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Paper w78-2011-Paper-37:
Bridge Life-Cycle Cost Analysis Using Artificial Neural Networks

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A Asadi, A Hadavi, R J. Krizek

Bridge Life-Cycle Cost Analysis Using Artificial Neural Networks

Abstract: Life-Cycle Cost analysis can significantly assist in making investment decisions. Several recentstudies have recognized the potential benefits of Life-Cycle Cost analysis and call for use of suchanalyses when making infrastructure investments, including investments in bridges. The Life-CycleCost of a bridge consists of the total investment throughout the life of the bridge. This includes theinitial construction cost, repair and rehabilitation costs, and all maintenance costs. The ability toaccurately determine the Life-Cycle Cost of a bridge will help agencies evaluate the asset value ofexisting bridges, make better decisions on the design and construction of new ones, and chooseimproved methods and approaches for rehabilitating existing structures. Research has shown thattimely maintenance, repair, and rehabilitation can lower the Life-Cycle Cost of a bridge. However, thisis a complex and nonlinear problem, and previous studies have failed to develop a satisfactory model. One effective technique for solving nonlinear problems with complicated functions is an ArtificialNeural Network. A neural network is a powerful data-modeling tool that captures and representscomplex input/output relationships. Using a set of input and output data belonging to a particularproblem, a neural system can be trained to predict outcomes for new versions of the same problem.Accordingly, an extensive set of data (bridge dimensions, age, initial cost, and Life-Cycle Cost) for 14Chicago bridges was used to quantify the degree of success that could be achieved with this model.Sixty percent of the data was used as input to train the model and the remaining forty percent was usedto assess the success of the model for predicting the Life-Cycle Cost. The results achieved wereencouraging and suggest that the neural network model is a promising tool for predicting the LifeCycleCost ofa bridge.

Keywords: life-cycle cost, artificial neural network, Chicago Trunnion Bascule bridges. initial cost, repair and rehabilitation cost


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