Abstract
Accurately estimating the interfacial bond capacity of the near-surface mounted (NSM) carbon fiber-reinforced polymer (CFRP) to concrete joint is a fundamental task in the strengthening and retrofit of existing reinforced concrete (RC) structures. The machine learning (ML) approach may provide an alternative to the commonly used semi-empirical or semi-analytical methods. Therefore, in this work we have developed a predictive model based on an artificial neural network (ANN) approach, i.e. using a back propagation neural network (BPNN), to map the complex data pattern obtained from an NSM CFRP to concrete joint. It involves a set of nine material and geometric input parameters and one output value. Moreover, by employing the neural interpretation diagram (NID) technique, the BPNN model becomes interpretable, as the influence of each input variable on the model can be tracked and quantified based on the connection weights of the neural network. An extensive database including 163 pull-out testing samples, collected from the authors’ research group and from published results in the literature, is used to train and verify the ANN. Our results show that the prediction given by the BPNN model agrees well with the experimental data and yields a coefficient of determination of 0.957 on the whole database. After removing one non-significant feature, the BPNN becomes even more computationally efficient and accurate. in addition, compared with the existed semi-analytical model, the ANN-based approach demonstrates a more accurate estimation. Therefore, the proposed ML method may be a promising alternative for predicting the bond strength of NSM CFRP to concrete joint for structural engineers.
Abstract
目的
针对采用纤维增强复合材料加固的钢筋混凝土结构, 本文旨在运用机器学习方法取代目前广泛使用的半经验-半分析理论公式, 以准确预测该类加固结构中表层嵌贴纤维增强复合材料 (CFRP) 板条与混凝土界面的粘结强度。
创新点
1. 建立反向传播人工神经网络 (BPNN) 预测表层嵌贴CFRP板与混凝土界面的粘结强度。2. 采用基于Garson算法和连接权重算法的神经解释图 (NID) 定量分析神经网络中各个输入变量的重要性。
方法
1. 从作者课题组完成的实验和已发表的文献中收集共163组表层嵌贴CFRP-混凝土单剪实验结果, 并形成数据集。2. 运用建立的数据集训练和测试BPNN, 构建实验参数与界面粘结强度间的非线性映射关系及预测模型。3. 基于Garson算法和连接权重算法分别计算神经网络输入变量的重要性, 并通过NID分析数据集中有重要影响的输入变量和无效输入变量。
结论
1. 建立的BPNN模型得出的预测结果与实验数据吻合良好, 预测值与真实值之间的决定系数在整个数据集中的表现为0.957。2. 通过删除数据集中的无效输入变量可提高BPNN的计算效率和准确性。3. 与现有的半经验-半分析理论公式相比, 本文建立的BPNN模型可以得出更准确的估计。
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Project supported by the National Natural Science Foundation of China (No. 51808056), the Hunan Provincial Natural Science Foundation of China (No. 2020JJ5583), the Research Foundation of Education Bureau of Hunan Province (No. 19B012), and the China Scholarship Council (No. 201808430232)
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Table S1 Experiment database of NSM CFRP to concrete joint
Contributors
Shao-fan LI designed the research. Hui PENG and Miao SU collected the experiment data. Miao SU wrote the first draft of the manuscript. Shao-fan LI revised and edited the final version.
Conflict of interest
Miao SU, Hui PENG, and Shao-fan LI declare that they have no conflict of interest.
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Application of an interpretable artificial neural network to predict the interface strength of a near-surface mounted fiber-reinforced polymer to concrete joint
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Su, M., Peng, H. & Li, Sf. Application of an interpretable artificial neural network to predict the interface strength of a near-surface mounted fiber-reinforced polymer to concrete joint. J. Zhejiang Univ. Sci. A 22, 427–440 (2021). https://doi.org/10.1631/jzus.A2000245
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DOI: https://doi.org/10.1631/jzus.A2000245
Key words
- Fiber-reinforced polymer (FRP)
- Bond strength
- Machine learning (ML)
- Neural interpretation diagram (NID)
- Regression
- Feature importance
- Connection weights approach