2026  >  26(8): 129-146
Next Previous
XING Chen, HUI Bing, DU Xiao-yi, MA Xin-yan, WANG Hai-nian. A prediction model for bonding strength of asphalt overlay on airport pavement based on texture feature data[J]. Journal of Traffic and Transportation Engineering, 2026, 26(8): 129-146. doi: 10.19818/j.cnki.1671-1637.2026.239
Citation: XING Chen, HUI Bing, DU Xiao-yi, MA Xin-yan, WANG Hai-nian. A prediction model for bonding strength of asphalt overlay on airport pavement based on texture feature data[J]. Journal of Traffic and Transportation Engineering, 2026, 26(8): 129-146. doi: 10.19818/j.cnki.1671-1637.2026.239
shu

A prediction model for bonding strength of asphalt overlay on airport pavement based on texture feature data

doi: 10.19818/j.cnki.1671-1637.2026.239
  • 1.

    School of Highway, Chang'an University, Xi'an 710064, Shaanxi, China

  • 2.

    Key Laboratory of Intelligent Construction and Maintenance of CAAC, Chang'an University, Xi'an 710064, Shaanxi, China

  • 3.

    Civil Aviation Research Base (Beijing) Co., Ltd., Beijing 100029, China

Funds:

Joint Funds of the National Natural Science Foundation of China U2433210

National Natural Science Foundation of China 52578500

More Information
  • Corresponding author: HUI Bing, professor, PhD, E-mail: 82628532@qq.com
  • Received Date: 2025-12-08
  • Accepted Date: 2026-03-19
  • Rev Recd Date: 2026-02-20
  • Publish Date: 2026-08-28
    Abstract
  • To accurately predict the bonding strength of "white-to-black" airport pavement interfaces and reveal the underlying mechanism linking interface texture to bonding performance, aged concrete slabs under 16 different treatment conditions were prepared. Three-dimensional laser scanning was used to extract geometric, spectral, and fractal indicators, establishing a multi-index texture characterization system covering geometric amplitude, distribution, and structural complexity. After placing asphalt mixture overlay to form "white-to-black" composite specimens, shear tests were conducted at three temperatures. A standardized shear strength dataset driven by multi-dimensional texture data was constructed. An ensemble machine-learning model combining a multilayer perceptron (MLP) and extreme gradient boosting (XGBoost) was developed, and an improved raccoon optimization algorithm (ICOA) was proposed for hyperparameter tuning. Finally, Shapley Additive Explanations (SHAP) was applied to quantify the contribution of each feature parameter to the predictions. The results show that when the milling depth is 6-10 mm, the shear strength can still maintain 0.65 MPa at 50 ℃. Compared with a milling depth of 4-6 mm, the strength increases by 67.9%-69.5%, indicating a significant improvement in high-temperature shear resistance for airport pavements. The ICOA-MLP-XGBoost model outperforms other models, with R2 improved by 6.1%-10.9% and MAE reduced by 35.8%-46.9%. It achieved higher prediction accuracy and better generalization, with improved robustness for engineering use. SHAP analysis indicates that mean texture depth, spectral entropy, and fractal dimension are the dominant factors affecting shear strength. Given the shear-stress concentration under heavy loads with high tire pressure at low speed on airport pavements, an interface milling depth of 6.0-8.0 mm is recommended for interface treatment to achieve high SHAP contribution ranges for these three indicators. This study provides a highly accurate and interpretable data-driven method for predicting interface performance in "white-to-black" airport pavements, which can guide interface-treatment optimization and improve pavement durability.

