Application and Comparison of Regression and Markov Chain Methods in Bridge Condition Prediction and System Benefit Optimization

The maximization of a bridge system is achieved using mathematical optimization techniques, such as linear programming and dynamic programming. For each bridge, the input data of the bridge project selection model includes the predicted bridge condition in future years, the recommended bridge repair action, the estimated cost of recommended bridge repair action, and the expected improvement or benefit from the repair action. Through mathematical manipulation, bridge projects are selected to maximize the total expected benefit of the bridge system while a number of constraints are simultaneously satisfied. This optimization process is based on the predicted bridge conditions. Therefore, the accuracy of bridge condition predictions is vital to the effectiveness of bridge project selection. This paper shows that bridge condition predictions will affect bridge project selections and the corresponding system benefits