Improved BP Neural Network Ensemble Model for Asphalt Pavement Performance Prediction

With the growing complexity and variability of the operational environment of asphalt pavements and the continuous increase in traffic loads, traditional pavement performance prediction models cannot accurately depict the nonlinear degradation process of pavement performance with the passage of time. Therefore, a novel approach is proposed in this paper to forecast the service performance of asphalt pavements accurately. This method optimises a Backpropagation (BP) neural network using the Levenberg-Marquardt (LM) algorithm. Seven main influencing factors were selected as the input parameters to construct the prediction model, and the performance of the prediction model was evaluated. The parameters considered in this analysis are: road age, average daily traffic volume for one year, annual temperature range, annual precipitation, relative humidity, pavement thickness and pavement surface compressive strength. Through these factors cumulatively, the model is able to predict and evaluate the road condition and Pavement Quality Index (PQI) accurately. The results indicate that the proposed model is better than the baseline model of traditional BP neural networks in predicting the Road Condition Index (RCI), with a Mean Absolute Error (MAE) of 0.395. This is an important reference to predict the service performance of asphalt pavements and validate the effectiveness of the model.