Structural Damage Detection Based on Sparrow Search Algorithm

Vibration modal parameters are widely used in structural damage identification due to their ease of measurement, facilitating rapid structural damage assessment in engineering practice. However, traditional vibration-based methods impose stringent requirements on data volume and accuracy, leaving room for improvement in computational efficiency and precision. To address this issue, a damage identification method combining the Sparrow Search Algorithm (SSA) and natural frequency sensitivity analysis is proposed. This method employs the SSA to solve the linear equations of frequency sensitivity to obtain structural damage parameters, thereby enabling structural damage assessment. The advantages of the proposed method are as follows: firstly, the SSA, compared to other swarm intelligence algorithms, can more accurately solve for damage parameters; secondly, sensitivity analysis is used to predefine the search area, thereby enhancing computational efficiency; furthermore, a conversion formula is applied to enhance computational accuracy in cases of significant damage. Three numerical cases and two experimental examples are used to validate the proposed algorithm, which is also compared with other swarm intelligence algorithms. The research results indicate that this method has significant advantages in locating damage and accurately assessing damage severity, with minimal misjudgment of undamaged units. The calculated damage parameters are closer to the true values compared to those obtained by other methods.