Evaluating the Tire/Pavement Noise and Surface Texture of Low-Noise Micro-Surface Using 3D Digital Image Technology Article Swipe
Wang Chen
,
Mulian Zheng
,
Haiyang Wang
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.3389/fmats.2021.683947
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.3389/fmats.2021.683947
As a common preventive maintenance technique for asphalt pavement, micro-surface (MS) has the advantages of waterproofing and crack sealing. However, issues such as the fact that the conventional MS generates large noise and the evaluation of the indexes of tire-road noise are relatively less studied. The traditional surface texture index cannot reveal the range and distribution of pavement surface texture, thus hindering research of low-noise MS. To study the mechanism of tire-road noise generated by MS, and propose the tire-road noise and surface texture indicators for MS. In this study, the mechanism of five low-noise MS was systematically analyzed and compared through surface texture and noise tests. Then, a three-dimensional digital texture model (3D-DTM) of MS surface texture was constructed using a series of digital image processing techniques, including grayscale identification, binary conversion, and noise reduction. The results show that optimizing the gradation, adding sound-absorbing materials, and improving the workability of construction can improve the noise reduction performance of MS, it is worth mentioning that the MS prepared with sound-absorbing materials and low-noise gradation has the greatest noise reduction effect, with a maximum reduction of 6.3 dB(A). In addition, it was also found that the 3D-DTM can well reflect the surface texture characteristics of MS. The probability of convex peak distribution (PCD) and the proportion of convex peak area (PCA) with peak heights greater than 0.25 mm ( K h ≥ 0.25 ), which are extracted from the 3D-DTM, can well reflect the surface texture, tire-road noise, respectively. The results show that the 3D-DTM is a promising tool to optimize the design of low-noise MS.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3389/fmats.2021.683947
- https://www.frontiersin.org/articles/10.3389/fmats.2021.683947/pdf
- OA Status
- gold
- Cited By
- 11
- References
- 29
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3182964391
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3182964391Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3389/fmats.2021.683947Digital Object Identifier
- Title
-
Evaluating the Tire/Pavement Noise and Surface Texture of Low-Noise Micro-Surface Using 3D Digital Image TechnologyWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-07-06Full publication date if available
- Authors
-
Wang Chen, Mulian Zheng, Haiyang WangList of authors in order
- Landing page
-
https://doi.org/10.3389/fmats.2021.683947Publisher landing page
- PDF URL
-
https://www.frontiersin.org/articles/10.3389/fmats.2021.683947/pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://www.frontiersin.org/articles/10.3389/fmats.2021.683947/pdfDirect OA link when available
- Concepts
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Gradation, Noise (video), Noise reduction, Road surface, Texture (cosmology), Image noise, Materials science, Computer science, Acoustics, Artificial intelligence, Image (mathematics), Composite material, PhysicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
11Total citation count in OpenAlex
- Citations by year (recent)
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2025: 3, 2024: 2, 2023: 5, 2022: 1Per-year citation counts (last 5 years)
- References (count)
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29Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.MS. | 65, 86, 204, 264 |
| abstract_inverted_index.The | 45, 136, 205, 248 |
| abstract_inverted_index.and | 16, 32, 54, 76, 81, 99, 104, 133, 146, 171, 212 |
| abstract_inverted_index.are | 41, 234 |
| abstract_inverted_index.can | 152, 196, 239 |
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| abstract_inverted_index.≥ | 230 |
| abstract_inverted_index.(MS) | 10 |
| abstract_inverted_index.0.25 | 225, 231 |
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| abstract_inverted_index.area | 218 |
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| abstract_inverted_index.well | 197, 240 |
| abstract_inverted_index.with | 168, 180, 220 |
| abstract_inverted_index.(PCA) | 219 |
| abstract_inverted_index.(PCD) | 211 |
| abstract_inverted_index.Then, | 107 |
