Prediction modelling of rutting depth index for asphalt pavement using de-noising method. Issue Volume 21:Issued 7(2020) (6th June 2020)

• Record Type: Journal Article
• Authors: Fang, Mingjing; Han, Chengjia; Xiao, Yue; Han, Zhongzhao; Wu, Shaopeng; Cheng, Meng
• Is Part Of: International journal of pavement engineering. Volume 21:Issued 7(2020)
• Page Start: 895
• Page End: 907
• Publication Date: 2020-06-06
• Subjects: Wavelet analysis -- de-noising -- time series analysis -- rutting depth index -- prediction model; Pavements -- Design and construction -- Periodicals; Highway engineering -- Periodicals; 625.805
• DOI: 10.1080/10298436.2018.1512712
• Languages: English
• ISSNs: 1029-8436

Abstract

ABSTRACT: Intelligent maintenance of pavement requires correct laws of distresses growing. In order to reduce the influence of frequent maintenance on revealing the law of rutting in asphalt pavement, a predictive analysis for rutting depth index (RDI) of asphalt pavement has been conducted via establishing the wavelet-time series prediction model (W-ARMA). The wavelet analysis is firstly used to de-noise the measured RDI data, which can filter out artificial-repairing-caused noise signals that do not vary continuously over time. Then, the rutting evolvement law can be revealed according to the autoregressive moving average (ARMA) model in time series analysis. The results showed that (i) The method of wavelet de-noising to preprocess measured RDI value can reduce the impact of noise signals on rutting predictions; (ii) The improved time series prediction methods are available when applied to rutting analysis based on small size samples (iii) W-ARMA model can directly apply the measured data from real projects, thus simplifying the predictive processing; (iv) The classification of RDI noise signals is given to divide noise factors into the time- and space-based noise; (v) The effective information must be kept during de-noising processing due to the existence of the excessively de-noised or ignored.