Bridge management by dynamic programming and neural networks

Bridges and pavements represent the major investment in a highway network. In addition, they are in constant need of maintenance, rehabilitation, and replacement. One of the problems related to highway infrastructure is that the cost of maintaining a network of bridges with an acceptable level-of-service is more than the budgeted funds. For large bridge networks, traditional management practices have become inadequate for dealing with this serious problem. Bridge management systems are a relatively new approach developed to solve the latter problem, following the successful application of similar system concepts to pavement management. Priority setting schemes used in bridge management systems range from subjective basis using engineering judgement to very complex optimization models. However, currently used priority setting schemes do not have the ability to optimize the system benefits in order to get optimal solutions. This paper presents a network optimization model which allocates a limited budget to bridge projects. The objective of the model is to determine the best timing for carrying out these projects and the spending level for each year of the analysis period in order to minimize the losses of the system benefits. A combined dynamic programming and neural network approach was utilized to formulate the model. The bridge problem has two dimensions: the time dimension and the bridge network dimension. The dynamic programming sets its stages in the time dimension, while the neural network handles the network dimension. Key words: bridge management, dynamic programming, neural networks, budget allocation.