A knowledge transfer enhanced ensemble approach to predict the shear capacity of reinforced concrete deep beams without stirrups
A knowledge transfer enhanced ensemble approach to predict the shear capacity of reinforced concrete deep beams without stirrups
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DOI：
10.1111/mice.12965
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发表时间：
2023-01
期刊：
影响因子：
--
通讯作者：
H. Pak;S. Leach;S. Yoon;S. Paal
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文献类型：
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作者：
H. Pak;S. Leach;S. Yoon;S. Paal
本文提出了一种新的学习算法--传递集成神经网络(Tenn)模型，以提高在小数据集上的抗剪承载力预测的性能，说明了先进的机器学习技术的有效性。通过结合集成学习和转移学习，Tenn模型被设计来控制在基于少量数据训练的机器学习模型中固有的高变异性。新的Tenn模型被验证在不同的数据可用性水平下预测无箍筋钢筋混凝土(RC)深梁的抗剪承载力。通过预训练获得的知识，将细长钢筋混凝土梁模型用于训练模型，以更好地预测无箍筋钢筋混凝土深梁的抗剪承载力。为了评估Tenn模型的性能，开发了三个基准模型，并在多个数据可用性级别上进行了检查。新的Tenn模型的性能优于基线模型，特别是在非常有限的数据集上进行训练时。此外，该算法在准确预测深部钢筋混凝土梁抗剪承载力方面达到了比目前公认的设计标准更高的精度，并证明了Tenn模型在其他大规模或物理试验成本较高的领域的外推能力。
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.