*3.4. Sensitivity Analysis*

The purpose of this evaluation was to discover the impact of input factors on RAC's compressive and flexural strength prediction. The anticipated result is considerably influenced by the input factors [51]. Figure 14 shows the influence of the input factors used in this research on the compressive strength evaluation of RAC. The analysis revealed that the RCA replacement ratio was the crucial element, accounting for 18.7% of the overall impact, followed by parent concrete strength at 15.3% and weff/c at 14.8%. The contribution of the other input factors to the strength estimation of RAC was found to be lower, with the Los Angeles abrasion index of RCA, water absorption of RCA, a/c, nominal maximum RCA size, bulk density of RCA, Los Angeles abrasion index of natural aggregate, bulk density of the natural aggregate, nominal maximum natural aggregate size, and water absorption of the natural aggregate accounting for 11.6%, 8.7%, 8.1%, 6.5%, 5.0%, 3.7%, 2.8%, 2.5%, and 2.3%, respectively. Sensitivity analysis produced results associated with the quantity of input variables and the dataset used to build the machine learning models. The impact of an input factor on the method's results was found using Equations (3) and (4).

$$N\_i = f\_{\max}(\mathbf{x}\_i) - f\_{\min}(\mathbf{x}\_i) \tag{3}$$

$$S\_i = \frac{N\_i}{\sum\_{j=-i}^n N\_j}.\tag{4}$$

where

*fmax*(*xi*) = highest estimated value on the *i th* result; *fmin*(*xi*) = lowest estimated value on the *i th* result; *Si* = attained impact percentage for a certain variable.

**Figure 14.** Input variables' contribution to estimating the outcomes of models.
