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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| Vydané v: | Construction & building materials Ročník 434; s. 136675 |
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| Médium: | Journal Article |
| Jazyk: | English |
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Elsevier Ltd
05.07.2024
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| ISSN: | 0950-0618, 1879-0526 |
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| Abstract | 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 experience and subjects to labor intensive. To address this issue, an automatic detection method based on extreme gradient boosting (XGBoost) combined with Bayesian hyper-parameter optimization (BHPO) was proposed to detect moisture damage area from GPR traces. High frequency GPR antenna with 2.3 GHz was used to detect the moisture damage area from simulation, laboratory and field tests, and moisture damage dataset with 7960 traces was collected. Thirty time-frequency parameters were extracted from each GPR trace, normalized to unify the three data source, and then optimized into 12 sensitive parameters by feature importance method. These 12 parameters were used to build the recognition model with XGBoost, and the model tuning parameters were optimized by BHPO. To obtain optimization model, random forest (RF) and artificial neural network (ANN) were also trained with BHPO, and compared with XGBoost model. The results indicate that performance of XGBoost model with BHPO achieves the highest accuracy and lowest time cost both in moisture damage and normal trace classification, the accuracies for moisture damage are XGBoost (96.9%) > ANN (95.6%) > RF (95.4%), respectively, and normal are XGBoost (96.5%) > RF (96.1%) and ANN (96.0%), respectively. On this basis, field tests were conducted by core samples, which verified the correct result of XGBoost model. Our method provides a swift and accurate method to locate subsurface targets directly from GPR signals.
•30 time-frequency features were extracted for representing moisture damage.•12 sensitive features were optimized by feature importance analysis.•XGBoost with BHPO was adopted to locate moisture damage from GPR signal.•Normalizing method was used to combine features from different data source.•BHPO-XGBoost model reaches high accuracy with 96.9%. |
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| AbstractList | 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 experience and subjects to labor intensive. To address this issue, an automatic detection method based on extreme gradient boosting (XGBoost) combined with Bayesian hyper-parameter optimization (BHPO) was proposed to detect moisture damage area from GPR traces. High frequency GPR antenna with 2.3 GHz was used to detect the moisture damage area from simulation, laboratory and field tests, and moisture damage dataset with 7960 traces was collected. Thirty time-frequency parameters were extracted from each GPR trace, normalized to unify the three data source, and then optimized into 12 sensitive parameters by feature importance method. These 12 parameters were used to build the recognition model with XGBoost, and the model tuning parameters were optimized by BHPO. To obtain optimization model, random forest (RF) and artificial neural network (ANN) were also trained with BHPO, and compared with XGBoost model. The results indicate that performance of XGBoost model with BHPO achieves the highest accuracy and lowest time cost both in moisture damage and normal trace classification, the accuracies for moisture damage are XGBoost (96.9%) > ANN (95.6%) > RF (95.4%), respectively, and normal are XGBoost (96.5%) > RF (96.1%) and ANN (96.0%), respectively. On this basis, field tests were conducted by core samples, which verified the correct result of XGBoost model. Our method provides a swift and accurate method to locate subsurface targets directly from GPR signals.
•30 time-frequency features were extracted for representing moisture damage.•12 sensitive features were optimized by feature importance analysis.•XGBoost with BHPO was adopted to locate moisture damage from GPR signal.•Normalizing method was used to combine features from different data source.•BHPO-XGBoost model reaches high accuracy with 96.9%. |
| ArticleNumber | 136675 |
| Author | Wang, Weitian Yang, Xiaokun Tian, Yaogang Gong, Jing Zhao, Liang Li, Hongwei Ye, Min Xu, Zhi Zhang, Jun Jiang, Wentao |
| Author_xml | – sequence: 1 givenname: Hongwei surname: Li fullname: Li, Hongwei organization: Key Laboratory of Road Construction Technology and Equipment of MOE, Chang’an University, Xi’an, Shaanxi 710064, China – sequence: 2 givenname: Jun orcidid: 0000-0001-5594-9203 surname: Zhang fullname: Zhang, Jun email: zhangjun@chd.edu.cn organization: Key Laboratory of Road Construction Technology and Equipment of MOE, Chang’an University, Xi’an, Shaanxi 710064, China – sequence: 3 givenname: Xiaokun surname: Yang fullname: Yang, Xiaokun organization: Key Laboratory of Road Construction Technology and Equipment of MOE, Chang’an University, Xi’an, Shaanxi 710064, China – sequence: 4 givenname: Min orcidid: 0000-0002-8301-5843 surname: Ye fullname: Ye, Min email: mingye@chd.edu.cn organization: Key Laboratory of Road Construction Technology and Equipment of MOE, Chang’an University, Xi’an, Shaanxi 710064, China – sequence: 5 givenname: Wentao surname: Jiang fullname: Jiang, Wentao organization: National Key Laboratory of Strength and Structural Integrity, Aircraft Strength Research Institute of China, Xi’an, Shaanxi 710065, China – sequence: 6 givenname: Jing surname: Gong fullname: Gong, Jing organization: NingBo High-grade Maintenance Co Ltd., Ningbo, Zhejiang 31500, China – sequence: 7 givenname: Yaogang surname: Tian fullname: Tian, Yaogang organization: School of Materials Science and Engineering, Chang’an University, Xi’an 710061, China – sequence: 8 givenname: Liang surname: Zhao fullname: Zhao, Liang organization: School of Mechatronic Engineering, Xi’an Technological University, Xi’an 710021, China – sequence: 9 givenname: Weitian surname: Wang fullname: Wang, Weitian organization: Department of Computer Science, Montclair State University, NJ 07043, USA – sequence: 10 givenname: Zhi surname: Xu fullname: Xu, Zhi organization: Hangzhou Institute of Technology, Xidian University, Hangzhou, Zhejiang Province 311231, China |
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| Keywords | Time-frequency feature Extreme gradient boosting (XGBoost) Ground penetrating radar (GPR) Moisture damage Asphalt pavement Bayesian optimization |
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| SubjectTerms | Asphalt pavement Bayesian optimization Extreme gradient boosting (XGBoost) Ground penetrating radar (GPR) Moisture damage Time-frequency feature |
| Title | Bayesian optimization based extreme gradient boosting and GPR time-frequency features for the recognition of moisture damage in asphalt pavement |
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