## 4 Discussion  
### For Insights from Data Analytical Results  
#### 4.1.1 For the Models and Factors  
This study revealed some surprising insights through analytical approaches, which may be beneficial in practise. For example, _all of the 10 SLR/MLR models are shown to predict the CS of a sample significantly (\(p<0.05\))_, and the predictive relationship does exist; _however, the explanatory power of the model may vary regarding \(R^{2}\)_. Another key observation is that _the involvement of the curing temperature, T, in a bivariate MLR model is a 'killer' to its data-model fitness_ during the prediction (i.e., \(R^{2}=0.1534\) for T and W/B ratio, =0.3818 for T and #days, and =0.4162 for T and SR-BJPA). The lowest \(R^{2}\) among all other combinations of the predictors is \(R^{2}=0.4741\) for W/B ratio and #days. This is mainly due to the inherent limited ability of T to explain the data variance, as T explains almost no variance when used as the sole predictor (\(R^{2}=0.03063\)-0) in the SLR model and has a low correlation strength with CS (barely a weakly positive one). With no data-model fitness, the model using T as the independent variable loses explanation power, despite the fact it was significant (\(p=0.003916<0.05\)), because the hydration effect  
Figure 6: The six bivariate MLR models in the 3D space. (**a**) Model using SR-BJPA and T to predict CS. (**b**) Model using SR-BJPA and the W/B ratio to predict CS. (**c**) Model using SR-BJPA and #days to predict CS. (**d**) Model using T and the W/B ratio to predict CS. (**e**) Model using T and #days to predict CS. (**f**) Model using T and #days to predict CS. should increase with an increase in the surrounding temperature. Using this model for making numerical predictions would be problematic. By contrast, _SR-BJPA contributes substantially to the explanatory power of the bivariate MLR models_: the \(R^{2}\) is 0.6632 or 0.5086 when combined with #days or the W/B ratio as the predictors, respectively, and \(R^{2}>0.4\) (i.e., 0.4162) when combined with T. Thus, when BJJPA is combined with any other variable, it forms a bifactorial model with sufficient explanatory power. The reason behind this can be easily identified in the SLR models: the SR-BJPA has the most stand-alone explanatory power for the variance observed in the CS data (\(R^{2}\) = 0.3791, compared to all other univariate models, the most significant SLR model with \(p\) = 2.2 \(\times\) 10\({}^{-16}\)). Moreover, SR-BJPA correlates with CS to the largest extent (\(r\) = 0.3791) from the beginning. _The #days factor can also contribute substantially_. For the context of this study, even when BJPA is added, even extra curing heat is supplied, and even the W/B ratio is adjusted, no violation of the pozzolanic reaction theory is observed (i.e., the CS development pattern does not deviate from normal cases). That is, this predictor is effective just because of the strong theoretical support. Besides the effectiveness of this factors, our findings also reflect an argument frequently encountered in the literature: _the relationship between the CS and #days should be logarithmic_. This study provides clear evidence for this argument in Section 3.2.1. Finally, _using bivariate MLR models seems good to make predictions for CS in future practise_. Among the four univariate SLR models, SR-BJPA and age provide more explanatory power than T and the W/B ratio. The situation improves when two variables are combined as predictors together: the explanatory powers of five of the six bivariate MLR models can be or may be accepted according to normal scientific standards (\(\sim\)=0.4 or >0.4). This is the reason why any models with more variables are unnecessary (i.e., neither the four tri-variate models nor the full model with all variables). _The T and W/B ratio combination cannot be used as an effective predictor_ because the model established based on which proves to be the only unacceptable bivariate MLR model that is having extremely low data-model fitness (Table 10).