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Statistical Damage Localization with Stochastic Load Vectors Using Multiple Mode Sets

Md Delwar Hossain Bhuyan 1 Michael Döhler 1 Laurent Mevel 1 
1 I4S - Statistical Inference for Structural Health Monitoring
IFSTTAR/COSYS - Département Composants et Systèmes, Inria Rennes – Bretagne Atlantique
Abstract : The Stochastic Dynamic Damage Locating Vector (SDDLV) method is an output-only damage localiza-tion method based on both a Finite Element (FE) model of the structure and modal parameters estimated from output-only measurements in the damage and reference states of the system. A vector is obtained in the null space of the changes in the transfer matrix computed in both states and then applied as a load vector to the model. The damage localization is related to this stress where it is close to zero. In previous works an important theoretical limitation was that the number of modes used in the computation of the transfer function could not be higher than the number of sensors located on the structure. It would be nonetheless desirable not to discard information from the identification procedure. In this paper, the SDDLV method has been extended with a joint statistical approach for multiple mode sets, overcoming this restriction on the number of modes. The new approach is validated in a numerical application, where the outcomes for multiple mode sets are compared with a single mode set. From these results, it can be seen that the success rate of finding the correct damage localization is increased when using multiple mode sets instead of a single mode set.
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Submitted on : Monday, July 11, 2016 - 2:34:07 PM
Last modification on : Friday, June 17, 2022 - 1:27:54 PM
Long-term archiving on: : Wednesday, October 12, 2016 - 1:47:19 PM


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  • HAL Id : hal-01344206, version 1



Md Delwar Hossain Bhuyan, Michael Döhler, Laurent Mevel. Statistical Damage Localization with Stochastic Load Vectors Using Multiple Mode Sets. EWSHM - 8th European Workshop on Structural Health Monitoring, Jul 2016, Bilbao, Spain. ⟨hal-01344206⟩



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