Long-term missing wind data recovery using free access databases and deep learning for bridge health monitoring

Abstract

Wind environment monitoring at the bridge site is essential in structural health monitoring (SHM). However, as the performance of the electronic equipment for bridge SHM system deteriorates, wind monitoring data often suffer from long-term continuous data missing. The duration of data missing may be as long as several months, creating barriers to safety monitoring and monitoring data mining of the bridge structures. The conventional interpolation techniques hardly achieve the desired data recovery. Therefore, a framework was proposed for long-term missing wind data recovery based on a deep neural network (DNN) utilizing a free access database (European Center for Medium-Range Weather Forecasts, ECMWF). This framework consisted of one regression task (Task 1) and one temporal super-resolution task (Task 2). In Task 1, the hourly wind data provided by ECMWF were first learned to the hourly ones of the bridge SHM system. In Task 2, the low-resolution wind data (hourly averages) were upsampled to high-resolution ones (10-min averages). The U-net architecture provided the basis for the DNNs in both tasks. Unlike the conventional time-domain loss function used in Task 1, Task 2 adopted a time-frequency cross-domain loss function for training, which innovatively employed a spectrum magnitude balance strategy to enhance the reconstruction of the high-frequency components of wind speed signals. The proposed framework's feasibility and effectiveness were verified through a case study of recovering long-term continuous missing wind data in the SHM system of Sutong Bridge, China. The proposed methodology provides a new perspective for recovering long-term continuous missing SHM data.