TY - GEN
T1 - Prediction of surface distress using neural networks
AU - Hamdi,
AU - Hadiwardoyo, Sigit Pranowo
AU - Correia, A. Gomes
AU - Pereira, Paulo
AU - Cortez, Paulo
N1 - Publisher Copyright:
© 2017 Author(s).
PY - 2017/6/15
Y1 - 2017/6/15
N2 - 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%).
AB - 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%).
UR - http://www.scopus.com/inward/record.url?scp=85021447115&partnerID=8YFLogxK
U2 - 10.1063/1.4985502
DO - 10.1063/1.4985502
M3 - Conference contribution
AN - SCOPUS:85021447115
T3 - AIP Conference Proceedings
BT - Green Process, Material, and Energy
A2 - Prasetyo, Hari
A2 - Setiawan, Wisnu
A2 - Suryawan, Fajar
A2 - Nugroho, Munajat Tri
A2 - Widayatno, Tri
A2 - Hidayati, Nurul
A2 - Setiawan, Eko
PB - American Institute of Physics Inc.
T2 - 3rd International Conference on Engineering, Technology, and Industrial Application - Green Process, Material, and Energy: A Sustainable Solution for Climate Change, ICETIA 2016
Y2 - 7 December 2016 through 8 December 2016
ER -