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Li, Huixia, Nyirandayisabye, Ritha, Dong, Qiming, Niyirora, Rosette, Hakuzweyezu, Theogene, Zardari, Irshad Ali, Nkinahamira, François (2024) Crack damage prediction of asphalt pavement based on tire noise: A comparison of machine learning algorithms. Construction and Building Materials, 414. 134867 doi:10.1016/j.conbuildmat.2024.134867

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Reference TypeJournal (article/letter/editorial)
TitleCrack damage prediction of asphalt pavement based on tire noise: A comparison of machine learning algorithms
JournalConstruction and Building Materials
AuthorsLi, HuixiaAuthor
Nyirandayisabye, RithaAuthor
Dong, QimingAuthor
Niyirora, RosetteAuthor
Hakuzweyezu, TheogeneAuthor
Zardari, Irshad AliAuthor
Nkinahamira, FrançoisAuthor
Year2024 (February)Volume414
PublisherElsevier BV
DOIdoi:10.1016/j.conbuildmat.2024.134867Search in ResearchGate
Generate Citation Formats
Mindat Ref. ID17156941Long-form Identifiermindat:1:5:17156941:6
GUID0
Full ReferenceLi, Huixia, Nyirandayisabye, Ritha, Dong, Qiming, Niyirora, Rosette, Hakuzweyezu, Theogene, Zardari, Irshad Ali, Nkinahamira, François (2024) Crack damage prediction of asphalt pavement based on tire noise: A comparison of machine learning algorithms. Construction and Building Materials, 414. 134867 doi:10.1016/j.conbuildmat.2024.134867
Plain TextLi, Huixia, Nyirandayisabye, Ritha, Dong, Qiming, Niyirora, Rosette, Hakuzweyezu, Theogene, Zardari, Irshad Ali, Nkinahamira, François (2024) Crack damage prediction of asphalt pavement based on tire noise: A comparison of machine learning algorithms. Construction and Building Materials, 414. 134867 doi:10.1016/j.conbuildmat.2024.134867
In(2024) Construction and Building Materials Vol. 414. Elsevier BV

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Chen, De, Ling, Cheng, Wang, Tingting, Su, Qian, Ye, Anjun (2018) Prediction of tire-pavement noise of porous asphalt mixture based on mixture surface texture level and distributions. Construction and Building Materials, 173. 801-810 doi:10.1016/j.conbuildmat.2018.04.062
Dong, Shihao, Han, Sen, Luo, Youdong, Han, Xiao, Xu, Ouming (2021) Evaluation of tire-pavement noise based on three-dimensional pavement texture characteristics. Construction and Building Materials, 306. 124935pp. doi:10.1016/j.conbuildmat.2021.124935
Dan, Han-Cheng, Lu, Bingjie, Li, Mengyu (2024) Evaluation of asphalt pavement texture using multiview stereo reconstruction based on deep learning. Construction and Building Materials, 412. 134837 doi:10.1016/j.conbuildmat.2023.134837
Guo, Xiaogang, Wang, Ning, Li, Yan (2023) Enhancing pavement maintenance: A deep learning model for accurate prediction and early detection of pavement structural damage. Construction and Building Materials, 409. 133970 doi:10.1016/j.conbuildmat.2023.133970
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Huang, Sheng, Wang, Li, Li, Zaoyuan, Su, Donghua, Luo, Qianmei (2024) Machine learning-based prediction model for CO2-induced corrosion on oil well cement under high-pressure and high-temperature condition. Construction and Building Materials, 414. 134999 doi:10.1016/j.conbuildmat.2024.134999
Arshad, Muhammad, Hamza, Hafiz Muhammad (2025) Applications of machine learning and traditional prediction techniques for estimating resilient modulus values of unbound granular materials incorporating reclaimed asphalt pavement (RAP) as a primary component. Construction and Building Materials, 473. doi:10.1016/j.conbuildmat.2025.140900
Chen, Anqi, Zhao, Yongli, Li, Pengbo, Li, Yuanyuan, Mohammed, Monketh, Guo, Peng (2020) Crack propagation prediction of asphalt pavement after maintenance as a function of initial cracks distribution. Construction and Building Materials, 231. 117157pp. doi:10.1016/j.conbuildmat.2019.117157
 
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