Abstract—Deterioration of reinforced concrete bridges is major issue in structural engineering due to the difficulty of estimating or predicting the service life of the bridge. Two types of models were developed to estimate the service life, one deterministic and the other probabilistic. The reliability of these models is questioned since they do not account for the many factors involved. Therefore, for this research artificial neural network (ANN) is used to estimate the deterioration age for RC bridges based on actual deterioration data. Historical records of bridges located in London is used to train and test ANN. Feedforward neural network is designed to be able to estimate the deterioration age. ANN inputs are bridge type, member type, exposure, and defects while the target is the defects age., Design of experiment is conducted to select and monitor the most important parameters that would affect ANN performance. The results were mediocre reflecting the type of data provided in neural network training.
Index Terms—Bridge deterioration, artificial neural networks, design of experiment.
Aseel Hussein and Abid Abu Tair are with the The British University in Dubai (BUID) Dubai, UAE (e-mail: Aseelal89@live.com, abid.abu-tair@buid.ac.ae)
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Cite: Aseel Hussein and Abid Abu Tair, "Estimating Bridge Deterioration Age Using Artificial Neural Networks," International Journal of Engineering and Technology vol. 11, no. 1, pp. 29-32, 2019.
