## 4 The Compressive Strength and Resistivity Prediction Model of the CLSM
### The Prediction Model of the Surface Resistivity  
Previous researchers used the power increase function to calculate the relationship between the surface resistivity and curing age and proposed a linear relationship between the compressive strength and resistivity ([PERSON] et al. (2012) [19]). [PERSON] and [PERSON] (2014) [20] used hyperbolic equations to evaluate the change of the concrete resistivity with the curing age. The results showed that the 28-day compressive strength increased nonlinearly with the increasing resistivity. However, the trend of the concrete resistivity with the increase of the specimen curing age became stable. In other studies, it was also shown that the compressive strength of the mortar at 28 days could be estimated from the resistivity of the 24-h sample, and a linear increase relationship was established ([PERSON] et al., 2012 [21]). Additionally, in 2010, [PERSON] and [PERSON] [22] used two methods for analysis between the surface resistivity and curing age. First, a hyperbolic equation was proposed to simulate the change in surface resistivity over time. Second, the exponential function was used to predict the relationship between the resistivity of the theoretical method model and the curing age. [PERSON] and [PERSON] (2010) [25] also used a hybrid design of controlled low-strength concrete using different water-binder ratios and concrete mixed with discarded LCD glass sand instead of different percentages of sand. It was found that the electrical resistivity increased with the increase of the curing age but decreased with the increase of the water-binder ratio. The resistivity increased as the  
Figure 10: The relationship between the compressive strength of the CLSM, DS content, and coefficient \(x\) on the 28 th day. percentage of glass sand substitutes increased. Therefore, based on the characteristics of the surface resistivity, this study considers the surface resistivity of CLSMs, including variables such as the curing age \(t\) and DS replacement rate DS, to infer the predicted model of CLSM resistivity. The results of past research indicate that the resistivity of waste glass concrete increases with the increase of waste glass ([PERSON] and [PERSON], 2010 [25]). Therefore, the prediction model in this study assumes that when the same mixing ratio is used to increase the DS content, the specific resistivity of the CLSM will increase. The model is deduced in the exponential function for use in predictive models and the relationship between surface resistivity and curing age, as shown in Figure 11. Where \(E_{r}\) is the surface resistivity and \(t\) is curing age. The deduced mathematical model in this study will assume that, when the same water-binder ratio is used, and the ratio of the DS replacement is increased and the resistivity of the CLSM increases. Figure 11a shows the test results of the resistivity \(E_{r}\) and the different curing ages of the CLSMs with different DS replacement ratio DS when the water-binder ratio (w/b) is 1.5. For the same DS replacement ratio DS, the resistivity \(E_{r}\) exhibits a slight nonlinear increase with the curing age \(t\). However, the nonlinear relationship is not obvious. As the content of the DS increases, the electrical resistivity also increases. Figure 11b shows that, in the semi-log scale coordinates, the relationship between the surface resistivity and curing age \(t\) has a significant nonlinear increase, and as the curing age increases, the increase becomes smooth. Therefore, the relationship between the resistivity and curing age is simulated using an exponential function equation, as shown in Equation (5).