东南大学学报（自然科学版）2024，Vol.54Issue(2)：416-422,7.DOI:10.3969/j.issn.1001-0505.2024.02.019
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
Performance prediction of asphalt pavement based on PSO-entropy weighted unbiased grey Markov model
摘要
Abstract
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.关键词
沥青道面/性能预测/粒子群算法/灰色模型/马尔可夫模型Key words
asphalt pavement/performance prediction/particle swarm algorithm/grey model/Markov model分类
交通工程引用本文复制引用
李岩,张久鹏,何印章,赵晓康,张子轩..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[J].东南大学学报（自然科学版）,2024,54(2):416-422,7.基金项目
国家自然科学基金面上资助项目(51978068)、陕西省自然科学基金面上资助项目(2020JM-217). （51978068）
