Skip to main content
SpringerLink
Log in

Unraveling the nexus between internal structural variability and macro-texture in asphalt mixtures: a mesoscopic investigation

  • Original Article
  • Published:
Materials and Structures Aims and scope Submit manuscript

Abstract

Macro-texture is crucial for its impact on tire-road friction, road safety, noise, and even greenhouse gas emissions. However, the substantial variability and uncertainty in surface texture pose challenges in optimizing the design and analysis system of asphalt pavement. Therefore, this study aims to identify the sources of macro-texture variability by detecting and quantifying meso-structural features and correlating them with asphalt pavement surface characterization. To achieve this objective, two typical macro-textural parameters, mean texture depth (MTD) and mean profile depth (MPD), were redefined in the three-dimensional domain using digital image processing techniques after eliminating the wall effect. Subsequently, thirty-nine meso-structural indicators were counted, grouped, and filtered based on Pearson correlation coefficients. Later, texture prediction/analysis models were developed and optimized through stepwise regression analysis and under-sampling techniques. The research results revealed significant variability in MTD and MPD, with coefficients of variation around 20%. The determination coefficients (R2) of the final models for MTD and MPD were 0.916 and 0.920, respectively, indicating the practicality and reliability of the proposed models. On this basis, these models provide a robust theoretical and methodological foundation for managing uncertainty and variability in macro-texture within asphalt pavement. Notably, aggregate-based variables, particularly those closely related to skeleton stability, were found to have a more significant impact on texture depth than void-structure variables. Therefore, optimizing pavement surface characteristics through adjusted aggregate-related design processes is more recommended.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

  1. Pratico FG, Vaiana R (2015) A study on the relationship between mean texture depth and mean profile depth of asphalt pavements. Constr Build Mater 101:72–79. https://doi.org/10.1016/j.conbuildmat.2015.10.021

    Article  Google Scholar 

  2. Dan H, Gao L, Wang H, Tang J (2022) Discrete-element modeling of mean texture depth and wearing behavior of asphalt mixture. J Mater Civ Eng 34:04022027. https://doi.org/10.1061/(ASCE)MT.1943-5533.0004165

    Article  Google Scholar 

  3. Liu X, Cao Q, Wang H, Chen J, Huang X (2019) Evaluation of vehicle braking performance on wet pavement surface using an integrated tire-vehicle modeling approach. Transp Res Record 2673:295–307. https://doi.org/10.1177/0361198119832886

    Article  Google Scholar 

  4. Wang H, Wang C, Bu Y, You Z, Yang X, Oeser M (2020) Correlate aggregate angularity characteristics to the skid resistance of asphalt pavement based on image analysis technology. Constr Build Mater 242:118150. https://doi.org/10.1016/j.conbuildmat.2020.118150

    Article  Google Scholar 

  5. Cui X, Zhou X, Lou J, Zhang J, Ran M (2017) Measurement method of asphalt pavement mean texture depth based on multi-line laser and binocular vision. Int J Pavement Eng 18:459–471. https://doi.org/10.1080/10298436.2015.1095898

    Article  Google Scholar 

  6. Chen B, Xiong C, Li W, He J, Zhang X (2021) Assessing surface texture features of asphalt pavement based on three-dimensional laser scanning technology. Build Basel 11:623. https://doi.org/10.3390/buildings11120623

    Article  Google Scholar 

  7. Gu F, Chen C, Heitzman M, Potter R, Powell B (2023) Evaluation of locked-wheel skid trailer and SCRIM friction measurements at NCAT test track. Int J Pavement Eng. https://doi.org/10.1080/10298436.2022.2124249

    Article  Google Scholar 

  8. Li QJ, Zhan Y, Yang G, Wang KCP (2020) Pavement skid resistance as a function of pavement surface and aggregate texture properties. Int J Pavement Eng 21:1159–1169. https://doi.org/10.1080/10298436.2018.1525489

