Abstract—In order to extract the optimal output in the form
of good management decisions with least resources, a bridge
management system or BMS in short, is an essential part for
every road transport authority. In a BMS, decisions regarding
frequency of maintenance, conducting repairs and
rehabilitation are based on inspection data collected for the
bridges by trained inspectors following a condition rating
method developed by the authority. The road authorities are
constantly trying to convert these condition monitoring data to
a meaningful practical decision supporting tool. To address
this need, a study has been conducted to forecast deterioration
of reinforced concrete bridge elements using Markov process.
The aim of the research work is to identify the future
maintenance needs utilizing the visual inspection data. Visual
inspection data has been sourced from Victoria, Australia and
transition matrices have been derived using Bayesian
optimisation techniques of Markov chain model to predict the
future condition of bridge components. Clustering of data with
respect to input parameters such as era of construction,
exposure conditions, annual average daily traffic and
percentage of heavy vehicles can provide an improved
deterioration model for bridge Engineers. Deterioration trends
for three major structural components are presented in this
paper.
Index Terms—Bridge deterioration, bridge management
system, condition monitoring, markov chain.
Md Saeed Hasan, Sujeeva Setunge, and David W. Law are with School
of Civil, Environmental and Chemical Engineering, RMIT University,
Melbourne, Australia (e-mail: m.hasan@student.rmit.edu.au).
Yew-Chin Koay is with Technical Services, Vicroads, Melbourne,
Victoria, Australia.
[PDF]
Cite: Md Saeed Hasan, Sujeeva Setunge, David W. Law, and Yew-Chin Koay, "Forecasting Deterioration of Bridge Components from
Visual Inspection Data," International Journal of Engineering and Technology vol. 7, no. 1, pp. 40-44, 2015.
