Research on intelligent prediction method of dynamic deformation modulus of roadbed

Jincheng He; Yingming Liang; Hongzhao Li; Qingsong Che; Wenjun Zhang; Guangbo Liu

doi:10.1117/12.3054828

20 December 2024 Research on intelligent prediction method of dynamic deformation modulus of roadbed

Jincheng He, Yingming Liang, Hongzhao Li, Qingsong Che, Wenjun Zhang, Guangbo Liu

Author Affiliations +

Proceedings Volume 13421, Eighth International Conference on Traffic Engineering and Transportation System (ICTETS 2024); 1342145 (2024) https://doi.org/10.1117/12.3054828
Event: Eighth International Conference on Traffic Engineering and Transportation System (ICTETS 2024), 2024, Dalian, China

Abstract

When collecting the CMV value of continuous testing results of roadbed compaction quality, due to the influence of the driving speed and vibration frequency of the roller, the CMV value will produce a certain collection error. In this paper, taking the rolling frequency, rolling speed, rolling mode and CMV as input parameters, and the dynamic deformation modulus Evd value as output parameter, the EVD prediction model was established by linear regression analysis and random forest regression, respectively, and the prediction performance was evaluated. The prediction performance was evaluated. The results showed that the correlation coefficient (R2) in the linear regression analysis for predicting Evd using CMV values alone was only 0.592, which was a poor fitting effect. In the random forest regression model for predicting Evd using CMV value alone, the test set correlation coefficient (R2) was 0.782 and the mean absolute error (MAE) was 3.619. In the random forest regression model for predicting Evd using four covariates, namely, milling frequency, milling speed, milling mode, and CMV value, the test set correlation coefficient (R2) was 0.894 and the mean absolute error (MAE) is 1.713, indicating that the random forest regression model is more effective in predicting EVD using the four covariates of milling frequency, milling speed, milling mode and CMV value. The training results have a certain guiding effect on the quality control of roadbed compaction stage.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Jincheng He, Yingming Liang, Hongzhao Li, Qingsong Che, Wenjun Zhang, and Guangbo Liu "Research on intelligent prediction method of dynamic deformation modulus of roadbed", Proc. SPIE 13421, Eighth International Conference on Traffic Engineering and Transportation System (ICTETS 2024), 1342145 (20 December 2024); https://doi.org/10.1117/12.3054828

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KEYWORDS

Vibration

Data modeling

Random forests

Correlation coefficients

Deformation

Education and training

Linear regression

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