An innovative predictive method based on supervised teacher-student learning for forecasting limited structural responses of long-span bridges from satellite images

The prediction of structural responses of large-scale civil structures offers an effective and efficient alternative to field measurement. Recently, spaceborne remote sensing technology has been increasingly adopted to monitor complex and large bridges in long-term monitoring programs by extracting structural displacements from synthetic aperture radar images. To overcome certain limitations of such images, machine learning-aided prediction of bridge displacement responses has become the best solution. For this purpose, it is necessary to consider main environmental and operational factors that influence bridge responses. Accordingly, various contact-based sensors are installed on bridges to measure influential predictors, including ambient temperature, relative humidity, wind characteristics, and traffic volume. However, in some cases, the installation of these sensors or the preparation of their data may be impractical, leading to unmeasured and unknown conditions that can affect structural responses and are not incorporated into the prediction process. To circumvent poor performance and erroneous prediction outputs, this paper proposes an innovative This method comprises two parts: an elaborate regression model acting as a 'teacher' and a simpler model serving as a 'student.' Initially, measured predictor and response data are fed into the teacher model, which is developed from a long-short-term memory neural network to reconstruct the response data. Subsequently, the residual data between the measured and reconstructed responses is computed to serve as a new predictor. This new predictor, along with the original measured response data, is then used to train a student model developed from a single-hidden-layer feedforward neural network, which finally predicts unseen response data. The effectiveness and reliability of the proposed predictive method are validated using limited information from long-span bridges. Results indicate that this method successfully predicts bridge responses using ambient temperature as the sole main predictor.