Bayesian optimization based extreme gradient boosting and GPR time-frequency features for the recognition of moisture damage in asphalt pavement

Moisture damage is one of the major defects in asphalt pavement, and will evolve into potholes in a short time which will affect traffic safety. Ground Penetrating Radar (GPR) is an effective non-destructive testing (NDT) method for detecting moisture damage but its data explanation replies on human...

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Vydáno v:	Construction & building materials Ročník 434; s. 136675
Hlavní autoři:	Li, Hongwei, Zhang, Jun, Yang, Xiaokun, Ye, Min, Jiang, Wentao, Gong, Jing, Tian, Yaogang, Zhao, Liang, Wang, Weitian, Xu, Zhi
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Elsevier Ltd 05.07.2024
Témata:	Asphalt pavement Bayesian optimization Extreme gradient boosting (XGBoost) Ground penetrating radar (GPR) Moisture damage Time-frequency feature Time-frequency feature Extreme gradient boosting (XGBoost) Ground penetrating radar (GPR) Moisture damage Asphalt pavement Bayesian optimization
ISSN:	0950-0618, 1879-0526
On-line přístup:	Získat plný text
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