Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Advertisement

Scientific Reports
  • View all journals
  • Search
  • My Account Login
  • Content Explore content
  • About the journal
  • Publish with us
  • Sign up for alerts
  • RSS feed
  1. nature
  2. scientific reports
  3. articles
  4. article
Study of surface texture wavelength slope spectra density distribution of micro-surfacing pavement related to vehicle interior noise
Download PDF
Download PDF
  • Article
  • Open access
  • Published: 02 February 2026

Study of surface texture wavelength slope spectra density distribution of micro-surfacing pavement related to vehicle interior noise

  • Jiangtao Lin1,
  • Hao Liang1,
  • Hao Wang2,
  • Zhenxiang Zhu2,
  • Liang Fan1,
  • Tao Liu1 &
  • …
  • Peihan Yu3 

Scientific Reports , Article number:  (2026) Cite this article

We are providing an unedited version of this manuscript to give early access to its findings. Before final publication, the manuscript will undergo further editing. Please note there may be errors present which affect the content, and all legal disclaimers apply.

Subjects

  • Engineering
  • Materials science

Abstract

Micro-surfacing pavements often exhibit elevated interior noise levels, posing challenges for environmental comfort. This study aimed to explore the surface texture wavelength characteristics of such pavements and their relationship with interior noise. A three-dimensional texture laser scanning system was used to determine the slope spectral density (SSD) of surface texture wavelengths, while environmental vibration analyzers measured interior noise and frequency spectra. Compared to SMA-13 pavements, micro-surfacing pavements exhibited higher noise levels in the low-mid frequency range (50–800 Hz) and high-frequency range (5000–16000 Hz). The SSD distributions of surface texture wavelengths were found to conform to Gaussmod functions with determination coefficients (R²) above 0.99. Strong linear correlations were observed between interior noise and SSD parameters, including peak value, peak area, and wavelength band area ratio. Particularly, node wavelengths between 10 and 20 mm showed determination coefficients (R²) exceeding 0.96 with interior noise, suggesting that reducing the area ratio of these wavelengths can significantly lower noise in the low-mid frequency range. Based on these findings, we propose a design threshold for low-noise micro-surfacing pavements: the area ratio for 10 mm node wavelengths should not exceed 50%. This recommendation provides a practical framework for future pavement designs aiming to minimize noise levels.

Data availability

The data presented in this study are generated in the laboratory and are not publicly available due to institutional policy. However, the data can be made available upon reasonable request to the corresponding author.

Abbreviations

SSD:

Slope spectra density

SPL:

Sound pressure level

References

  1. Asdrubali, F., Schiavoni, S. & Horoshenkov, K. V. A review of sustainable materials for acoustic applications. Build. Acoust. 19, 283–312. https://doi.org/10.1260/1351-010X.19.4.283 (2012).

    Google Scholar 

  2. Fredianelli, L. et al. Traffic flow detection using camera images and machine learning methods in ITS for noise map and action plan optimization. Sensors 22, 1929. https://doi.org/10.3390/s22051929 (2022).

    Google Scholar 

  3. Alías, F. & Socoró, J. C. A comprehensive study of a robust and efficient speech/music classification approach applied to broadcast audio recordings. Appl. Sci. 9, 441. https://doi.org/10.3390/app9030441 (2019).

    Google Scholar 

  4. Liu, Y., Kang, J. & Behm, H. Birdsong as an element of the urban sound environment: A case study concerning the area of Warnemünde in Germany. Acta Acust United Acust. 106, 1–9. https://doi.org/10.3813/AAA.919308 (2020).

    Google Scholar 

  5. Pallas, M. A. et al. Towards a model for electric vehicle noise emission in the European prediction method CNOSSOS-EU. Appl. Acoust. 113, 89–101. https://doi.org/10.1016/j.apacoust.2016.06.012 (2016).

    Google Scholar 

  6. Licitra, G., Teti, L., Cerchiai, M. & Bianco, F. Electric vehicles diffusion changing pavement acoustic design. Transp. Res. Part. D Transp. Environ. 112, 103514. https://doi.org/10.1016/j.trd.2022.103514 (2023).

    Google Scholar 

  7. Tyagi, A., Kumar, R. & Kapoor, K. (eds) Characterisation of microsurfacing mix design: A review. In Proceedings of Indian Geotechnical and Geoenvironmental Engineering Conference (IGGEC) 2021; Agnihotri, A.K., Singh, S.P., Eds.; Springer: Singapore, 2022; Volume 2, pp. 251–265. https://doi.org/10.1007/978-981-19-4731-5_23

  8. Moura, C. F. N. et al. Development and application of a micro-surfacing mix design method to assess the influence of the emulsion type. Appl. Sci. 13, 7925. https://doi.org/10.3390/app13137925 (2023).

    Google Scholar 

  9. Usman, K. R. et al. Performance evaluation of asphalt micro surfacing – a review. IOP Conf. Ser. Mater. Sci. Eng. 527, 012052. https://doi.org/10.1088/1757-899X/527/1/012052 (2019).

