## 3 Results and Discussion
### Strength Prediction and Relationship Analysis
#### 3.4.1 Model Parameter  
Preparation comprises: (i) computing yields from input information; (ii) looking at the deliberate and ascertained yields; and (iii) altering the weights for every node to diminish the distinction between the estimated and computed values. The exactness of the forecasts of a system was measured by \begin{table}
\begin{tabular}{c c c c c c} \hline
**Title** & **Description** & **Mean** & **Std Dev** & **Min** & **Max** \\ \hline Temp (\({}^{\circ}\)C) & Temperature at which concrete is treated & 154.42 & 139.55 & 27 & 400 \\ Days & Curing Days & 23 & 8.15 & 7 & 28 \\ \% Replacement & \% replacement of Cement & 0.12 & 0.08 & 0 & 0.25 \\ Additive Type & 1-SF2-GGBFS,3-MP & & & 1 & 3 \\ Comp Strength & Compressive Strength(Mpa) & 21.36 & 4.84 & 13.8 & 31.9 \\ Tensile Strength & Tensile Strength (Mpa) & 1.70 & 0.74 & 0.41 & 3.09 \\ Flexural Strength & Flexural Strength (Mpa) & 2.49 & 1.01 & 0.4 & 4.6 \\ \hline \end{tabular}
\end{table} 
Table 7: Descriptive statistics of parameters for self compacted concrete. Figure 6: Artificial Neural Networks (ANNs) structure for self-compacted concrete. the foundation of the root mean squared error (RMSE), between the actual and the predicted values, mean absolute error (MAE) and the various coefficients of determination (R\({}^{2}\)) [11]. \[\text{RMSE}=\sqrt{(\frac{1}{\text{N}}\sum_{n=1}^{\text{N}}(\text{actual}-\text{ predicted})^{2})} \tag{1}\]  
\[\text{R}^{2}=1-\frac{\text{SSE}}{\text{SSy}} \tag{2}\]  
where SSE is sum of squared errors of prediction and SSy is total variation. Mean absolute error is similar to root mean square except using absolute difference instead of squared difference. Usually, performance of a model is compared by coefficient of determination (R\({}^{2}\)). A classic fit would bring about a R\({}^{2}\) of 1 and poor fit almost 0 [11]. The plan of a manufactured neural system requires the assurance of appropriate engineering. A back-propagation neural system based demonstration calculation requires setting up various learning parameters (e.g., learning rate, momentum, etc. ), the ideal number of nodes in the hidden layer and the quantity of hidden layers to have a less unpredictable system with a moderately better prediction capacity. In most applications, choice of various hidden layers and the quantity of nodes in hidden layer is finished by utilizing a general guideline or attempting a few subjective structures and choosing one that gives the best execution [11]. Further, a reasonable estimation of parameters such as learning rate is likewise required for chosen hidden layers and nodes. The acknowledgment/dismissal of the model created are resolved by its capacity to anticipate the quality of SCC. Additionally, an effectively prepared model is portrayed by its capacity to foresee quality esteems for the information it was prepared on [11]. A five-folded (k-folded) cross validation is utilized to foresee the quality for the informational collection utilized. Cross-validation is a technique for showing the exactness of an arrangement. The informational index is separated into a few parts (a number characterized by the client), with each part used to test a model fitted to the rest [11]. The relationship coefficient, root mean square error (RMSE) and mean absolute error is utilized to judge the execution of the neural system approach in anticipating the quality. Measurable techniques are regularly utilized as part of the advancement of exact connections between different interfacing factors [11]. This is usually difficult, especially for nonlinear connections. Likewise, to find the measurable model, the essential parameters must be known. By correlation, the displaying procedure in back-propagation neural systems is more straightforward, as there is no need to determine a scientific connection between the input and output factors. Neural systems can be compelled for breaking down a framework containing various factors, to predict the unknown features or properties [11].