## 5 Results and Discussion  
In the previous research [44], the neural network algorithm was used to estimate the compressive strength of cement concrete for each period of 28 days. In that research, a neural network was designed consisting of three layers: the first layer was the \"input layer\", which contained twenty neurons; then, the second layer was the \"hidden layer\", which consisted of ten neurons; the third layer was the output layer, which consisted of five neurons. The algorithm used in training the neural network was the rapid deployment algorithm, and the number of training sessions for this network was 1000 training sessions, where in each training session, the weights of neurons within the network layers were adjusted in order to reach the optimal solution and reduce errors to the lowest possible extent. The same data of experimental mixtures were entered into the network (163 mixtures from the experimental works of the laboratories of the faculty of civil engineering at the University of Aleppo and a prefabricated concrete factory of the industrial region of Aleppo City in Syria). The used input data of the neural network were temperature, slump, and cement grade, and the output was the density and the compressive strength at 7 and 28 days of concrete. The data of experimental mixtures were divided into three groups: The first group, containing 111 mixtures of data, was used to train the network. The second group, containing 25 mixtures of data, was used to audit the training results. The third group, containing 25 mixtures of data, was used to test and to ensure that the neural network received sufficient training and was able to show satisfactory results. As the correlation coefficient is quite low, R\({}^{2}\) = 0.875 (even though it is acceptable regarding other research [45; 46; 47]), the results presented in Figure 4 indicate that the selected  
Figure 3: Statistical distribution of database values. ANN technique does not fit the experimental data well in terms of compressive strength; there was a need to use different AI techniques, and as for the reason mentioned above, the authors selected the BPSO and then improved it to NPBPSO. The results of the neural networks algorithm, related to estimating the resistance of concrete for 28 days, showed that there was a shift between the experimental values (in the laboratory) and the values generated by the neural networks with a capacity of (1.91 Mpa). We also notice that there is a large dispersion in the values generated by the neural network algorithm. Figure 5 presents experimental compressive strength measurements and BPSO results obtained after 7 and 28 days, respectively. The predicted compressive strength values at the age of 7 and 28 days have been found to be extremely close to those experimentally achieved with a shift of (0.01112 MPa). Figure 4: Comparison of the neural network results with the real values of the test data in the resistance condition after 28 days. Figure 5: Experimental versus predicted compressive strength using BPSO (**a**) after 7 days, (**b**) after 28 days. Figure 6 shows the differences between experimental compressive strength values and others resulting from applying the new procedural binary particle swarm optimization NPBPSO after 7 and 28 days, respectively, where it has been noticed that most of the values resulting from NPBPSO matched the experimental ones. As for the points that did not match, the shift ratio reached (0.00798 MPa); in other words, the accuracy of the values resulting from artificial intelligence has been improved by almost 30%. Finally, Table 2 shows an improvement in the results after using the improved version of the algorithm (NPBPSO), as shown in the last column of the table. Figure 7 shows the differences between average density and others resulting from applying the new procedural binary particle swarm optimization NPBPSO after 28 days, where it has been noticed that most of the values resulting from NPBPSO matched the experimental ones.