Abstract Summary
Structural health monitoring relies on direct measurements from the structure for a variety of necessary investigations including operational modal analysis, life-cycle assessment, damage detection, and model updating. Although, because of complications and inherent inaccuracies in this direct measurement, engineers have been seeking to facilitate the data collection using machine learning-based approaches for a more robust strategy. In this work, a novel transfer learning framework is proposed to enable spatiotemporal strain estimation from acceleration measurement of the bridge for unmeasured or faulty locations and times. This task is executed by extraction of time-dependant and location-dependant contents of collected vibration signals and reconstruction of strain signal merely by providing the desired time and location. The framework has been verified on a simulation case study and showed high accuracy signal reconstruction metrics.
