Sensor placement and seismic response reconstruction for structural health monitoring using a deep neural network

In seismic structural health monitoring (SHM), a structure is normally instrumented with limited sensors at certain locations to monitor its structural behavior during an earthquake event. To reconstruct the responses at non-instrumented locations, an effective regression method has to be used given the measured data from the sensed locations. In addition, determination of where to place the sensors directly affects the ability of the system to infer the behaviour of the entire structure. In this study, a practical framework is proposed for sensor placement and seismic response reconstruction at non-instrumented locations, which adopts a novel attention-based deep neural network (DNN). The developed DNN model is trained by using structural displacements at measured locations as input and the structural displacements at unmeasured locations of interest as output. The proposed framework is demonstrated by a case study of an instrumented long-span girder bridge in California. Different sensor placement schemes are investigated using the proposed DNN model. Real-time seismic assessment of the bridge is achieved by issuing each reconstructed output in 1.5 ms. The case study validates the effectiveness and accuracy of the proposed method to be used as part of a seismic SHM system.