## 6 Conclusions  
By comparing the results shown by the neural networks algorithm to obtain the compressive strength of concrete for 7 days and 28 days, respectively, (1.413 MPa--7 days and 1.91 MPa--28 days) and the results shown by the binary particle swarm algorithm BPSO (0.01112) MPa--7 and 28 days), the great superiority of the BPSO algorithm over the neural networks algorithm appears in two main important points: the first is the number of cycles or iterations that the algorithm needs to reach the results (i.e., the time to obtain the results), and the second point is the accuracy of the results that the algorithm shows. The binary particle swarm algorithm was developed with a new procedure (NPBPSO) in order to estimate the strength of concrete and the compressive strength. Through this development, we were able to achieve two goals. One is to prevent the algorithm from falling into the problem of local solutions that the algorithm can fall into. The other is that the speed of the algorithm reaches the desired optimal solution. After that, the new algorithm (NPBPSO) was converted into a computer program using a high-level programming language, where the data of the experimental compressive strength tests of the concrete mixtures that were conducted in the laboratories of the Faculty of Civil Engineering at the University of Aleppo and the ready-mixed concrete factory were entered. BPSO and NPBDSO compressive strength and density outputs were obtained. With an average shift (0.01112 MPa) in compressive strength, it was discovered that the solutions obtained using the binary particle swarm technique are close to the experimental values. However, the new procedural binary particle swarm algorithm (0.00798 MPa) shows a shift from the experimental values with approximately 30% improvement in accuracy. The density results for BPSO and NBPSO show a shift rate of 2.311 kg/m\({}^{3}\) and 1.001 kg/m\({}^{3}\), respectively, with an accuracy improvement of about 50%. As a result of this research, it can be found that both BPSO and NBPSO techniques provide good results with better accuracy than NPBPSO because of the modification made to avoid local solutions and reduce the number of iterations. The intensity results for both BPSO and NBPSO show a significant improvement in the speed of [PERSON]'s access to optimal solutions, as the number of iterations needed \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Standard**} & \multirow{2}{*}{**Variance**} & \multirow{2}{*}{**R\({}^{2}\)**} & \multicolumn{2}{c}{**Experimental/**} \\  & & & & **Predicted Average** \\ \hline \multirow{2}{*}{7 days} & BPSO & 5.26 & 27.66 & 0.999451 & 1.0005 \\ \cline{2-6}  & NPBPSO & 5.26 & 27.66 & 0.999423 & 1.0004 \\ \hline \multirow{2}{*}{28 days} & BPSO & 7.24 & 52.52 & 0.999653 & 1.0003 \\ \cline{2-6}  & NPBPSO & 7.24 & 52.52 & 0.999784 & 1.0002 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Statistics evaluation of compressive strength results. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Standard**} & \multirow{2}{*}{**Variance**} & \multirow{2}{*}{**R\({}^{2}\)**} & \multicolumn{2}{c}{**Experimental/**} & \multirow{2}{*}{**Execution**} \\  & & & & & **Predicted Average** & **Time** \\ \hline BPSO & 28.77 & 828.12 & 0.992211 & 1.0010 & 60 iterations \\ \hline NPBPSO & 28.79 & 829.36 & 0.998454 & 1.0004 & 25 iterations \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Statistics evaluation of density results. to reach the optimal solution in the version (BPSO) decreased from (60 iterations) to (25 iterations) in the modified version of the algorithm (NPBPSO). Finally, it can be found that BPSO and NBPSO AI techniques are good at predicting some properties of concrete such as compressive strength and density, which means that they can be highly recommended due to their speed, accuracy, and low cost. Conceptualization, F.A. and G.W. ; methodology, M.B.H. and F.A. ; software--Algorithm development, M.B.H. ; validation, G.W. and M.B.H. ; formal analysis, M.B.H.