This paper proposes a novel learning algorithm, the transfer ensemble neural network (TENN) model, to increase the performance of shear capacity predictions on small datasets, illuminating the usefulness of advanced machine learning techniques in general. By incorporating ensemble learning and transfer learning, the TENN model is designed to control the high variability inherent in machine learning models trained on small amounts of data. The novel TENN model is validated to predict the shear capacity of deep reinforced concrete (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 in other domains where large-scale or physical testing is cost-prohibitive. BACKGROUNDGenerally, reinforced concrete (RC) beams fail in flexure or shear. Shear failure occurs in a relatively brittle manner compared to the more ductile flexural failure. As sudden brittle failure of RC beams may cause severe collapse of structures, loss of properties, and casualties, accurate prediction of shear behavior is critical. Although RC beams are designed to fail in flexure to avoid such hazards, deep beams are usually governed by shear. In addition, considerable research has shown that the American Concrete Institute (ACI) provisions for estimating the shear capacity of large, narrow, lightly reinforced beams without stir-

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