A multi-sensor mapping Bi-LSTM model of bridge monitoring data based on spatial-temporal attention mechanism

The premise of intelligent structural health monitoring is to build a digital benchmark model representing the multi-sensor mapping relationship. Previously, researchers have used machine learning methods such as Long -and-Short-Term-Memory (LSTM) networks for modeling multi-sensor mapping relationships. However, the typical LSTM networks treat multi-dimensional input data equally, ignoring the possible correlations of different periods and sensor placements. Consequently, they can hardly obtain an accurate model for long-time series and multidimensional datasets. To address the issues, by introducing a two-layer attention mechanism in the tem-poral and spatial dimensions, an improved Bi-direction LSTM neural network model embedding the attention mechanism is proposed. The model focuses on grasping the non-stationary response process and the spatial correlation of multi-sensors. The accuracy and efficiency of the proposed approach are verified through two case studies. In addition, based on the visualization of attention weight assignments, the spatial-temporal attention distribution is studied to give an engineering interpretation.