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.

摘要

国际粗糙度指数（IRI）是路面性能评估的关键参数。本研究旨在开发一种可靠的预测模型，该模型可用于使用创新的机器学习技术估计刚性路面的 IRI。优化修剪极限学习机 (OP-ELM) 和小波分析相结合，以改进 OP-ELM 结果，并为 IRI 预测设计了一种新颖的混合小波-OPELM (WOPELM) 模型。所提出的模型在统计上与 OP-ELM 和传统的前馈人工神经网络 (ANN) 以及回归模型在预测美国接缝普通混凝土路面 (JPCP) 路段 IRI 的效率方面进行了比较。相关数据来自长期路面性能（LTPP）数据库。八个输入变量，初始 IRI，路面年龄，横向裂缝、接头剥落百分比、柔性和刚性修补区域、总接头断层、冻结指数和路基通过 200 号美国筛分的百分比被评估并用于预测 IRI。结果表明，初始IRI、总节理断层和冻结指数是IRI预测的最重要参数。与 OP-ELM、ANN 和 IRI 预测回归相比，WOPELM 被发现是一种稳健且更准确的建模技术，预测误差仅为 7%。