    Article  Google Scholar 

  9. Xiao S, Li M, Chen B, Zhou X, Xi C, Tan Y (2023) Understanding the pavement texture evolution of RIOH Track using multi-scale and spatiotemporal analysis. Tribol Int 184:108492. https://doi.org/10.1016/j.triboint.2023.108492

    Article  Google Scholar 

  10. Freitas E, Freitas C, Braga AC (2014) The analysis of variability of pavement indicators: MPD, SMTD and IRI A case study of Portugal roads. Int J Pavement Eng 15:361–371. https://doi.org/10.1080/10298436.2013.807343

    Article  Google Scholar 

  11. Sahdeo SK, Ransinchung GD, Rahul KL, Debbarma S (2020) Effect of mix proportion on the structural and functional properties of pervious concrete paving mixtures. Constr Build Mater 255:119260. https://doi.org/10.1016/j.conbuildmat.2020.119260

    Article  Google Scholar 

  12. Ren Z, Tan Y, Huang L, Li G, Lv H (2023) Study on stochastic behavior of particle system in hot mix asphalt mixture from a meso-structural perspective. Constr Build Mater 372:130844. https://doi.org/10.1016/j.conbuildmat.2023.130844

    Article  Google Scholar 

  13. Liu W, Lu W, Liu X, Zhang L (2018) Variability analysis of asphalt mixture beam bending test. In: Sohn H, Lynch JP, Wang KW (eds) Sensors and smart structures technologies for civil, mechanical, and aerospace systems 2018, vol 10598. Bellingham, Spie-Int Soc Optical Engineering, p UNSP 1059831

    Google Scholar 

  14. Rao F, Zhang Z, Ye G, Liu J, Han J (2021) Mesostructure of foamed cement paste and its influence on macromechanical behavior. J Mater Civ Eng 33:04021114. https://doi.org/10.1061/(ASCE)MT.1943-5533.0003738

    Article  Google Scholar 

  15. Neumann J, Simon J-W, Mollenhauer K, Reese S (2017) A framework for 3D synthetic mesoscale models of hot mix asphalt for the finite element method. Constr Build Mater 148:857–873. https://doi.org/10.1016/j.conbuildmat.2017.04.033

    Article  Google Scholar 

  16. Ren Z, Tan Y, Huang L, Xiao S (2023) Fractal and multifractal characteristics of three-dimensional meso-structure for asphalt mixture. Constr Build Mater 384:131429. https://doi.org/10.1016/j.conbuildmat.2023.131429

    Article  Google Scholar 

  17. Zhang J, Li X, Ma W, Pei J (2019) Characterizing heterogeneity of asphalt mixture based on aggregate particles movements. Iran J Sci Technol-Trans Civ Eng 43:81–91. https://doi.org/10.1007/s40996-018-0125-0

    Article  Google Scholar 

  18. Neumann J, Simon J-W, Reese S (2018) Digital sieving of irregular 3D particles—a study using XRCT and statistically similar synthetic data. Powder Technol 338:1001–1015. https://doi.org/10.1016/j.powtec.2018.07.002

    Article  Google Scholar 

  19. Li T, Liu P, Du C, Schnittcher M, Hu J, Wang D et al (2022) Microstructural analysis of the effects of compaction on fatigue properties of asphalt mixtures. Int J Pavement Eng 23:9–20. https://doi.org/10.1080/10298436.2020.1728532

    Article  Google Scholar 

  20. Lv S, Liu C, Yao H, Zheng J (2018) Comparisons of synchronous measurement methods on various moduli of asphalt mixtures. Constr Build Mater 158:1035–1045. https://doi.org/10.1016/j.conbuildmat.2017.09.193

    Article  Google Scholar 

  21. Sun S, Li P, Cheng L, Wang X, Zhang W (2022) Analysis of skeleton contact stability of graded aggregates system and its effect on slip creep properties of asphalt mixture. Constr Build Mater 316:125911. https://doi.org/10.1016/j.conbuildmat.2021.125911

