## 4 Theoretical Investigation of Bond Behaviour  
The application of a new composite material in reinforcing concrete structures is highly dependent on the bond behaviour and strength, and specifically on the bar-concrete interaction in comparison to steel bars. Hence, knowledge of bond behaviour and other interfacial characteristics is vital to confirm a reliable transfer of forces in the reinforced structure. The commonly adopted tests by the ACI, Canadian, and other relevant standards include direct pull-out tests, notched beam tests, and push-out tests. Out of these tests, direct pull-out tests are the most commonly adopted, due to ease in test configuration. However, to confirm the bond characteristics, several parameters need to be studied. Analytical prediction models are used as an indicator in order to assess the bond properties of a possible configuration. The use of theoretical prediction models will help researchers in identifying the most critical parameters that affect overall behaviour. In the current study, the composite bars under consideration are basalt FRP bars embedded in a newly developed self-compacting geopolymer concrete, the behaviour of which is yet to be fully understood. Table 5 below lists the existing mathematical representations for steel and FRP bars in conventional concrete, which are currently adopted in ACI and CSA for the bond strength prediction of FRP bars [27; 59; 60]. The theoretically predicted bond strength values, using the models above by ACI and CSA, are found to exaggerate the true bond behaviour, as depicted in Table 6 below: \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Sl.No:** & **Standard Guideline** & **Bond Strength Prediction Equation** & **FRP Bar Type** & **Remark** \\ \hline
1 & ACI 440.1R-06 & \(\tau_{\text{max}}=0.083\sqrt{t_{\text{c}}}(4.0+0.3\text{c}/\text{d}+100\) & Glass, E-glass, Carbon, & No specific coefficients \\  & & d/J & & Aramid & of factors to account for \\ \hline
2 & ACI 440.1R-03 & \(\tau_{\text{max}}=20.23\sqrt{t_{\text{c}}}/\text{d}\) & Glass, E-glass, Carbon, & the type and material \\  & & & Aramid & of FRP bar \\ \hline
3 & CSA-S806-02 & \(\tau_{\text{max}}=d_{\text{cs}}\sqrt{t_{\text{c}}}/1.15(k_{1}\ k_{2}\ k_{3}\ k_{4}\) & Glass, E-glass, Carbon, & Considers fibre type \\  & & k\({}_{\text{s}})\text{yrd}_{\text{b}}\) & Aramid & and profile of bar \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Bond strength prediction equation for FRP bars in concrete. The comparison of the predicted values with the experimental values confirms the inadequacy of standards/design codes for accurately predicting the bond behaviour of a new FRP-geopolymer concrete composite. The only guidelines available for FRP bars in concrete--namely, the ACI [22; 59] and CSA [27] guidelines--do not include basalt FRP bars or geopolymer concrete. The current practice is to use the same coefficients for glass FRP and BFRP bars, which creates non-conformity owing to differences in the material properties of glass and basalt fibres [18; 61; 62]. Numerous studies have focused on finding accurate coefficients corresponding to the type of FRP bar, material, and surface properties in order to produce an accurate prediction model [31; 52; 63]. However, it has been found that the developed mathematical models are not applicable to all the parameters affecting bond behaviour and performance [33; 38; 49]. The current paper thus identifies the need for a powerful learning technique, such as an artificial neural network (ANN), which can take into consideration an array of various parameters that can affect the bond performance of the FRP-SCGC composite. Unlike traditional prediction methods, the software supporting ANN learning tools is able to predict bond behaviour by considering the complex relationships amongst the various parameters and understanding the pattern of experimental results [64; 65; 66].