Abstract Summary
Structural health monitoring of bridges aims to provide an assessment of the condition of the structure, using collected structural response. The efficacy of the methods usually is constrained by the number and spatial distribution of sensors. Developing methods to map the response from known to unknown locations has been a challenging yet interesting area of study in recent years. In this research, we propose and study a novel framework to estimate and reconstruct the dynamic response of bridges at the connection level from the vibration response at global locations. The bridge is considered as a dynamic system in which vehicle excitations are the input and responses at sensor locations are the outputs. The proposed method studies an output-only problem and the input is considered unknown. The response at two types of output locations of the bridge, one at a global and one at the connection detail level are used to learn the dynamic relationship between the time signals via convolutional neural networks. This model-free framework is validated through a finite element simulation to reconstruct the strain response at the gusset plate of a truss bridge from the responses at other nodes along the bridge.
