Soft Computing Models to Predict Pavement Roughness: A Comparative Study

Panos Georgiou,Christina Plati,Andreas Loizos

doi:10.1155/2018/5939806

Panos Georgiou, Christina Plati + Show 1 more

Open Access

https://doi.org/10.1155/2018/5939806

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Abstract

Pavement roughness as a critical determinant of public satisfaction can potentially play a major role in road or highway resource allocation to competing pavement resurfacing projects. With this in mind, the aim of the present paper is to develop an accurate model for the prediction of pavement roughness in terms of the International Roughness Index (IRI) using artificial neural networks (ANNs) and support vector machines (SVMs). The modeling is based on pavement roughness data collected periodically for a high-volume motorway during a seven-year period, on a yearly basis. The comparative study of the developed models concludes that the performance of the ANN model is slightly better compared to the SVM in terms of prediction accuracy. Further, the analysis results produce evidence in support of the statement that both models are capable to predict accurately pavement roughness; hence, they are deemed useful for supporting decision making of pavement maintenance and rehabilitation strategies.

Highlights

Background and ObjectivesRoad pavements deteriorate under the combined effect of traffic loading and environmental conditions
artificial neural networks (ANNs) is the computational intelligence system that mimics the human brain behaviour; this computing system consists of neurons, which are simple, interconnected, and adaptive processing units [23]
Historical roughness IRIt-p data, which are gathered as previously described during the rst six years of monitoring, are referred hereinafter as IRIt-6, IRIt-5, IRIt-4, IRIt-3, IRIt-2, and IRIt-1, considering the variable of time (p 1...6) in years. ese data are used as input parameters for the development of the one-year step roughness prediction model

Summary

Introduction

Background and ObjectivesRoad pavements deteriorate under the combined effect of traffic loading and environmental conditions. Performance is a general term describing the way pavements’ conditions change or satisfy their intended function offering an at least acceptable level of service to the road users over their design life. Over the past few decades, road agencies have established performance indicators to assess the effectiveness and efficiency of their service provision. An important indicator of pavement performance is ride quality. Is is a rather subjective measure of performance that depends on (i) the physical properties of the pavement surface, (ii) the mechanical characteristics of the ride vehicle, and (iii) the standards of the road users concerning the acceptability of the perceived ride quality. Due to the subjectivity of the ride quality assessment, a lot of researchers had worked in the past to establish an objective indicator of pavement performance. Starting at the early 1960’s with the development of present serviceability index (PSI) [1, 2], nowadays the International Roughness Index (IRI) seems to have the broadest application for the assessment of ride quality [3,4,5]

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