An Analytical Study Predicting Future Conditions and Application Strategies of Concrete Bridge Pavement Based on Pavement Management System Database

Jinhyuk Lee, Donghyuk Jung (), Cheolmin Baek () and Deoksoon An
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Jinhyuk Lee: Department of Structural Engineering Research, Korea Institute of Civil Engineering and Building Technology, Goyang-daero 283, Ilsanseo-gu, Goyang-si 10223, Gyeonggi-do, Republic of Korea
Donghyuk Jung: Department of Highway & Transportation Research, Korea Institute of Civil Engineering and Building Technology, Goyang-daero 283, Ilsanseo-gu, Goyang-si 10223, Gyeonggi-do, Republic of Korea
Cheolmin Baek: Department of Highway & Transportation Research, Korea Institute of Civil Engineering and Building Technology, Goyang-daero 283, Ilsanseo-gu, Goyang-si 10223, Gyeonggi-do, Republic of Korea
Deoksoon An: Department of Highway & Transportation Research, Korea Institute of Civil Engineering and Building Technology, Goyang-daero 283, Ilsanseo-gu, Goyang-si 10223, Gyeonggi-do, Republic of Korea

Sustainability, 2023, vol. 15, issue 24, 1-16

Abstract: South Korea is implementing various policies to address the aging of infrastructures and improve road infrastructure management. Moreover, numerous research projects aiming at the development of necessary technologies for the proper implementation of these policies are underway. This study specifically aims to overcome existing problems in bridge pavement maintenance, such as the inaccuracy of future condition predictions and the selection of incorrect evaluation indicators. Our goal is to provide a new approach for the improved management of the bridge pavement management system (BPMS). To address the issues of accuracy in future condition prediction and evaluation indicator selection within the existing maintenance system, we utilized particle filtering, a Kalman filter method among machine learning techniques. This method allows for the prediction of future conditions, based on the nonlinearly collected bridge pavement conditions within BPMS. Furthermore, we proposed a systematic bridge pavement management strategy. This strategy utilizes traffic volume (ESALs; equivalent single axle loadings), a factor that can influence the future condition of bridge pavement, in correlation with the future condition predicted through particle filtering within BPMS.

Keywords: bridge deck concrete pavement; condition index; bridge asset management; Particle Filtering; vehicle type; time series analysis (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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