    Article  Google Scholar 

  22. Cai X, Wu KH, Huang WK, Wan C (2018) Study on the correlation between aggregate skeleton characteristics and rutting performance of asphalt mixture. Constr Build Mater 179:294–301. https://doi.org/10.1016/j.conbuildmat.2018.05.153

    Article  Google Scholar 

  23. JTG F40 (2004) Technical specification for construction of highway asphalt pavements. Ministry of Communications, China

    Google Scholar 

  24. Ren Z, Tan Y, Huang L, Yu H (2022) Optimization of automatic extraction procedure for particles in asphalt mixture towards superior robustness and accuracy. Constr Build Mater 342:128002. https://doi.org/10.1016/j.conbuildmat.2022.128002

    Article  Google Scholar 

  25. Zhao S, You Q, Sesay T (2022) Fine aggregate sizes effects on the creep behavior of asphalt mortar. Constr Build Mater 342:127931. https://doi.org/10.1016/j.conbuildmat.2022.127931

    Article  Google Scholar 

  26. Chen M, Javilla B, Hong W, Pan C, Riara M, Mo L et al (2019) Rheological and interaction analysis of asphalt binder. Mastic Mortar Mater 12:128. https://doi.org/10.3390/ma12010128

    Article  Google Scholar 

  27. Ren Z, Tan Y, Huang L, Yu H, Xiao S (2023) Irregular characteristic analysis of 3D particles—a novel virtual sieving technique. Powder Technol 420:118383. https://doi.org/10.1016/j.powtec.2023.118383

    Article  Google Scholar 

  28. AASHTO (2011) Standard method of test for sieve analysis of fine and coarse aggregates. AASHTO Designation: T 27; American Association of State Highway and Transportation Officials, Washington

    Google Scholar 

  29. Kim S, Guarin A, Roque R, Birgisson B (2008) Laboratory evaluation for rutting performance based on the DASR porosity of asphalt mixture. Road Mater Pavement Des 9:421–440. https://doi.org/10.3166/RMPD.9.421-440

    Article  Google Scholar 

  30. Kim S, Roque R, Birgisson B, Guarin A (2009) Porosity of the dominant aggregate size range to evaluate coarse aggregate structure of asphalt mixtures. J Mater Civ Eng 21:32–39. https://doi.org/10.1061/(ASCE)0899-1561(2009)21:1(32)

    Article  Google Scholar 

  31. Xing C, Zhang L, Anupam K, Tan Y, Wang D, Zhai C (2020) Particle distribution around the damage area of asphalt mixture based on digital image correlation. Powder Technol 375:11–19. https://doi.org/10.1016/j.powtec.2020.07.090

    Article  Google Scholar 

  32. Suzuki M, Shinmura T, Iimura K, Hirota M (2008) Study of the wall effect on particle packing structure using x-ray micro computed tomography. Adv Powder Technol 19:183–195. https://doi.org/10.1163/156855208X293817

    Article  Google Scholar 

  33. Li Q, Yang H, Ni F, Ma X, Luo L (2015) Cause analysis on permanent deformation for asphalt pavements using field cores. Constr Build Mater 100:40–51. https://doi.org/10.1016/j.conbuildmat.2015.09.012

    Article  Google Scholar 

  34. ISO 13473-1 (1997) Characterization of pavement texture by use of surface profiles—part 1: determination of mean profile depth. International Organization for Standardization, Geneve

    Google Scholar 

  35. Li P, Su J, Ma S, Dong H (2020) Effect of aggregate contact condition on skeleton stability in asphalt mixture. Int J Pavement Eng 21:196–202. https://doi.org/10.1080/10298436.2018.1450503

    Article  Google Scholar 

  36. Kusumawardani DM, Wong YD (2021) Effect of aggregate shape properties on performance of porous asphalt mixture. J Mater Civ Eng 33:04021208. https://doi.org/10.1061/(ASCE)MT.1943-5533.0003801