     

    • FullText(HTML)
  • loading
    • References(57)
  • [1]
    JIANG Li, SU Yu, CHENG Wei, et al. Finite element analysis of temperature stress of "white to black" pavement based on stress absorption layer[J]. Science Technology and Engineering, 2019, 19(3): 232-238.
    [2]
    XIANG Ze-jun, LUO Zai-qian, WANG Ming. Application of digital panorama map to road project during the night work[J]. Bulletin of Surveying and Mapping, 2011(9): 42-44, 69.
    [3]
    WU C Y, HONG S X, CHEN T, et al. Application of hot-mixed ultrathin friction course in the white-to-black maintenance project of concrete bridge deck[J]. Journal of Performance of Constructed Facilities, 2022, 36: 04021116. doi: 10.1061/(ASCE)CF.1943-5509.0001697
    [4]
    DAS R, MOHAMMAD L N, ELSEIFI M, et al. Effects of tack coat application on interface bond strength and short-term pavement performance[J]. Transportation Research Record: Journal of the Transportation Research Board, 2017, 2633(1): 1-8.
    [5]
    SALINAS A, AL-QADI I L, HASIBA K I, et al. Interface layer tack coat optimization[J]. Transportation Research Record: Journal of the Transportation Research Board, 2013(2372): 53-60.
    [6]
    RAPOSEIRAS A C, CASTRO-FRESNO D, VEGA-ZAMANILLO A, et al. Test methods and influential factors for analysis of bonding between bituminous pavement layers[J]. Construction and Building Materials, 2013, 43: 372-381. doi: 10.1016/j.conbuildmat.2013.02.011
    [7]
    KOZUBAL J V, HASSANAT A, TARAWNEH A S, et al. Automatic strength assessment of the virtually modelled concrete interfaces based on shadow-light images[J]. Construction and Building Materials, 2022, 359: 129296. doi: 10.1016/j.conbuildmat.2022.129296
    [8]
    MOHAMAD M E, IBRAHIM I S, ABDULLAH R, et al. Friction and cohesion coefficients of composite concrete-to-concrete bond[J]. Cement and Concrete Composites, 2015, 56: 1-14.
    [9]
    COURARD L, PIOTROWSKI T, GARBACZ A. Near-to-surface properties affecting bond strength in concrete repair[J]. Cement and Concrete Composites, 2014, 46: 73-80. doi: 10.1016/j.cemconcomp.2013.11.005
    [10]
    SONG W M, SHU X, HUANG B S, et al. Influence of interface characteristics on the shear performance between open-graded friction course and underlying layer[J]. Journal of Materials in Civil Engineering, 2017, 29(8): 04017077. doi: 10.1061/(ASCE)MT.1943-5533.0001907
    [11]
    ZHANG B S, YU J J, CHEN W Z, et al. Experimental study on bond performance of NC-UHPC interfaces with different roughness and substrate strength[J]. Materials, 2023, 16(7): 2708. doi: 10.3390/ma16072708
    [12]
    VALIKHANI A, JAHROMI A J, MANTAWY IM, et al. Experimental evaluation of concrete-to-UHPC bond strength with correlation to surface roughness for repair application[J]. Construction and Building Materials, 2020, 238: 117753. doi: 10.1016/j.conbuildmat.2019.117753
    [13]
    SHEN Y J, YANG H W, XI J M, et al. A novel shearing fracture morphology method to assess the influence of freeze-thaw actions on concrete-granite interface[J]. Cold Regions Science and Technology, 2020, 169: 102900. doi: 10.1016/j.coldregions.2019.102900
    [14]
    TANG Z P, HUANG F L, PENG H. Effect of 3D roughness characteristics on bonding behaviors between concrete substrate and asphalt overlay[J]. Construction and Building Materials, 2021, 270: 121386. doi: 10.1016/j.conbuildmat.2020.121386
    [15]
    ORESHKIN O, PLATONOV A, PANOV D, et al. Comparison of metrics for shape quality evaluation of textures produced by laser structuring by remelting (waveshape)[J]. Micromachines, 2022, 13(4): 618. doi: 10.3390/mi13040618
