Research on Asphalt Pavement Construction Temperature Control Model Based on Feedback and Control Theory

Abstract

Based on data preprocessing, a 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 using multilayer perception (MLP). Based on the feedback control theory, the control principle of the PID controller was analyzed, and a comprehensive and multistage temperature feedback control model was constructed in conjunction with the construction temperature prediction model. In order to solve the problem that the superparameter cannot be self-tuning, the genetic algorithm (GA) was 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 the asphalt mixture’s 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 multistage 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.