Abstract
Fibre-reinforced cement mortar (FRCM) has been widely utilised for the repair and restoration of building structures. The bond strength between FRCM and concrete typically takes precedence over the mechanical parameters. However, the bond behaviour of the FRCM–concrete interface is complex. Due to several failure modes, the prediction of bond strength is difficult to forecast. In this paper, effective machine learning models were employed in order to accurately predict the FRCM–concrete bond strength. This article employed a database of 382 test results available in the literature on single-lap and double-lap shear experiments on FRCM–concrete interfacial bonding. The compressive strength of concrete, width of concrete block, FRCM elastic modulus, thickness of textile layer, textile width, textile bond length, and bond strength of FRCM–concrete interface have been taken into consideration with popular machine learning models. The paper estimates the predictive accuracy of different machine learning models for estimating the FRCM–concrete bond strength and found that the GPR model has the highest accuracy with an R-value of 0.9336 for interfacial bond strength prediction. This study can be utilising in the estimation of bond strength to minimise the experimentation cost in minimum time.
Highlights
It is common to evaluate the model performance once a predictive approach has been fully constructed.The analytical estimates produced from the Artificial Neural Networks (ANNs), regression tree, Gaussian process regression (GPR), linear regression, Support Vector Machine (SVM), ensemble tree, optimized GPR, optimized SVM, optimized ensemble tree and optimized regression tree machine learning algorithms are presented
Despite the fact that the statistical metrics of the ANN and GPR models on the training data do not differ much, the overall performance of the ANN model is significantly better than the other models
The number of Fibre-reinforced cement mortar (FRCM) layers, the compressive strength of the concrete block, the width of the concrete block, the tensile strength, the elastic modulus, the thickness of the FRCM plate and the concrete block width were considered as input parameters to predict FRCM–concrete bond strength
Summary
The necessity for retrofitting existing concrete infrastructure is essential due to ageing, environmental-induced degradation, lack of maintenance, or the need to fulfill current design standards [1]. Su et al [52] used ANN, MLR and SVM algorithms to predict the interfacial bond strength between FRP and concrete. Alam et al [57] developed a machine leaning prediction model to analyze the shear capacity of FRP-strengthened RC beams using Bayesian optimisation algorithm-support vector regression. The proposed bond–slip strength prediction model was compared to five existing empirical models, with the optimized ANN-GA model outperforming them all He et al [63] studied the comparison of different machine learning models for the assessment of delamination in FRP composite beams. The feasibility and reliability of machine learning models such as GPR, SVM, ANN, DT, linear regression and ensemble trees in predicting FRCM–concrete bond strength were studied in this work. The outcomes of this study have been briefly discussed in the last Section 6 of this article
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