    Article  Google Scholar 

  37. Thyagarajan S, Tashman L, Masad E, Bayomy F (2010) The heterogeneity and mechanical response of hot mix asphalt laboratory specimens. Int J Pavement Eng 11:107–121. https://doi.org/10.1080/10298430902730521

    Article  Google Scholar 

  38. Hu J, Liu P, Wang D, Oeser M, Tan Y (2016) Investigation on fatigue damage of asphalt mixture with different air-voids using microstructural analysis. Constr Build Mater 125:936–945. https://doi.org/10.1016/j.conbuildmat.2016.08.138

    Article  Google Scholar 

  39. Xing C, Liang Z, Tan Y, Wang D, Zhai C (2021) Skeleton filling system evaluation method of asphalt mixture based on compressible packing model. J Transp Eng Pt B-Pavements 147:04021062. https://doi.org/10.1061/JPEODX.0000320

    Article  Google Scholar 

  40. Iwanski M, Mazurek G, Buczynski P, Zapala-Slaweta J (2021) Multidimensional analysis of foaming process impact on 50/70 bitumen ageing. Constr Build Mater 266:121231. https://doi.org/10.1016/j.conbuildmat.2020.121231

    Article  Google Scholar 

  41. Hussain F, Ali Y, Irfan M (2022) Quantifying the differential phase angle behaviour of asphalt concrete mixtures using artificial neural networks. Int J Pavement Res Technol 15:640–658. https://doi.org/10.1007/s42947-021-00042-0

    Article  Google Scholar 

  42. Liu J, Feng H, Tang Y, Zhang L, Qu C, Zeng X et al (2023) A novel hybrid algorithm based on Harris Hawks for tumor feature gene selection. PeerJ Comput Sci. https://doi.org/10.7717/peerj-cs.1229

    Article  Google Scholar 

  43. Barghabany P, Zhang J, Mohammad LN, Cooper SB, Cooper SB (2022) Novel model to predict critical strain energy release rate in semi-circular bend test as fracture parameter for asphalt mixtures using an artificial neural network approach. Transp Res Record 2676:388–400. https://doi.org/10.1177/03611981211036357

    Article  Google Scholar 

  44. Han Q, Yang R, Wan Z, Chen S, Huang M, Wen H (2020) Imbalanced data classification based on DB-SLSMOTE and random forest. In: 2020 Chinese automation congress (cac 2020). IEEE, New York, pp 6271–6276. https://doi.org/10.1109/CAC51589.2020.9326743

Download references

Acknowledgements

This work was supported by the Open Fund of the Key Laboratory of Transport Industry of Road Structure and Material, Research Institute of Highway Ministry of Transport, the funding National Natural Science Foundation of China joint fund for regional innovation and development [grant number U20A20315], and the China Scholarship Council.

Author information

Authors and Affiliations

  1. Key Laboratory of Transport Industry of Road Structure and Material, Research Institute of Highway Ministry of Transport, Beijing, 100088, China

    Zhibin Ren & Erhu Yan

  2. School of Transportation Science and Engineering, Harbin Institute of Technology, Harbin, 150090, China

    Zhibin Ren, Lan Huang & Yiqiu Tan

  3. Department of Civil and Environmental Engineering, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133, Milan, Italy

    Zhibin Ren & Maurizio Crispino

  4. Heilongjiang Transportation Investment Group Co., Ltd., Harbin, 150060, China

    Baocai He

Authors
  1. Zhibin Ren
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Erhu Yan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Baocai He
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Maurizio Crispino
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Lan Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Yiqiu Tan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yiqiu Tan.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ren, Z., Yan, E., He, B. et al. Unraveling the nexus between internal structural variability and macro-texture in asphalt mixtures: a mesoscopic investigation. Mater Struct 57, 48 (2024). https://doi.org/10.1617/s11527-024-02329-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1617/s11527-024-02329-7

Keywords

Search

Navigation