    Google Scholar 

  10. Keymanesh, M. R. et al. Mix design and performance evaluation of microsurfacing containing electric Arc furnace (EAF) steel slag filler. Constr. Build. Mater. 269, 121336. https://doi.org/10.1016/j.conbuildmat.2020.121336 (2021).

    Google Scholar 

  11. Wang, C. H. et al. Preparation and effect of environment-friendly cooling micro-surfacing. China J. Highw Transp. 30, 9–17. https://doi.org/10.19721/j.cnki.1001-7372.2017.07.002 (2017).

    Google Scholar 

  12. Robati, M., Carter, A. & Perraton, D. Evaluation of a modification of current microsurfacing mix design procedures. Can. J. Civ. Eng. 42, 319–328. https://doi.org/10.1139/cjce-2012-0063 (2012).

    Google Scholar 

  13. Hencken, J. et al. An evaluation of noise related to pavement preservation surfaces in NJ. In Asphalt Pavements 521–530 (CRC, 2014). https://doi.org/10.1201/b16831-48.

    Google Scholar 

  14. Newstead, B. et al. Investigation of ambient noise, surface quality improvements, and friction characteristics of different asphalt surfaces in Alberta. Can. Can. J. Civ. Eng. 47, 842–853. https://doi.org/10.1139/cjce-2019-0342 (2020).

    Google Scholar 

  15. Li, W. et al. Research on road performances and noise reduction characteristic of rubber-fiber micro-surfacing mixture. J. Railw Sci. Eng. 14, 1623–1631. https://doi.org/10.19721/j.cnki.2095-0802.2017.08.003 (2017).

    Google Scholar 

  16. Sun, X. L., Zhang, X. N. & Cai, X. Experiment of noise characteristics of different types of micro-surfacing. J. Highw Transp. Res. Dev. 29, 18–22. https://doi.org/10.19721/j.cnki.1002-0268.2012.02.004 (2012).

    Google Scholar 

  17. Katicha, S. W. et al. Adaptive Spike removal method for high-speed pavement macrotexture measurements by controlling the false discovery rate. Transp. Res. Rec J. Transp. Res. Board. 2525, 100–110. https://doi.org/10.3141/2525-11 (2015).

    Google Scholar 

  18. Chen, B. & Thesis, P. D. Research on asphalt pavement skid resistance performance evaluation method of based on tire-pavement effective contact characteristics. South China University of Technology, Guangzhou, China, (2018).

  19. Serigos, P. A., Smit, A. & de Fortier; Prozzi, J. A. Incorporating surface microtexture in the prediction of skid resistance of flexible pavements. J. Transp. Res. Board. 2457, 105–113. https://doi.org/10.3141/2457-13 (2014).

    Google Scholar 

  20. Cui, P. et al. Artificial neural network modeling for predicting surface texture and its Attenuation of micro-surfacing containing steel slag aggregates. Constr. Build. Mater. 312, 128504. https://doi.org/10.1016/j.conbuildmat.2021.128504 (2022).

    Google Scholar 

  21. Mikhailenko, P. et al. Low-noise pavement technologies and evaluation techniques: A literature review. Int. J. Pavement Eng. 23, 1911–1934. https://doi.org/10.1080/10298436.2020.1767074 (2022).

    Google Scholar 

  22. Sandberg, U. & Descornet, G. Road surface influence on tire/road noise – part I. In Proceedings of Inter-Noise 80; Bernhard, R.J., Ed.; Noise Control Foundation: Miami, FL, USA, ; pp. 1–6. (1980).

  23. Garcia, N. Z. Predicting friction with improved texture characterization. Ph.D. Thesis, University of Texas at Austin, Austin, TX, USA, (2017).

  24. Hartikainen, L., Petry, F. & Westermann, S. Frequency-wise correlation of the power spectral density of asphalt surface roughness and tire wet friction. Wear 317, 111–119. https://doi.org/10.1016/j.wear.2014.01.015 (2014).

    Google Scholar 

  25. Wang, D. W. et al. Overview on evaluation methods of pavement evenness for pavements without speed limiting expressway in Germany. Chin. J. Highw. 32, 105–113. https://doi.org/10.19721/j.cnki.1001-7372.2019.04.015 (2019).

    Google Scholar 

  26. Duan, S. H. et al. Evaluation and extracting characteristic parameters for a road profile based on PSD. J. Vib. Shock. 32, 26–30. https://doi.org/10.13465/j.cnki.jvs.2013.04.017 (2013).

    Google Scholar 

  27. Steinauer, B. Approaches for a three-dimensional assessment of road evenness data based on three-dimensional vehicle. In Proceedings of the Transportation Research Congress, Berlin, Germany, ; pp. 1–36. (2017). https://doi.org/10.1016/j.conbuildmat.2021.12850

  28. Ueckermann, A. & Steinauer, B. The weighted longitudinal profile. Road. Mater. Pavement Des. 9, 135–157. https://doi.org/10.1080/14680629.2008.9690123 (2008).

    Google Scholar 

  29. Sayers, M. W. The Little Book of Profiling—Basic Information about Measuring and Interpreting Road Profiles; University of Michigan Transportation Research Institute: Ann Arbor, MI, USA, (1998).

