International roughness index prediction based on multigranularity fuzzy time series and particle swarm optimization. (July 2019)

• Record Type: Journal Article
• Authors: Li, Wei; Huyan, Ju; Xiao, Liyang; Tighe, Susan; Pei, Lili
• Is Part Of: Expert systems with applications. Volume 2(2019)
• Publication Date: 2019-07
• Subjects: International roughness index -- Fuzzy time series -- Multigranularity -- Automatic clustering -- Particle swarm optimization; 006.33
• DOI: 10.1016/j.eswax.2019.100006
• Languages: English
• ISSNs: 2590-1885

Abstract

Highlights: Based on fuzzy trend forecasting, automatic clustering and PSO techniques. IRI is regarded as fuzzy time series, which are divided into different granular spaces. IRI prediction in this research can utilize multiple factors. PSO technique is employed incorporating with multi-granularity analysis. Abstract: The effective prediction of pavement performance trends can help in achieving the cost-effective management of pavements over their service life. The international roughness index (IRI) is a widely used pavement performance index, which can be considered as a time-dependent variable in terms of scientific modeling. This research aims to develop an innovative IRI prediction model based on fuzzy-trend time-series forecasting and particle swarm optimization (PSO) techniques. Raw datasets extracted from the Long-Term Pavement Performance database are used for model training, testing, and performance assessment. First, IRI values are divided into different granular spaces, which are considered as the principal factor and subfactors. In addition, the multifactor interval division method is proposed according to the principle of the automatic clustering technique. Next, a second-order fuzzy-trend model and fuzzy-trend relationship classification method are proposed to predict the fuzzy-trend of each factor. Then, the fuzzy-trend states for multiple granular spaces are generated while giving full consideration to various uncertainties. Finally, the PSO technique is used to optimize the performance model while carrying out future IRI forecasting. Comparative experiments are performed using more than 20, 000 data items from different regions to verify the effectiveness of the proposed method. The experimental results indicate that the proposed method outperforms other approaches including the polynomial fitting, autoregressive integrated moving average, and backpropagation neural network methods in terms of the root mean squared error (0.191) and relative error (6.37%).