Characterisation of microstructural and sound absorption properties of porous asphalt subjected to progressive clogging

• Main Author: Putra Jaya, Ramadhansyah
• Format: Article
• Language: English
• Published: Elsevier Ltd. 2021
• Subjects: TE Highway engineering. Roads and pavements
• Online Access: http://umpir.ump.edu.my/id/eprint/30868/1/Characterisation%20of%20microstructural%20and%20sound%20absorption%20properties.pdf; http://umpir.ump.edu.my/id/eprint/30868/; https://doi.org/10.1016/j.conbuildmat.2021.122654
• Institution: Universiti Malaysia Pahang

Road traffic noise is a significant environmental issue. Porous asphalt is often used to mitigate this prob�lem. However, the clogging issue is one of the main challenges of using porous asphalt. Clogging leads to a significant reduction in permeability and sound absorption as the pavement aged. This study measures the changes in the microstructural properties and sound absorption with multiple clogging cycles via X-ray Computed Tomography scanning. The identification of the presence of clogging particles in the X-ray images is also one of the challenges due to the complexity of image segmentation. Clogging simu�lation tests are performed on the compacted samples at a concentration of 1.0 g/l and repeated for five clogging cycles. The microstructural analysis shows that severe clogging densification occurred at the top section of the porous asphalt (approximately one-third of the sample height), thereby leading to alteration in the physical void structure and void properties (i.e., percentage, number, and size). Accordingly, the peak of the sound absorption coefficient observed at 800 Hz under an initial condition shifted to a new peak of 630 Hz after severe clogging. Low-frequency sound wave energy has a low atten�uation energy that can penetrate the clogged surface given the correlation between the sound absorption coefficient and the air void properties. This study also recommends the use of Non-local mean filter to minimise the effect of white noise (equal signal intensity or density) and improve the accuracy of image segmentation.