  30. Liu, Y. & Thesis, P. D. Structural and acoustic coupling analysis of medium and low frequency noise in vehicle. Shanghai Jiao Tong University, Shanghai, China, (2005).

  31. Ding, W. P. Research on sound source identification of indoor low frequency noise of vehicle ride. China Mech. Eng. 14, 262–265. https://doi.org/10.3969/j.issn.1004-132X.2003.03.017 (2003).

    Google Scholar 

  32. Han, X. et al. Study on automotive interior sound field refinement based on panel acoustic contribution analysis. J. Shanghai Jiao Tong Univ. 42, 1254–1258. https://doi.org/10.3969/j.issn.1007-1172.2008.08.023 (2008).

    Google Scholar 

  33. Gui, S. R., Fang, Y. & Chen, S. S. Effect of road roughness coherent function on vehicle-bridge coupling random vibration. J. Jiangsu Univ. (Nat Sci. Ed). 40, 87–93. https://doi.org/10.3969/j.issn.1671-7775.2019.01.013 (2019).

    Google Scholar 

Download references

Funding

This research was funded by Shandong Transportation Science and Technology Innovation Plan Project, grant number 2021106160297.

Author information

Authors and Affiliations

  1. Shandong Transportation Institute, Jinan, China

    Jiangtao Lin, Hao Liang, Liang Fan & Tao Liu

  2. Shandong Hi-speed Company Limited, Jinan, China

    Hao Wang & Zhenxiang Zhu

  3. Shandong Hi-Speed Group Yantai Development Co., Ltd, Yantai, China

    Peihan Yu

Authors
  1. Jiangtao Lin
    View author publications

    Search author on:PubMed Google Scholar

  2. Hao Liang
    View author publications

    Search author on:PubMed Google Scholar

  3. Hao Wang
    View author publications

    Search author on:PubMed Google Scholar

  4. Zhenxiang Zhu
    View author publications

    Search author on:PubMed Google Scholar

  5. Liang Fan
    View author publications

    Search author on:PubMed Google Scholar

  6. Tao Liu
    View author publications

    Search author on:PubMed Google Scholar

  7. Peihan Yu
    View author publications

    Search author on:PubMed Google Scholar

Contributions

Conceptualization, Jiangtao Lin. and Hao Liang; methodology, Jiangtao Lin; software, Hao Wang and Peihan Yu; formal analysis, Zhenxiang Zhu; in-vestigation, Liang Fan; data curation, Tao Liu; writing—original draft preparation, Jiangtao Lin; writing—review and editing, Hao Liang. All authors have read and agreed to the published version of the manuscript.

Corresponding authors

Correspondence to Jiangtao Lin or Liang Fan.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note

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

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Lin, J., Liang, H., Wang, H. et al. Study of surface texture wavelength slope spectra density distribution of micro-surfacing pavement related to vehicle interior noise. Sci Rep (2026). https://doi.org/10.1038/s41598-026-38065-x

Download citation

  • Received: 08 September 2025

  • Accepted: 28 January 2026

  • Published: 02 February 2026

  • DOI: https://doi.org/10.1038/s41598-026-38065-x

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

Keywords

  • Micro-surfacing pavements
  • Interior noise
  • Surface texture wavelength
  • Slope spectra density SSD
  • Power spectral density PSD
Download PDF

Advertisement

Explore content

  • Research articles
  • News & Comment
  • Collections
  • Subjects
  • Follow us on Facebook
  • Follow us on Twitter
  • Sign up for alerts
  • RSS feed

About the journal

  • About Scientific Reports
  • Contact
  • Journal policies
  • Guide to referees
  • Calls for Papers
  • Editor's Choice
  • Journal highlights
  • Open Access Fees and Funding

Publish with us

  • For authors
  • Language editing services
  • Open access funding
  • Submit manuscript

Search

Advanced search

Quick links

  • Explore articles by subject
  • Find a job
  • Guide to authors
  • Editorial policies

Scientific Reports (Sci Rep)

ISSN 2045-2322 (online)

nature.com sitemap

About Nature Portfolio

  • About us
  • Press releases
  • Press office
  • Contact us

Discover content

  • Journals A-Z
  • Articles by subject
  • protocols.io
  • Nature Index

Publishing policies

  • Nature portfolio policies
  • Open access

Author & Researcher services

  • Reprints & permissions
  • Research data
  • Language editing
  • Scientific editing
  • Nature Masterclasses
  • Research Solutions

Libraries & institutions

  • Librarian service & tools
  • Librarian portal
  • Open research
  • Recommend to library

Advertising & partnerships

  • Advertising
  • Partnerships & Services
  • Media kits
  • Branded content

Professional development

  • Nature Awards
  • Nature Careers
  • Nature Conferences

Regional websites

  • Nature Africa
  • Nature China
  • Nature India
  • Nature Japan
  • Nature Middle East
  • Privacy Policy
  • Use of cookies
  • Legal notice
  • Accessibility statement
  • Terms & Conditions
  • Your US state privacy rights
Springer Nature

© 2026 Springer Nature Limited

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing