  This paper focuses on exploring efficient ways to find $\mathcal{H}_2$
optimal Structure-Preserving Model Order Reduction (SPMOR) of the second-order
systems via interpolatory projection-based method Iterative Rational Krylov
Algorithm (IRKA). To get the reduced models of the second-order systems, the
classical IRKA deals with the equivalent first-order converted forms and
estimates the first-order reduced models. The drawbacks of that of the
technique are failure of structure preservation and abolishing the properties
of the original models, which are the key factors for some of the physical
applications. To surpass those issues, we introduce IRKA based techniques that
enable us to approximate the second-order systems through the reduced models
implicitly without forming the first-order forms. On the other hand, there are
very challenging tasks to the Model Order Reduction (MOR) of the large-scale
second-order systems with the optimal $\mathcal{H}_2$ error norm and attain the
rapid rate of convergence. For the convenient computations, we discuss
competent techniques to determine the optimal $\mathcal{H}_2$ error norms
efficiently for the second-order systems. The applicability and efficiency of
the proposed techniques are validated by applying them to some large-scale
systems extracted form engineering applications. The computations are done
numerically using MATLAB simulation and the achieved results are discussed in
both tabular and graphical approaches.
