A hybrid wavelet-optimally-pruned extreme learning machine model for the estimation of international roughness index of rigid pavements. Issue 3 (23rd February 2022)

• Record Type: Journal Article
• Authors: Kaloop, Mosbeh R.; El-Badawy, Sherif M.; Ahn, Jungkyu; Sim, Hyoung-Bo; Hu, Jong Wan; Abd El-Hakim, Ragaa T.
• Is Part Of: International journal of pavement engineering. Volume 23:Issue 3(2022)
• Page Start: 862
• Page End: 876
• Publication Date: 2022-02-23
• Subjects: International roughness index -- rigid pavement -- machine learning -- wavelet -- ANN -- LTPP; Pavements -- Design and construction -- Periodicals; Highway engineering -- Periodicals; 625.805
• DOI: 10.1080/10298436.2020.1776281
• Languages: English
• ISSNs: 1029-8436

Abstract

ABSTRACT: International Roughness Index (IRI) is a key parameter in pavement performance evaluation. This study investigates developing a reliable prediction model that can be used to estimate IRI of rigid pavements using innovative machine learning techniques. Optimally Pruned Extreme Learning Machine (OP-ELM) and Wavelet analysis are integrated to improve the OP-ELM results and design a novel hybrid Wavelet-OPELM (WOPELM) model for the IRI prediction. The proposed model is compared statistically to the OP-ELM and conventional feed-forward Artificial Neural Network (ANN) as well as regression model with respect to their efficiency to predict IRI of jointed plain concrete pavement (JPCP) sections in USA. The relevant data was collected from the Long-Term Pavement Performance (LTPP) database. Eight input variables, initial IRI, pavement age, transverse cracks, percent joints spalled, flexible and rigid patching areas, total joint faulting, freeze index, and percent subgrade passing No. 200 U.S. sieve, are assessed and used to predict the IRI. The results show that the initial IRI, total joint faulting, and freezing index are the most significant parameters for IRI prediction. The WOPELM is found to be a robust and more accurate modelling technique compared to OP-ELM, ANN, and regression for IRI prediction with only a 7% prediction error.