Long-Term Performance of Bridge Elements Using Integrated Deterioration Method Incorporating Elman Neural Network
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Currently, probabilistic deterioration modeling techniques have been employed in most state-of-the-art Bridge Management Systems (BMSs) to predict future bridge condition ratings. As confirmed by many researchers, the reliability of the probabilistic deterioration models rely heavily on the sufficient amount of condition data together with their well-distributed historical deterioration patterns over time. However, inspection records are usually insufficient in most bridge agencies. As a result, a typical standalone probabilistic model (e.g. state-based or time-based model) is not promising for forecasting a reliable bridge long-term performance. To minimise the shortcomings of lacking condition data, an integrated method using a combination of state- and time-based techniques has recently been developed and has demonstrated an improved performance as compared to the standalone techniques. However, certain shortcomings still remain in the integrated method which necessities further improvement. In this study, the core component of the state-based modeling is replaced by an Elman Neural Networks (ENN). The integrated method incorporated with ENN is more effective in predicting long-term bridge performance as compared to the typical deterioration modeling techniques. As part of comprehensive case studies, this paper presents the deterioration prediction of 52 bridge elements with material types of Steel (S), Timber (T) and Other (O). These elements are selected from 94 bridges (totaling 4,115 inspection records). The enhanced reliability of the proposed integrated method incorporating ENN is confirmed.
Applied Mechanics and Materials
Infrastructure Engineering and Asset Management