Funding source: US DOT

Contract number: 69A3551747109

Funding amount: $26,650

Performance period: 8/16/2021 to 8/15/2022

An early damage identification process in bridge structures may offer an opportunity to slowdown progressive failure and thus prevent catastrophic collapses. With a structural health monitoring system which allows real-time measurement of structural responses, this may be possible if proper techniques are employed to identify early damage in bridge structures. In doing so, the proposed project will integrate two methods (i.e., a model updating technique and an artificial intelligence (AI) prediction) that can compensate for each other's the weakness that otherwise imposed difficulty in precise real-time application of health monitoring systems. This project will leverage a mode-updating technique with high-fidelity experimental data to obtain an accurate digital model that represents an actual bridge model. The drawback of the model updating technique (i.e., high computational time) will be overcome by applying an artificial intelligence algorithm such as artificial neural networks that are known to be computationally efficient while perusing high accuracy. The proposed approach will then result in a fast and accurate method (i.e., a model-based data-driven method) for early damage identification of bridge structures.