    [16]
    CHEN D. Evaluating asphalt pavement surface texture using 3D digital imaging[J]. International Journal of Pavement Engineering, 2020, 21(4): 416-427. doi: 10.1080/10298436.2018.1483503
    [17]
    ZHANG Ling, CHEN Yun-hao, WANG Wen-tao, et al. Strength prediction for coarse-grained soil strength subjected to penetrating erosion and cyclic loading via a data-driven approach[J]. China Journal of Highway and Transport, 2025, 38(10): 228-239.
    [18]
    YAO JUN-feng, HE RUI, SHI TONG-tong, et al. Review on machine learning based traffic flow prediction methods[J]. Journal of Traffic and Transportation Engineering, 2023, 23(3): 44-67. doi: 10.19818/j.cnki.1671-1637.2023.03.003
    [19]
    SADOWSKI Ł, HOŁA J, CZARNECKI S, et al. Pull-off adhesion prediction of variable thick overlay to the substrate[J]. Automation in Construction, 2018, 85: 10-23. doi: 10.1016/j.autcon.2017.10.001
    [20]
    TAN YI-qiu, XIAO SHEN-qing, XIONG XUE-tang, et al. Review on detection and prediction methods for pavement skid resistance[J]. Journal of Traffic and Transportation Engineering, 2021, 21(4): 32-47. doi: 10.19818/j.cnki.1671-1637.2021.04.002
    [21]
    QU Shi-qi, LIANG Zun-dong, ZHANG Xin. Enhanced XGBoost-based prediction method for dynamic modulus and phase angle of asphalt mixture[J]. Science Technology and Engineering, 2025, 25(3): 1225-1234.
    [22]
    CUI Jian-xun, YAO Jia, ZHAO Po Yuan. Review on short-term traffic flow prediction methods based on deep learning[J]. Journal of Traffic and Transportation Engineering, 2024, 24(2): 50-64. doi: 10.19818/j.cnki.1671-1637.2024.02.003
    [23]
    WANG YI-bing, YU HONG-xin, JIN FENG-yue, et al. Freeway traffic state estimation using continuum-flow-model guided deep learning[J]. Journal of Traffic and Transportation Engineering, 2025, 12: 1-31.
    [24]
    MA Ze-chao, LIU Xiao-ming, XIA Han-qing, et al. Pavement distress situation prediction method based on graph neural network[J]. Journal of Zhejiang University (Engineering Science), 2024, 58(12): 2596-2608.
    [25]
    ZOU Zheng, CHEN Jiang, LANG Hong, et al. A review of pavement distress detection based on machine learning methods[J]. Journal of Transport Information and Safety, 2025, 43(2): 154-168.
    [26]
    CAO Yu-gui, WEI Jia-hao, MEI Zhi-yao, et al. Prediction of bond strength between FRP bars and UHPC using a hybrid machine learning algorithm[J]. Journal of Shenyang Jianzhu University (Natural Science), 2025, 41(3): 289-298.
    [27]
    LIU Wen-bo, YANG Xi-juan, WANG Li, et al. Study on interfacial bond strength of concrete-filled steel tube based on SSA-RBFNN[J]. Journal of Safety Science and Technology, 2025, 21(3): 148-155.
    [28]
    ZHANG C, SHEN S H, HUANG H, et al. In-situ dynamic modulus prediction for asphalt pavement combining machine learning algorithm and sensing technology[J]. IEEE Transactions on Intelligent Transportation Systems, 2024, 25(8): 8695-8704. doi: 10.1109/TITS.2024.3385649
    [29]
    VAN DAO D, NGUYEN N L, LY H B, et al. Cost-effective approaches based on machine learning to predict dynamic modulus of warm mix asphalt with high reclaimed asphalt pavement[J]. Materials, 2020, 13(15): 3272. doi: 10.3390/ma13153272
    [30]
    FENG D C, WANG W J, MANGALATHU S, et al. Interpretable XGBoost-SHAP machine-learning model for shear strength prediction of squat RC walls[J]. Journal of Structural Engineering, 2021, 147(11): 04021173. doi: 10.1061/(ASCE)ST.1943-541X.0003115
