Soft Computing Models to Predict Pavement Roughness: A Comparative Study

• Published in: Advances in civil engineering Vol. 2018; no. 2018; pp. 1 - 8
• Main Authors: Georgiou, Panos, Loizos, Andreas, Plati, Christina
• Format: Journal Article
• Language: English
• Published: Cairo, Egypt Hindawi Publishing Corporation 01-01-2018; Hindawi; Hindawi Limited
• Subjects: Artificial neural networks; Back propagation; Civil engineering; Comparative studies; Decision making; Gene expression; Infrastructure; Learning; Mathematical models; Neural networks; Pavement resurfacing; Performance evaluation; Physical properties; Quality; Rehabilitation; Researchers; Resource allocation; Resurfacing; Roughness; Soft computing; Software; Support vector machines; Surfacing
• ISSN: 1687-8086; 1687-8094
• DOI: 10.1155/2018/5939806

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.