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

Abstract

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 temporal 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.