Network Deterioration Prediction for Reinforced Concrete Pipe and Box Culverts Using Markov Model: Case Study

Reinforced concrete (RC) pipe and box culverts are widely used as an alternative to bridge structures in road transport networks around the world. The deterioration of the RC culverts is a complex problem caused by combined humanmade and natural processes with various influential factors. Visual inspection is often used to monitor the deterioration of culverts, and the inspection results are used to rate condition of culverts by using a discrete condition rating system. The objective of this case study was to investigate the deterioration of RC culverts at the network and cohort levels by using a Markov model and culverts' influential factors and inspected condition data. The Markov deterioration model can forecast the future deterioration of a culvert network, which can be used for asset management planning of the culvert network. A real case study with a regional local government in Australia was used to demonstrate the application of this study. The results of network deterioration modeling showed that the deterioration rates of culverts varied with culvert type (pipe and box culvert), built year, demographic location, and pipe size. However, annual average daily traffic (AADT) affected only box culverts. Deterioration prediction was found to be sensitive to the time length of evidence data, which highlights the importance of keeping records of maintenance and rehabilitation activities for producing accurate modeling data.

Automated summary

This case study investigates the deterioration of reinforced concrete culverts at the network and cohort levels using a Markov model and influential factors and inspected condition data. The Markov deterioration model can forecast the future deterioration of a culvert network, which is important for asset management planning.