A knowledge transfer enhanced ensemble approach to predict the shear capacity of reinforced concrete deep beams without stirrups

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 trans...

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Bibliographic Details
Published in:Computer-aided civil and infrastructure engineering 2023-07, Vol.38 (11), p.1520-1535
Main Authors: Pak, Hongrak, Leach, Samuel, Yoon, Seung Hyun, Paal, Stephanie German
Format: Article
Language:English
Subjects:
Algorithms
Availability
Data acquisition
Datasets
Design standards
Knowledge acquisition
Knowledge management
Machine learning
Neural networks
Performance evaluation
Reinforced concrete
Shear
Stirrups
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Summary: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.
ISSN:1093-9687
1467-8667
DOI:10.1111/mice.12965