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Journal of Zhejiang University SCIENCE A 1998 Vol.-1 No.-1 P.

http://doi.org/10.1631/jzus.A2000245


Application of an interpretable artificial neural network to predict the interface strength of a near-surface-mounted CFRP to concrete joint


Author(s):  Miao SU, Hui PENG, Shao-fan Li

Affiliation(s):  School of Civil Engineering, Changsha University of Science and Technology, Changsha 410114, China; more

Corresponding email(s):   shaofan@berkeley.edu

Key Words:  FRP, Bond strength, Machine learning, Neural interpretation diagram, Regression, Feature importance, Connection weights approach


Miao SU, Hui PENG, Shao-fan Li. Application of an interpretable artificial neural network to predict the interface strength of a near-surface-mounted CFRP to concrete joint[J]. Journal of Zhejiang University Science A, 1998, -1(-1): .

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Abstract: 
Accurately estimating the interfacial bond capacity of the near-surface-mounted 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 a near-surface-mounted 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 ML method proposed in this work may be a promising alternative for predicting the bond strength of near-surface-mounted CFRP to concrete joint for structural engineers.

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