Prediction of surface distress using neural networks

Hamdi, Sigit Pranowo Hadiwardoyo, A. Gomes Correia, Paulo Pereira, Paulo Cortez

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Citations (Scopus)

Abstract

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 (R2 = 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%).

Original languageEnglish
Title of host publicationGreen Process, Material, and Energy
Subtitle of host publicationA Sustainable Solution for Climate Change - Proceedings of the 3rd International Conference on Engineering, Technology, and Industrial Application, ICETIA 2016
EditorsHari Prasetyo, Wisnu Setiawan, Fajar Suryawan, Munajat Tri Nugroho, Tri Widayatno, Nurul Hidayati, Eko Setiawan
PublisherAmerican Institute of Physics Inc.
ISBN (Electronic)9780735415294
DOIs
Publication statusPublished - 15 Jun 2017
Event3rd International Conference on Engineering, Technology, and Industrial Application - Green Process, Material, and Energy: A Sustainable Solution for Climate Change, ICETIA 2016 - Surakarta, Indonesia
Duration: 7 Dec 20168 Dec 2016

Publication series

NameAIP Conference Proceedings
Volume1855
ISSN (Print)0094-243X
ISSN (Electronic)1551-7616

Conference

Conference3rd International Conference on Engineering, Technology, and Industrial Application - Green Process, Material, and Energy: A Sustainable Solution for Climate Change, ICETIA 2016
CountryIndonesia
CitySurakarta
Period7/12/168/12/16

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