Prediction of the skid-resistance deterioration in asphalt pavement based on peephole–LSTM neural network

Abstract

ABSTRACT Poor pavement skid resistance contributes to frequent traffic accidents, with the asphalt surface rapidly deteriorating over time. Accurate prediction of skid resistance behavior is essential for road safety. This study introduces a pavement accelerated polishing system, collecting data from 11 stages using a British pendulum tester and a high-precision three-dimensional laser scanner. It develops a predictive model, FrictionNet-II, based on a peephole long-short time memory (peephole-LSTM) neural network. The model uses data from the first eight polishing stages as input and the last three as output, allowing for predictions of pavement skid resistance after 160,000, 180,000, and 200,000 cycles based on the friction deterioration curve of the initial stages (0 to 140,000 cycles). FrictionNet-II accurately forecasts skid resistance after 160,000, 180,000, and 200,000 cycles, achieving R2 values of 88%, 90%, and 90%, respectively. This research demonstrates the potential of deep learning in predicting asphalt pavement skid resistance, laying the foundation for intelligent pavement preservation and improved skid-resistance performance.