Roughness prediction of asphalt pavement using FGM(1,1-sin) model optimized by swarm intelligence and Markov chain.

Abstract

The road traffic volumes are constantly increasing worldwide, leading to significant challenges in maintaining asphalt pavements. Vehicular loads and environmental changes impact asphalt pavements, necessitating suitable predictive models. The International Roughness Index (IRI), a key indicator of road smoothness, requires IRI prediction models for performance analysis. Using the fractional accumulation operator and sine term can improve the traditional grey model's low prediction accuracy. Then, the chaotic adaptive whale optimization algorithm and Markov chain are used to optimize the model. Based on the different asphalt pavement structures used by RIOHtrack as data for the experiments, the average RMSE, MAE, and MAPE reached 0.025, 0.020, and 1.392%, respectively. Compared with other grey models, it performs better in IRI multi-step prediction. Particularly, the proposed model can achieve compelling predictions in a small sample size only through the changes in IRI itself, which helps to evaluate road conditions and design maintenance plans.