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Prediction of highway pavement performance based on combined model
This article focuses on the analysis of the performance of asphalt pavement, and establishes a prediction model for the performance of asphalt pavement in Guidu based on the data of traffic volume, climate, and road surface smoothness. After fully understanding the performance evaluation indicators of various asphalt pavements, the international roughness index is selected as the performance evaluation indicators of asphalt pavements. According to the technical performance of the asphalt mixture (high temperature stability, low temperature crack resistance, water stability, antifatigue performance, anti-aging performance, etc.), the influencing factors (temperature, rainfall days, traffic volume, etc.) of the asphalt pavement performance are derived. Collect traffic flow and asphalt pavement performance data on the spot, and process and analyze the data. Establish a gray forecast model, a moving average forecast model, as well as a multiple regression forecast model and a VAR model that consider the four variables of traffic volume, truck ratio, temperature, and rainfall days. Predict the performance of the asphalt pavement through the above model, and get the prediction result.
Prediction of highway pavement performance based on combined model
This article focuses on the analysis of the performance of asphalt pavement, and establishes a prediction model for the performance of asphalt pavement in Guidu based on the data of traffic volume, climate, and road surface smoothness. After fully understanding the performance evaluation indicators of various asphalt pavements, the international roughness index is selected as the performance evaluation indicators of asphalt pavements. According to the technical performance of the asphalt mixture (high temperature stability, low temperature crack resistance, water stability, antifatigue performance, anti-aging performance, etc.), the influencing factors (temperature, rainfall days, traffic volume, etc.) of the asphalt pavement performance are derived. Collect traffic flow and asphalt pavement performance data on the spot, and process and analyze the data. Establish a gray forecast model, a moving average forecast model, as well as a multiple regression forecast model and a VAR model that consider the four variables of traffic volume, truck ratio, temperature, and rainfall days. Predict the performance of the asphalt pavement through the above model, and get the prediction result.
Prediction of highway pavement performance based on combined model
Liang, Ruishi (editor) / Wang, Jing (editor) / Ren, Guangli (author) / Zhang, Peng (author) / Cui, Yixin (author)
International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2022) ; 2022 ; Guangzhou, China
Proc. SPIE ; 12604
2023-05-23
Conference paper
Electronic Resource
English
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