# Predicting Compressive Strength of 3D Printed Mortar in Structural Members Using Machine Learning
## 5 Results and Discussion  
Table 1 shows the adjustment parameters determined through the trial-and-error method prior to running the model. After fitting the model 10 times, the Pareto front depicted in Figure 13 is the best Pareto front. There are two axes in this graph: com-plexity and error. The OBJ value was utilized as the error in this study, while the com-plexity was defined as the number of connecting weights in the ANN. As it can be ob-served, there are five non-dominated grasshoppers on the Pareto front after generating 150,000 distinct ANNs, and the complexity is growing as the error reduces. In other words, a more accurate network comes with a more sophisticated structure. The arch-tectures of the simplest algorithm (ANNMOGOA-3) and more complex algorithm (ANNMOGOA-1) are shown in Figure 14, and their weights and biases are listed in the Appendix B. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Values** \\ \hline The total number of runs & 10 \\ Neurons’ maximum number in each hidden layer & 16 \\ The maximum hidden layers’ number & 3 \\ The size of repository & 50 \\ Maximum number of iterations & 100 \\ Agent’ number & 30 \\ Activation function neurons of hidden layers & Hyperbolic tangent sigmoid \\ Training algorithm of ANNs & Levenberg–Marquardt \\ Activation function neurons of output layer & Linear \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Adjustment parameters of the proposed model. Figure 12: Schematic illustration of basic mix materials and experimental test in 3 DP concrete. A collection of statistical indicators is utilized to compare the behavior of the var-ious networks. Aside from the statistical indicators indicated in the technique section, the following parameters are used: mean bias error (MBE), mean absolute percentage error (MAPE), and scatter index (SI). \[MBE=\frac{1}{D}\sum_{i=1}^{D}(R_{d}-O_{d}); \tag{19}\]  
\[MAPE=\frac{100}{D}\ \sum_{i=1}^{D}\frac{R_{d}-O}{R_{d}}; \tag{20}\]  
\[SI=RMSE/\overline{O} \tag{21}\]  
Figure 14: Architecture of ANNMOGOA-2 and ANNMOGOA-1. Figure 13: Non-dominated solutions (ANNs). \(O_{d}\) and \(O\) are the dth pattern's experimental and average value experimental outcomes, respectively, \(D\) is the total number of patterns, and \(R_{d}\) is the dth pattern's model result. If an ANN's SI value is greater than 0.3, it has \"poor performance,\"; between 0.2 and 0.3, it has \"fair performance\"; between 0.1 and 0.2, it has \"good performance\"; and for less than 0.1, it has \"excellent performance\", according to SI ([PERSON] et al., 2013). Table 2 also includes the designs and complexity of all five networks, as well as statistical indicators for ANNs. Table 2 shows that ANNMOGOA-2 is the simplest network with 6 connecting weights and no hidden layers, followed by ANNMOGOA-7 with a single neuron in its hidden layer and 8 linking weights. ANNMOGOA-6, ANNMOGOA-3, ANNMOGOA-5, and ANNMOGOA-4 have 15, 22, 71, and 85 connecting weights, respectively. With fif-teen neurons in its only hidden layer and a complexity score of 106, ANNMOGOA-1 is the most complicated network. On the other hand, ANNMOGOA-1 is the most accurate model, with an OBJ value of 4.12, while ANNMOGOA-2 is the least accurate model, with an OBJ value of 23.28; all other models are in between. The RMSE of ANNMOGOA-1 is 4.49 MPa, which is about 75% lower than that of ANNMOGOA-2 with a value of 18.69. Furthermore, ANNMOGOA-2 has an MAE value of 14.47 MPa, which is about six times that of ANNMOGOA-1. Furthermore, when it comes to MAPE, the most accurate model is ANNMOGOA-1, which has a MAPE value of 8.75 percent, followed by ANNMOGOA-5, which has a MAPE value of 10.37 percent. The following five models are ANNMOGOA-4, ANNMOGOA-3, ANNMOGOA-6, ANNMOGOA-7, and ANNMOGOA-2, which have MAPE values of 13.17 percent, 16.66 percent, 20.25 percent, 27.60 percent, and 52.40 percent, respectively. So, ANNMOGOA-2 is the least accurate model. The Pearson correlation coefficient (R) measures the similarity between anticipated and measured values.