A Knowledge Transfer Enhanced Ensemble Approach to Predict the Shear Capacity of Reinforced Concrete Deep Beams without Stirrups

This study proposes a novel learning algorithm, the Transfer Ensemble Neural Network (TENN) model, to increase the performance of shear capacity predictions on small datasets, reducing the need for these expensive physical tests. By incorporating ensemble learning and transfer learning, the TENN model is designed to control the high variability inherent when trained on small amounts of data. The novel TENN model is validated to predict the shear capacity of deep RC beams without stirrups across varying data availability levels. Knowledge acquired through pretraining a model on slender RC beams is utilized for training a model to better predict the shear capacity of deep RC beams without stirrups. To evaluate the performance of the TENN model, three baseline models are developed and examined across multiple data availability levels. The novel TENN model outperforms the baseline models, particularly when trained on a very limited dataset. Furthermore, the proposed algorithm achieves a higher accuracy than the currently accepted design standards in accurately predicting deep RC beams' shear capacity and demonstrates the capabilities of the TENN model to extrapolate on other domains where large-scale or physical testing is cost-prohibitive.