A novel evaluation method for pavement distress based on impact of ride comfort. Issue 3 (23rd February 2022)

• Record Type: Journal Article
• Authors: Li, Yishun; Liu, Chenglong; Du, Yuchuan; Jiang, Shengchuan
• Is Part Of: International journal of pavement engineering. Volume 23:Issue 3(2022)
• Page Start: 638
• Page End: 650
• Publication Date: 2022-02-23
• Subjects: Pavement distress evaluation -- ride comfort -- data fusion -- convolutional neural network -- wavelet transform; Pavements -- Design and construction -- Periodicals; Highway engineering -- Periodicals; 625.805
• DOI: 10.1080/10298436.2020.1765241
• Languages: English
• ISSNs: 1029-8436

Abstract

ABSTRACT: Assessment of the pavement condition plays a significant role in pavement maintenance and driving comfort enhancement. Current evaluation methods primarily employ manual weights according to the geometric appearance of the distress, which makes it difficult to assess its depth or impact on passengers' experience. This paper proposes a data fusion-based method for pavement distress evaluation, which comprehensively considers the joint effect of distress physical appearance and the corresponding impact on riding comfort. A deep convolutional neural network was employed to automatically detect and locate the pavement distress using image data. A wavelet transform was applied to extract the acceleration effectuated by the defects in the frequency domain using vibration data. Finally, a comfort evaluation index was constructed based on the results of image and vibration data fusion. Furthermore, a mobile vehicle-mounted collective system was designed for rapid evaluation of the pavement distress, which integrated multiple distributed accelerometers, an industrial camera, and a graphics processing unit. The results demonstrated the stability and efficiency of the proposed approach, making it a potential tool to comprehensively evaluate the condition of pavement distress.