Pavement Distress Initiation Prediction by Time-Lag Analysis and Logistic Regression
Pavement condition prediction plays a vital role in pavement maintenance. Many prediction models and analyses have been conducted based on long-term pavement condition data. However, the condition evaluation for road sections can hardly support daily routine maintenance. This paper uses high-frequen...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-11-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/12/22/11855 |
| _version_ | 1827644978342395904 |
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| author | Hao Liu Yishun Li Chenglong Liu Guohong Shen Hui Xiang |
| author_facet | Hao Liu Yishun Li Chenglong Liu Guohong Shen Hui Xiang |
| author_sort | Hao Liu |
| collection | DOAJ |
| description | Pavement condition prediction plays a vital role in pavement maintenance. Many prediction models and analyses have been conducted based on long-term pavement condition data. However, the condition evaluation for road sections can hardly support daily routine maintenance. This paper uses high-frequency pavement distress data to explore the relationship between distress initiation, weather, and geometric factors. Firstly, a framework is designed to extract the initial time of pavement distress. Weather and geometric data are integrated to establish a pavement distress initiation dataset. Then, the time-lag cross-correlation analysis methods were utilized to explore the relationship between distress initiation and environmental factors. In addition, the logistic regression model is used to establish the distress initiation prediction model. Finally, Akaike information criterion (AIC), Bayesian information criterions (BIC), and areas under receiver operating characteristic curves (AUC) of logistic regression models with or without time-lag variables are compared as performance measurements. The results show that pavement distress initiation is susceptible to weather factors and location relationships. Daily total precipitation, minimum temperature, and daily average temperature have a time delay effect on the initiation of the pavement distress. Distress initiation is negatively correlated with the distance from the nearby intersection and positively correlated with adjacent distresses. The weather factors, considering the time-lag effect, can improve the model performance of the distress initiation prediction model and provide support for emergency management after severe weather. |
| first_indexed | 2024-03-09T18:29:04Z |
| format | Article |
| id | doaj.art-25b5695283a64f93a34a25e013fafd4b |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T18:29:04Z |
| publishDate | 2022-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-25b5695283a64f93a34a25e013fafd4b2023-11-24T07:42:00ZengMDPI AGApplied Sciences2076-34172022-11-0112221185510.3390/app122211855Pavement Distress Initiation Prediction by Time-Lag Analysis and Logistic RegressionHao Liu0Yishun Li1Chenglong Liu2Guohong Shen3Hui Xiang4Key Laboratory of Road and Traffic Engineering of the Ministry of Education, Tongji University, 4800 Cao’an Road, Shanghai 201804, ChinaKey Laboratory of Road and Traffic Engineering of the Ministry of Education, Tongji University, 4800 Cao’an Road, Shanghai 201804, ChinaKey Laboratory of Road and Traffic Engineering of the Ministry of Education, Tongji University, 4800 Cao’an Road, Shanghai 201804, ChinaThe Digital Industry Group of Shanghai Urban Construction Corporation, Shanghai 200122, ChinaChina State Construction Railway Investment & Engineering Group Co., Ltd., Beijing 102600, ChinaPavement condition prediction plays a vital role in pavement maintenance. Many prediction models and analyses have been conducted based on long-term pavement condition data. However, the condition evaluation for road sections can hardly support daily routine maintenance. This paper uses high-frequency pavement distress data to explore the relationship between distress initiation, weather, and geometric factors. Firstly, a framework is designed to extract the initial time of pavement distress. Weather and geometric data are integrated to establish a pavement distress initiation dataset. Then, the time-lag cross-correlation analysis methods were utilized to explore the relationship between distress initiation and environmental factors. In addition, the logistic regression model is used to establish the distress initiation prediction model. Finally, Akaike information criterion (AIC), Bayesian information criterions (BIC), and areas under receiver operating characteristic curves (AUC) of logistic regression models with or without time-lag variables are compared as performance measurements. The results show that pavement distress initiation is susceptible to weather factors and location relationships. Daily total precipitation, minimum temperature, and daily average temperature have a time delay effect on the initiation of the pavement distress. Distress initiation is negatively correlated with the distance from the nearby intersection and positively correlated with adjacent distresses. The weather factors, considering the time-lag effect, can improve the model performance of the distress initiation prediction model and provide support for emergency management after severe weather.https://www.mdpi.com/2076-3417/12/22/11855pavement distress initiation predictiontime-lag cross-correlationlogistic regression |
| spellingShingle | Hao Liu Yishun Li Chenglong Liu Guohong Shen Hui Xiang Pavement Distress Initiation Prediction by Time-Lag Analysis and Logistic Regression Applied Sciences pavement distress initiation prediction time-lag cross-correlation logistic regression |
| title | Pavement Distress Initiation Prediction by Time-Lag Analysis and Logistic Regression |
| title_full | Pavement Distress Initiation Prediction by Time-Lag Analysis and Logistic Regression |
| title_fullStr | Pavement Distress Initiation Prediction by Time-Lag Analysis and Logistic Regression |
| title_full_unstemmed | Pavement Distress Initiation Prediction by Time-Lag Analysis and Logistic Regression |
| title_short | Pavement Distress Initiation Prediction by Time-Lag Analysis and Logistic Regression |
| title_sort | pavement distress initiation prediction by time lag analysis and logistic regression |
| topic | pavement distress initiation prediction time-lag cross-correlation logistic regression |
| url | https://www.mdpi.com/2076-3417/12/22/11855 |
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