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With the development of the technology and increase of human dependency on structures, healthy structures play an important role in people lives and communications. Hence, structural health monitoring has been attracted strongly in recent decades. Improvement of measuring instruments made signal processing as a powerful tool in structural heath monitoring. Wavelet transform invention causes a great evolution in signal processing. Wavelet transform decomposes a signal into several groups based on scaled and translated basic functions. In this study, a novel methodology based on wavelet transform using complex Morlet wavelet has been introduced for system identification. This process includes a multivariable constrained optimization problem for selecting suitable complex Morlet wavelet. Using selected wavelet, modal parameters and flexibility matrix of structure can be estimated properly. Because of small modal participation of higher mode using finite number of modes leads to flexibility matrix with acceptable accuracy. Since damages cause change in structural properties, a damage index based on flexibility matrix has been applied and its performance has been investigated in some structures.

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Sound Energy Optimizer (SEO) is a recent metaheuristic algorithm inspired by the propagation and reception of sound waves in physical environments. While conventional metaheuristics that rely on random number generators with certain distributions, SEO can utilize various real-world or simulated sound signals as the source of stochasticity to guide its search process. Concerning structural design by SEO, the effect of natural sound signals is compared with the artificial signals generated from uniform or normal distributions. In this regard, a 244-bar power transmission tower and a 1016-bar double-layer grid are simultaneously optimized with continuous geometry as well as discrete sizing variables to evaluate the impact of input signals on convergence behavior, solution quality and robustness of the algorithm. A sensitivity analysis is conducted to calibrate key control parameters of SEO. The results declare that the nature of the input sound signal can significantly affect the algorithm’s exploration-exploitation balance. In this study, the "Knocking sound" signal yields the best performance, while the synthetic random signals revealed less stable optimization trajectories.