    [31]
    MEDDAGE D P P, EKANAYAKE I U, WEERASURIYA A U, et al. Explainable Machine Learning (XML) to predict external wind pressure of a low-rise building in urban-like settings[J]. Journal of Wind Engineering and Industrial Aerodynamics, 2022, 226: 105027. doi: 10.1016/j.jweia.2022.105027
    [32]
    RANJBARAN G, RECUPERO D R, ROY C K, et al. C-SHAP: A hybrid method for fast and efficient interpretability[J]. Applied Sciences, 2025, 15(2): 672. doi: 10.3390/app15020672
    [33]
    WANG H J, LIANG Q X, HANCOCK J T, et al. Feature selection strategies: A comparative analysis of SHAP-value and importance-based methods[J]. Journal of Big Data, 2024, 11(1): 44. doi: 10.1186/s40537-024-00905-w
    [34]
    XING C, HUI B, WANG H N, et al. Research on the roughness characteristics of cement concrete base after treatment based on 3D laser detection technology[J]. Construction and Building Materials, 2025, 460: 139818. doi: 10.1016/j.conbuildmat.2024.139818
    [35]
    LIANG J, GU X Y, CHEN Y Z, et al. A novel pavement mean texture depth evaluation strategy based on three-dimensional pavement data filtered by a new filtering approach[J]. Measurement, 2020, 166: 108265. doi: 10.1016/j.measurement.2020.108265
    [36]
    MAHATA S, KAR R, MANDAL D. Optimal modelling of (1+α) order butterworth filter under the CFE framework[J]. Fractal and Fractional, 2020, 4(4): 55. doi: 10.3390/fractalfract4040055
    [37]
    XU Xing-xing, HUANG Chang. Container lock pins recognition based on a self-built dataset with high completeness[J]. Journal of East China Normal University (Natural Science), 2025(4): 28-37.
    [38]
    DUNLOP P, SMITH S. Estimating key characteristics of the concrete delivery and placement process using linear regression analysis[J]. Civil Engineering and Environmental Systems, 2003, 20(4): 273-290. doi: 10.1080/1028660031000091599
    [39]
    YU H, WANG Q F, SHI J Y. Data augmentation generated by generative adversarial network for small sample datasets clustering[J]. Neural Processing Letters, 2023, 55(6): 8365-8384. doi: 10.1007/s11063-023-11315-z
    [40]
    LIN L Y, ZHAO S X, ZHANG Y R, et al. Purposive data augmentation strategy and lightweight classification model for small sample industrial defect dataset[J]. IEEE Transactions on Industrial Informatics, 2024, 20(9): 11475-11484. doi: 10.1109/TII.2024.3404053
    [41]
    FENG K, MA S X, XI H Y, et al. Small-sample-data augmentation and transfer strategies for forest cover change monitoring[J]. Ecological Indicators, 2025, 178: 113870. doi: 10.1016/j.ecolind.2025.113870
    [42]
    GAO S, YANG W H, XU M L, et al. U-MLP: MLP-based ultralight refinement network for medical image segmentation[J]. Computers in Biology and Medicine, 2023, 165: 107460. doi: 10.1016/j.compbiomed.2023.107460
    [43]
    NIAZKAR M, MENAPACE A, BRENTAN B, et al. Applications of XGBoost in water resources engineering: A systematic literature review[J]. Environmental Modelling & Software, 2024, 174: 105971.
    [44]
    QIU Y G, ZHOU J, KHANDELWAL M, et al. Performance evaluation of hybrid WOA-XGBoost, GWO-XGBoost and BO-XGBoost models to predict blast-induced ground vibration[J]. Engineering with Computers, 2022, 38(5): 4145-4162.
    [45]
    HUANG J D, KUMAR G S, REN J L, et al. Accurately predicting dynamic modulus of asphalt mixtures in low-temperature regions using hybrid artificial intelligence model[J]. Construction and Building Materials, 2021, 297: 123655. doi: 10.1016/j.conbuildmat.2021.123655
