Inverse problem for dynamic structural health monitoring based on slime mould algorithm
Tiachacht, S.; Khatir, S.; Thanh, C.L.; Rao, R.V.; Mirjalili, S.; Abdel Wahab, M. (2022). Inverse problem for dynamic structural health monitoring based on slime mould algorithm. Engineering With Computers 38: 2205-2228. https://hdl.handle.net/10.1007/s00366-021-01378-8
In: Engineering With Computers. Springer: New York. ISSN 0177-0667; e-ISSN 1435-5663, more
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| Author keywords |
Modal strain energy; Damage indicator; Slime mould algorithm; Marine predators algorithm; Inverse problem; Modal analysis; Damage assessment |
| Authors | | Top |
- Tiachacht, S.
- Khatir, S., more
- Thanh, C.L.
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- Rao, R.V.
- Mirjalili, S.
- Abdel Wahab, M., more
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| Abstract |
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. |
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