Soft Computing Models to Predict Pavement Roughness: A Comparative Study
Joint Authors
Plati, Christina
Georgiou, Panos
Loizos, Andreas
Source
Issue
Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-8, 8 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2018-07-08
Country of Publication
Egypt
No. of Pages
8
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Georgiou, Panos& Plati, Christina& Loizos, Andreas. 2018. Soft Computing Models to Predict Pavement Roughness: A Comparative Study. Advances in Civil Engineering،Vol. 2018, no. 2018, pp.1-8.
https://search.emarefa.net/detail/BIM-1116302
Modern Language Association (MLA)
Georgiou, Panos…[et al.]. Soft Computing Models to Predict Pavement Roughness: A Comparative Study. Advances in Civil Engineering No. 2018 (2018), pp.1-8.
https://search.emarefa.net/detail/BIM-1116302
American Medical Association (AMA)
Georgiou, Panos& Plati, Christina& Loizos, Andreas. Soft Computing Models to Predict Pavement Roughness: A Comparative Study. Advances in Civil Engineering. 2018. Vol. 2018, no. 2018, pp.1-8.
https://search.emarefa.net/detail/BIM-1116302
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1116302