    [46]
    ZHANG Y, XU X J. Modulus of elasticity predictions through LSBoost for concrete of normal and high strength[J]. Materials Chemistry and Physics, 2022, 283: 126007. doi: 10.1016/j.matchemphys.2022.126007
    [47]
    LI Qian-yun, WANG Zi-xi, LI Jie, et al. Research on the influence of meteorological factors on ground-level ozone in Beijing from 2018 to 2022 based on generalized additive model[J]. Acta Scientiae Circumstantiae, 2024, 44(4): 206-214.
    [48]
    WU Guo-chao, LI Ai-lian, XIE Shao-feng. Improved black-winged kite optimization algorithm-light gradient boosting machine model to predict converter steelmaking endpoint temperature[J]. Journal of Materials and Metallurgy, 2026, 25(1): 37-45.
    [49]
    ADLER J, RIBAK E N. Simulated annealing in application to telescope phasing[J]. Physica A: Statistical Mechanics and Its Applications, 2021, 572: 125900. doi: 10.1016/j.physa.2021.125900
    [50]
    ZHANG K, MIN Z H, HAO X T, et al. Enhancing understanding of asphalt mixture dynamic modulus prediction through interpretable machine learning method[J]. Advanced Engineering Informatics, 2025, 65: 103111. doi: 10.1016/j.aei.2025.103111
    [51]
    ZHANG F, CANNONE FALCHETTO A, WANG D, et al. Prediction of asphalt rheological properties for paving and maintenance assistance using explainable machine learning[J]. Fuel, 2025, 396: 135319. doi: 10.1016/j.fuel.2025.135319
    [52]
    PHUNG B N, LE T H, MAI H T, et al. Advancing basalt fiber asphalt concrete design: A novel approach using gradient boosting and metaheuristic algorithms[J]. Case Studies in Construction Materials, 2023, 19: 02528.
    [53]
    KENNEDY D M, VAHEY J, HANNEY D. Micro shot blasting of machine tools for improving surface finish and reducing cutting forces in manufacturing[J]. Materials and Design, 2005, 26(3): 203-208. doi: 10.1016/j.matdes.2004.02.013
    [54]
    LU X H, JIA Z Y, ZHANG H X, et al. Tool point frequency response prediction for micromilling by receptance coupling substructure analysis[J]. Journal of Manufacturing Science and Engineering, 2017, 139(7): 071004. doi: 10.1115/1.4035491
    [55]
    LI L, WANG K C P, LI Q, et al. Impacts of sample size on calculation of pavement texture indicators with 1mm 3D surface data[J]. Periodica Polytechnica Transportation Engineering, 2017, 46(1): 42. doi: 10.3311/PPtr.9587
    [56]
    QIAN J S, CEN Y B, et al. Spectrum parameters for runway roughness based on statistical and vibration analysis[J]. International Journal of Pavement Engineering, 2022, 23(11): 3757-3769. doi: 10.1080/10298436.2021.1916821
    [57]
    WONG T T, YEH P Y. Reliable accuracy estimates from k-fold cross validation[J]. IEEE Transactions on Knowledge and Data Engineering, 2020, 32(8): 1586-1594. doi: 10.1109/TKDE.2019.2912815
    • Relative Articles
    • Supplements(0)
    • Cited By

Catalog

    Get Citation
    PDF
    XML

    Article Metrics

    Article views (119) PDF downloads(12) Cited by()
    Related

    Website copyright Journal of Traffic and Transportation Engineering Editorial Department陕ICP备05001904号-1

    Address ：Editorial Department of Journal of Traffic and Transportation Engineering, Chang'an University, Middle Section of South 2nd Ring Road, Xi'an, China（710064） Tel：029-82334388 Email：jygc@chd.edu.cn

    All visit:Today's visit:

    Supported by: Beijing Renhe Information Technology Co. Ltd    

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return