| abstract_inverted_index.crack | 17 |
| abstract_inverted_index.found | 192 |
| abstract_inverted_index.image | 125 |
| abstract_inverted_index.index | 49 |
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| abstract_inverted_index.model | 112 |
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| abstract_inverted_index.3D-DTM | 195, 253 |
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| abstract_inverted_index.binary | 131 |
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| abstract_inverted_index.common | 2 |
| abstract_inverted_index.convex | 208, 216 |
| abstract_inverted_index.dB(A). | 186 |
| abstract_inverted_index.design | 261 |
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| abstract_inverted_index.series | 122 |
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| abstract_inverted_index.tests. | 106 |
| abstract_inverted_index.3D-DTM, | 238 |
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| abstract_inverted_index.heights | 222 |
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| abstract_inverted_index.indexes | 37 |
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| abstract_inverted_index.results | 137, 249 |
| abstract_inverted_index.surface | 47, 58, 82, 102, 116, 200, 243 |
| abstract_inverted_index.texture | 48, 83, 103, 111, 117, 201 |
| abstract_inverted_index.through | 101 |
| abstract_inverted_index.(3D-DTM) | 113 |
| abstract_inverted_index.However, | 19 |
| abstract_inverted_index.analyzed | 98 |
| abstract_inverted_index.compared | 100 |
| abstract_inverted_index.greatest | 176 |
| abstract_inverted_index.optimize | 259 |
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| abstract_inverted_index.prepared | 167 |
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| abstract_inverted_index.sealing. | 18 |
| abstract_inverted_index.studied. | 44 |
| abstract_inverted_index.texture, | 59, 244 |
| abstract_inverted_index.addition, | 188 |
| abstract_inverted_index.extracted | 235 |
| abstract_inverted_index.generated | 73 |
| abstract_inverted_index.generates | 29 |
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| abstract_inverted_index.improving | 147 |
| abstract_inverted_index.including | 128 |
| abstract_inverted_index.low-noise | 64, 94, 172, 263 |
| abstract_inverted_index.materials | 170 |
| abstract_inverted_index.mechanism | 69, 91 |
| abstract_inverted_index.pavement, | 8 |
| abstract_inverted_index.promising | 256 |
| abstract_inverted_index.reduction | 156, 178, 183 |
| abstract_inverted_index.technique | 5 |
| abstract_inverted_index.tire-road | 39, 71, 79, 245 |
| abstract_inverted_index.advantages | 13 |
| abstract_inverted_index.evaluation | 34 |
| abstract_inverted_index.gradation, | 142 |
| abstract_inverted_index.indicators | 84 |
| abstract_inverted_index.materials, | 145 |
| abstract_inverted_index.mentioning | 163 |
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| abstract_inverted_index.preventive | 3 |
| abstract_inverted_index.processing | 126 |
| abstract_inverted_index.proportion | 214 |
| abstract_inverted_index.reduction. | 135 |
| abstract_inverted_index.relatively | 42 |
| abstract_inverted_index.constructed | 119 |
| abstract_inverted_index.conversion, | 132 |
| abstract_inverted_index.maintenance | 4 |
| abstract_inverted_index.performance | 157 |
| abstract_inverted_index.probability | 206 |
| abstract_inverted_index.techniques, | 127 |
| abstract_inverted_index.traditional | 46 |
| abstract_inverted_index.workability | 149 |
| abstract_inverted_index.construction | 151 |
| abstract_inverted_index.conventional | 27 |
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| abstract_inverted_index.micro-surface | 9 |
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| abstract_inverted_index.waterproofing | 15 |
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| abstract_inverted_index.characteristics | 202 |
| abstract_inverted_index.identification, | 130 |
| abstract_inverted_index.sound-absorbing | 144, 169 |
| abstract_inverted_index.three-dimensional | 109 |
| cited_by_percentile_year.max | 98 |
| cited_by_percentile_year.min | 89 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 3 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/11 |
| sustainable_development_goals[0].score | 0.5600000023841858 |
| sustainable_development_goals[0].display_name | Sustainable cities and communities |
| citation_normalized_percentile.value | 0.74319196 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |