Please use this identifier to cite or link to this item: http://hdl.handle.net/1822/52168

TitlePrediction of surface distress using neural networks
Author(s)Hamdi
Hadiwardoyo, Sigit P.
Correia, A. Gomes
Pereira, Paulo A. A.
Cortez, Paulo
Issue date1-Jan-2017
PublisherAmerican Institute of Physics
JournalAIP Conference Proceedings
Abstract(s)Road infrastructures contribute to a healthy economy throughout a sustainable distribution of goods and services. A road network requires appropriately programmed maintenance treatments in order to keep roads assets in good condition, providing maximum safety for road users under a cost-effective approach. Surface Distress is the key element to identify road condition and may be generated by many different factors. In this paper, a new approach is aimed to predict Surface Distress Index (SDI) values following a data-driven approach. Later this model will be accordingly applied by using data obtained from the Integrated Road Management System (IRMS) database. Artificial Neural Networks (ANNs) are used to predict SDI index using input variables related to the surface of distress, i.e., crack area and width, pothole, rutting, patching and depression. The achieved results show that ANN is able to predict SDI with high correlation factor (R-2 = 0.996%). Moreover, a sensitivity analysis was applied to the ANN model, revealing the influence of the most relevant input parameters for SDI prediction, namely rutting (59.8%), crack width (29.9%) and crack area (5.0%), patching (3.0%), pothole (1.7%) and depression (0.3%).
TypeConference paper
URIhttp://hdl.handle.net/1822/52168
ISBN9780735415294
DOI10.1063/1.4985502
ISSN0094-243X
Peer-Reviewedyes
AccessRestricted access (UMinho)
Appears in Collections:CAlg - Artigos em livros de atas/Papers in proceedings

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