@article{Si2023, 
author = {Wei Si and Weijie Mao and Yan Shi and  Ci-dan-duo-jie and Tianjun Yang},
title = {Research on Asphalt Pavement Construction Temperature Control Model Based on Feedback and Control Theory},
year = {2023},
journal = {Journal of Highway and Transportation Research and Development (English Edition)},
volume = {17},
number = {3},
pages = {1-15},
keywords = {road engineering, control model, feedback theory, construction technology, construction temperature},
url = {https://www.sciopen.com/article/10.1061/JHTRCQ.0000869},
doi = {10.1061/JHTRCQ.0000869},
abstract = {Based on data preprocessing, digital analysis of the asphalt pavement construction process was conducted, and important construction processes and meteorological parameters affecting construction temperature were screened using the random forest (RF) algorithm. Based on the selected important parameters, the construction temperature prediction model was established by multi-layer perception (MLP). Based on feedback control theory, the control principle of the PID controller was analyzed, and a comprehensive and multi-stage temperature feedback control model was constructed in conjunction with the construction temperature prediction model. In order to solve the problem that the super-parameter cannot be self-tuning, the genetic algorithm (GA) is used to optimize the feedback control model to make the model adaptive. The feedback control model's construction process decision was compared and analyzed with the actual construction process parameters, and the robustness of the model's feedback control results was evaluated to effectively adjust the construction process parameters and achieve precise control of asphalt mixture construction temperature. The research results showed that the construction temperature prediction model could accurately predict the construction temperature. The comprehensive feedback control model could feedback control all parameters, while the multi-stage feedback control model could maintain the determined parameters and feedback control other parameters. In addition, the GA-PID feedback control model based on genetic algorithms had the performance of adaptive tuning of hyperparameters. Through analysis of the feedback control results, it was found that the construction process parameters obtained from the GA-PID feedback control model were evenly distributed within the effective range of actual process parameters, maintaining good consistency with the actual construction process parameters. The GA-PID control system had good robustness for different prediction models and different temperature control, and the proposed construction process decision was consistent with actual situations.}
}