In this paper Magnetic Charged System Search (MCSS) and Particle Swarm Optimization (PSO) are applied to the problem of damage detection using frequencies and mode shapes of the structures. The objective is to identify the location and extent of multi-damage in structures. Both natural frequencies and mode shapes are used to form the required objective function. To moderate the effect of noise on measured data, a penalty approach is applied. A variety of numerical examples including two beams and two trusses are considered. A comparison between the PSO and MCSS is conducted to show the efficiency of the MCSS
in finding the global optimum. The results show that the present methodology can reliably identify damage
scenarios using noisy measurements and incomplete data.

Damage detection based on MCSS and PSO using modal data

The use of changes in dynamically measured flexibility matrices to detect structural damage has received considerable attention during the last years. A new flexibility-based method is proposed in this study to provide an insight to the characterizations of structural damage. The presented method makes use of the eigenparameter decomposition of structural flexibility change and approaches the damage identification problem in a decoupled manner. First, a theory is developed to determine the number of damaged elements according to the number of non-zero eigenvalues of structural flexibility change. With the number of damaged elements determined, the localization and quantification algorithms are then developed. The proposed method is applied to three numerical examples and its efficiency is demonstrated through damage simulations. Copyright © 2008 John Wiley & Sons, Ltd.

Damage identification by the eigenparameter decomposition of structural flexibility change

Response surface methodology for damage detection using frequency and mode shape

In this paper, damage detection, localization and quantification are performed using modal strain energy change ratio (MSEcr) as damage indicator combined with a new optimization technique, namely slime mould algorithm (SMA) developed in 2020. The SMA algorithm is employed to assess structural damage and monitor structural health. Two structures, including a laboratory beam and a bar planar truss are considered to study the effectiveness of the proposed approach. Another recent algorithm called marine predators algorithm (MPA) is also used for comparison purposes with SMA. The MSEcr is utilized in the first stage to predict the location of the damaged elements. Single and multiple damages cases are analysed based on different number of modes to study the sensitivity of the proposed indicator to the total number of modes considered in the analysis. Next, this indicator is used as an objective function in a second stage to solve the inverse problem using SMA and MPA for damage quantification of the elements identified in the first stage. Experimental validation is conducted using a 3D frame structure with four stories that have damaged components. It is demonstrated that the proposed approach, using MSEcr and SMA, provides superior results for the considered structures. The effectiveness of this technique is tested by introducing a white Gaussian noise with different levels, namely 2% and 4%. The results show that the provided approach can predict the location and level of damage with high accuracy after introducing the noise.

Inverse problem for dynamic structural health monitoring based on slime mould algorithm

Based on the new conception of natural frequency vector (NFV) and natural frequency vector assurance criterion (NFVAC), a new damage detection method using the natural frequencies as damage feature is described in detail. For a specific structure, a series of damage states can be simulated based on the finite element model of the intact structure. Then, the NFVs of these damaged structures can be calculated and used to build the damage feature database of the structure. At last, the NFVAC, which is defined by the relationship of NFVs in the damage feature database and the NFV measured on the actual structure are used as damage index. An 8-story shear frame model is adopted as an example to verify the feasibility and validity of the proposed method. Both numerical simulations and model experiments for damage detection are performed, the results of damage detection for several damage cases demonstrate that using NFVAC as damage index, the damage location can be identified correctly and the damage extents ca...

Structural Damage Detection by Changes in Natural Frequencies

Structural damage identification based on residual force vector

A coupled method for structural damage identification

A new structural damage identification method

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Structural damage identification based on residual force vector

A coupled method for structural damage identification

A new structural damage identification method