Heterogeneous Deterioration Process and Risk of Deficiencies of Aging Bridges for Transportation Asset Management

Abstract

The government of the Republic of Korea has set the minimum service level of bridges as Grade B and has defined the risk management level as higher than 95 percent. To achieve this goal, it is necessary to understand the deterioration process and risk of deficiencies for bridges, and these characteristics should be reflected in the management strategy and budget investment plan. To this end, this study developed deterioration models according to the bridge ages to define heterogeneous deterioration characteristics of aging bridges. To build the deterioration models, this study collected and processed bridge diagnosis data for 10 years, and a Bayesian Markov mixed hazard model was introduced. Analysis results showed that the life expectancy of the aging bridges over 30 years was remarkably short, 1/3 of the average life expectancy of the network, and the probability of failure was seven times higher than that of new bridges within 10 years after completion. In addition, the optimal maintenance demand that satisfies a risk management level of 95 percent illustrated that 44.7 percent of the bridges at Grade C should be continuously maintained annually. The results showed that it is urgent to prepare a preemptive response strategy and budget-securing plan for aging bridges, which will rapidly increase to 39% in the next 10 years and 76% in 20 years.

Assessing Critical Road Sections: A Decision Matrix Approach Considering Safety and Pavement Condition

“…It is a preferred choice for decision-making due to its practicality and risk measurement capabilities [39,40]. The MMH model has gained popularity among the advanced Markovian models [40,41]. Initially, the MMH model, which was developed by Obama et al to consider the effect of infrastructure heterogeneity on the hazard rate and to compare the deterioration rate of infrastructures through benchmark analysis, uses the maximum likelihood estimator (MLE) [35].…”

“…Application of the Bayesian approach overcomes MLE's shortcomings, such as a requirement for a relatively large amount of data, sensitivity to outliers, and the local maxima estimation. The MMH's superiority over other Markov models in considering heterogeneity, combined with its capability to determine the life expectancy of infrastructures and quantify uncertainty, satisfies the deterioration model's requirements [41]. The MMH model is used in this study to assess the deterioration rate of pavement sections due to its advantageous features and practicality.…”