Enhancing load prediction for structures with concrete overlay using transfer learning of time–frequency feature-based deep models

Abstract

This study proposes a method to predict applied loads on bonded structures via non-destructive testing results. Various types of concrete, such as high-performance alkali-activated slag concrete (HPAASC) and regular Portland cement concrete (OPCC) are simultaneously tested via ultrasound testing and bi-surface shear for extracting features and target values, respectively. Variational Mode Decomposition is employed to extract useful features from ultrasound signals. In the first part, selected features using a PCA-based method are utilized to train a set of deep-learning models to estimate the imposed mechanical load on the tested specimens. The most effective models from the first step, i.e., a CNN-LSTM model and LSTM models, are then fine-tuned in the second step to estimate loading conditions in another set of specimens. Results indicate the superior performance of the CNN-LSTM model. The proposed algorithm highlights the effectiveness of ultrasound features in accurately evaluating structural loading conditions for efficient monitoring of various structures.