Crack damage prediction of asphalt pavement based on tire noise: A comparison of machine learning algorithms
文献类型：期刊论文
| 作者 | Li, Huixia2; Nyirandayisabye, Ritha2; Dong, Qiming2; Niyirora, Rosette3; Hakuzweyezu, Theogene4,5; Zardari, Irshad Ali1; Nkinahamira, Francois6 |
| 刊名 | CONSTRUCTION AND BUILDING MATERIALS
 |
| 出版日期 | 2024-02-02 |
| 卷号 | 414页码:13 |
| 关键词 | Road surface damage Machine learning AdaBoost classifier Tire noise Noise reduction Stacking classifier |
| ISSN号 | 0950-0618 |
| DOI | 10.1016/j.conbuildmat.2024.134867 |
| 英文摘要 | Predicting road pavement damage is a vital aspect of traffic management aimed at decreasing accident rates. Compared with other pavement non-destructive testing methods, using tire noise for testing has the advantages of low cost and convenient detection. This study introduces a machine learning (ML) algorithm specifically designed to predict road pavement damage based on tire noise propagation. Five machine learning algorithms, Support Vector Classifier (SVC), Random Forest Classifier (RFC), AdaBoost, Multilayer Perceptron (MLP), and Stacked Classifier were utilized to enhance the accuracy of damage prediction using tire noise. The data for this study was collected from Yuanjiang Road, Fuzhou City, Fujian, China, in September 2022, using a microphone, camera, and GPS to create an audio dataset. This data was then split into training and testing sets to assess the performance of the algorithms. The RFC method proved superior to the other models, demonstrating accuracy, precision, recall, and F1-scores of 99%, 98%, 99%, and 96%, respectively. The findings show that tire noise propagation datasets can be used to detect road damage through various classification prediction models. This approach is reliable, efficient, cost-effective, and highly effective. |
| 资助项目 | Natural Science Foundation Project of Fujian Province[2021J011060] ; Innovation and Entrepreneurship Training Program for College Students in Fujian Province[202010388024] ; Fujian Institute of Technology Research Startup Fund[GY-Z19129] |
| WOS研究方向 | Construction & Building Technology ; Engineering ; Materials Science |
| 语种 | 英语 |
| WOS记录号 | WOS:001166819800001 |
| 出版者 | ELSEVIER SCI LTD |
| 源URL | [http://119.78.100.198/handle/2S6PX9GI/40685]  |
| 专题 | 中科院武汉岩土力学所 |
| 通讯作者 | Li, Huixia; Nyirandayisabye, Ritha |
| 作者单位 | 1.Beijing Jiaotong Univ, Sch Civil Engn, Beijing 100044, Peoples R China 2.Fujian Univ Technol, Sch Civil Engn, Fuzhou 350108, Fujian, Peoples R China 3.Lanzhou Jiao Tong Univ, Sch Civil Engn, Lanzhou 730070, Peoples R China 4.Chinese Acad Sci, Inst Rock & Soil Mech, State Key Lab Geomech & Geotech Engn, Wuhan 430071, Hubei, Peoples R China 5.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 6.Guangzhou Univ, Sch Civil Engn, Guangzhou 510006, Peoples R China |
| 推荐引用方式 GB/T 7714 | Li, Huixia,Nyirandayisabye, Ritha,Dong, Qiming,et al. Crack damage prediction of asphalt pavement based on tire noise: A comparison of machine learning algorithms[J]. CONSTRUCTION AND BUILDING MATERIALS,2024,414:13. |
| APA | Li, Huixia.,Nyirandayisabye, Ritha.,Dong, Qiming.,Niyirora, Rosette.,Hakuzweyezu, Theogene.,...&Nkinahamira, Francois.(2024).Crack damage prediction of asphalt pavement based on tire noise: A comparison of machine learning algorithms.CONSTRUCTION AND BUILDING MATERIALS,414,13. |
| MLA | Li, Huixia,et al."Crack damage prediction of asphalt pavement based on tire noise: A comparison of machine learning algorithms".CONSTRUCTION AND BUILDING MATERIALS 414(2024):13. |
入库方式： OAI收割
来源：武汉岩土力学研究所
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