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A Laboratory and Field Universal Estimation Method for Tire–Pavement Interaction Noise (TPIN) Based on 3D Image Technology

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  • Hui Wang

    (Key Laboratory of New Technology for Construction of Cities in Mountain Area, Chongqing University, Ministry of Education, Chongqing 400045, China
    School of Civil Engineering, Chongqing University, Chongqing 400045, China)

  • Xun Zhang

    (School of Civil Engineering, Chongqing University, Chongqing 400045, China)

  • Shengchuan Jiang

    (The Key Laboratory of Road and Traffic Engineering, Ministry of Education, Tongji University, Shanghai 201804, China
    Shanghai Tonglu Cloud Transportation Technology Co. Ltd., Shanghai 201800, China)

Abstract

Tire–pavement interaction noise (TPIN) accounts mainly for traffic noise, a sensitive parameter affecting the eco-based maintenance decision outcome. Consistent methods or metrics for lab and field pavement texture evaluation are lacking. TPIN prediction based on pavement structural and material characteristics is not yet available. This paper used 3D point cloud data scanned from specimens and road pavement to conduct correlation and clustering analysis based on representative 3D texture metrics. We conducted an influence analysis to exclude macroscope pavement detection metrics and macro deformation metrics’ effects (international roughness index, IRI, and mean profile depth, MPD). The cluster analysis results verified the feasibility of texture metrics for evaluating lab and field pavement wear, differentiating the wear states. TPIN prediction accuracy based on texture indicators was high (R 2 = 0.9958), implying that it is feasible to predict the TPIN level using 3D texture metrics. The effects of pavement texture changes on TPIN can be simulated by laboratory wear.

Suggested Citation

  • Hui Wang & Xun Zhang & Shengchuan Jiang, 2022. "A Laboratory and Field Universal Estimation Method for Tire–Pavement Interaction Noise (TPIN) Based on 3D Image Technology," Sustainability, MDPI, vol. 14(19), pages 1-21, September.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:19:p:12066-:d:923738
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    References listed on IDEAS

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    1. Zhiyuan Luo & Hui Wang & Shenglin Li, 2022. "Prediction of International Roughness Index Based on Stacking Fusion Model," Sustainability, MDPI, vol. 14(12), pages 1-13, June.
    2. Rita Kleizienė & Ovidijus Šernas & Audrius Vaitkus & Rūta Simanavičienė, 2019. "Asphalt Pavement Acoustic Performance Model," Sustainability, MDPI, vol. 11(10), pages 1-15, May.
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    Cited by:

    1. Bozhi & Mahmoud Mohamed & Vahid Najafi Moghaddam Gilani & Ayesha Amjad & Mohammed Sh. Majid & Khalid Yahya & Mohamed Salem, 2023. "A Review of Wireless Pavement System Based on the Inductive Power Transfer in Electric Vehicles," Sustainability, MDPI, vol. 15(20), pages 1-20, October.
    2. Faguo Zhou & Huichang Zu & Yang Li & Yanan Song & Junbin Liao & Changshuo Zheng, 2023. "Traffic-Sign-Detection Algorithm Based on SK-EVC-YOLO," Mathematics, MDPI, vol. 11(18), pages 1-12, September.
    3. Aditya Raj & Tarun Sharma & Sandeep Singh & Umesh Sharma & Prashant Sharma & Rajesh Singh & Shubham Sharma & Jatinder Kaur & Harshpreet Kaur & Bashir Salah & Syed Sajid Ullah & Soliman Alkhatib, 2023. "Building a Sustainable Future from Theory to Practice: A Comprehensive PRISMA-Guided Assessment of Compressed Stabilized Earth Blocks (CSEB) for Construction Applications," Sustainability, MDPI, vol. 15(12), pages 1-35, June.

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