Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Smart Structural Health Monitoring of Flexible Pavements Using Machine Learning Methods

Version 1 : Received: 1 April 2020 / Approved: 3 April 2020 / Online: 3 April 2020 (09:35:44 CEST)

How to cite: Karballaeezadeh, N.; Mohammadzadeh S., D.; Moazami, D.; Nabipour, N.; Mosavi, A.; Reuter, U. Smart Structural Health Monitoring of Flexible Pavements Using Machine Learning Methods. Preprints 2020, 2020040029 (doi: 10.20944/preprints202004.0029.v1). Karballaeezadeh, N.; Mohammadzadeh S., D.; Moazami, D.; Nabipour, N.; Mosavi, A.; Reuter, U. Smart Structural Health Monitoring of Flexible Pavements Using Machine Learning Methods. Preprints 2020, 2020040029 (doi: 10.20944/preprints202004.0029.v1).

Abstract

The construction of different roads, such as freeways, highways, major roads or minor roads must be accompanied by constant monitoring and evaluation of service delivery. Pavements are generally assessed by engineers in terms of the smoothness, surface condition, structural condition and surface safety. Pavement assessment is often conducted using the qualitative indices such as international roughness index (IRI), pavement condition index (PCI), structural condition index (SCI) and skid resistance value (SRV), which are used for smoothness assessment, surface condition assessment, structural condition assessment, and surface safety assessment, respectively. In this paper, Tehran-Qom Freeway in Iran has been selected as the case study and its smoothness and pavement surface conditions are assessed. At 2-km intervals, a 100-meter sample unit is selected in the slow-speed lane (totally, 118 sample units). In these sample units, the PCI is calculated after a visual inspection of the pavement and the recording of distresses. Then, in each sample unit, the average IRI is computed. The purpose of this study is to provide a method for estimating PCI based on IRI. The proposed theory was developed by Random Forest (RF), and Random Forest optimized by Genetic Algorithm (RF-GA) methods and these methods were validated using correlation coefficient (CC), scattered index (SI), and Willmott’s index of agreement (WI) criteria. The proposed method reduces costs, saves time and eliminates the safety risks.

Subject Areas

Mobility; infrastructure; flexible pavement; pavement condition index (PCI); international roughness index (IRI); artificial intelligence (AI); predictive models; ensemble learning; structural health monitoring; machine learning

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