基于粒子群优化-熵权无偏灰色马尔可夫模型的沥青道面性能预测OA北大核心CSTPCD

To improve the prediction accuracy of asphalt pavement performance,multiple pavement units were weighted by the entropy value for the characteristics of pavement performance data with few years of accumu-lation and large fluctuations.The particle swarm optimization(PSO)algorithm and Markov model were used to optimize the state intervals and whitening coefficients of the residual sequences of the conventional unbiased grey model.The PSO-entropy weighted unbiased grey Markov model for asphalt pavement performance pre-diction was constructed.The model accuracy was tested by combining the previous 7 years'pavement condi-tion index from 6 inspection units of asphalt pavement at an airport in Northwest China.The results show that compared with the traditional unbiased grey model,the residuals and annual relative errors of each unit in the first five years of the optimized model decrease using the Markov model to divide the residual series space and applying the PSO algorithm to find the best whitening function.The overall forecast accuracies of the 1st year to the 5th year increase by 0.05％,0.28％,0.05％,0.03％and 0.14％,respectively,and those of the sixth and seventh years increase by 12.9％and 19.2％,respectively.The optimized model is more effective and relevant in predicting the actual asphalt pavement.