Prediction of subgrade settlement using PMIGM(1,1) model based on particle swarm optimization and Markov optimization

Accurate prediction of subgrade settlement of expressways is of great significance to their disease prevention and treatment. The previous prediction models for the subgrade settlement are mostly single models or simple improved models. A PMIGM(1,1) prediction model based on the particle swarm optimization (PSO) and Markov optimization is proposed. Firstly, based on the grey theory, an improved GM(1,1) prediction model is put forward. Then, according to the theory knowledge of Markov chains, an MIGM(1, 1) model is built to correct the relative residuals of IGM(1, 1) model, which can reflect the volatility characteristics of the data. Based on the principle of PSO, an optimization of PMIGM(1, 1) model is set up, which crystallizes the parameters of grey interval. The forecasting model is applied to a high-fill subgrade of Baoshan-Shidian Expressway in Yunnan Province. The analysis results show that the proposed model can improve the accuracy of the forecasting model.