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Artificial Neural Network Model For Bridge Deterioration And Assessment, G. Ali, A. Elsayegh, R. Assaad, Islam H. El-Adaway, I. S. Abotaleb
Artificial Neural Network Model For Bridge Deterioration And Assessment, G. Ali, A. Elsayegh, R. Assaad, Islam H. El-Adaway, I. S. Abotaleb
Civil, Architectural and Environmental Engineering Faculty Research & Creative Works
Missouri has the seventh largest number of bridges nationwide, yet must maintain its inventory with funding from just the fourth lowest gasoline tax in the country. Estimation and prediction of the condition of bridges is necessary to create and optimize future maintenance, repair, and rehabilitation plans as well as to assign the necessary associated budgets. Previous studies have used statistical analysis, fuzzy logic, and Markovian models to develop algorithms for predicting future bridge conditions. Due to the non-linear nature of the relationship between the characteristics of bridges and their deterioration behavior, Artificial Neural Networks (ANN) have shown to be more …