Inverse analysis and optimization-based model updating for structural damage detection

Inverse analysis and optimization-based model updating for structural damage detection

Paper in proceedings (conference paper)

Structural health monitoring and early detection of structural damage is extremely important to maintain and preserve the service life of civil engineering structures. Identification of structural damage is usually performed using non-destructive vibration experiments combined with a mathematical procedure called model updating. The finite element model of the investigated structure is updated by incrementally adjusting its parameters so that the model responses gradually approach those of the real possibly damaged structure under investigation. This paper describes the use of two model updating methods. The first method employs metaheuristic optimization technique aimed multilevel sampling to efficiently search the design parameter space to achieve the best match between the deformed structure and its model. The second method approaches model updating as an inverse problem and uses machine learning-based model to approximate inverse relationship between structural response and structural parameters. Both methods are applied to damage identification of single- and double-span steel trusses. Finally, initial results of the hybrid method are presented. The effect of the damage rate and location on the identification speed and the accuracy of the solution is investigated and discussed.

Structural health monitoring and early detection of structural damage is extremely important to maintain and preserve the service life of civil engineering structures. Identification of structural damage is usually performed using non-destructive vibration experiments combined with a mathematical procedure called model updating. The finite element model of the investigated structure is updated by incrementally adjusting its parameters so that the model responses gradually approach those of the real possibly damaged structure under investigation. This paper describes the use of two model updating methods. The first method employs metaheuristic optimization technique aimed multilevel sampling to efficiently search the design parameter space to achieve the best match between the deformed structure and its model. The second method approaches model updating as an inverse problem and uses machine learning-based model to approximate inverse relationship between structural response and structural parameters. Both methods are applied to damage identification of single- and double-span steel trusses. Finally, initial results of the hybrid method are presented. The effect of the damage rate and location on the identification speed and the accuracy of the solution is investigated and discussed.

Damage identification, Model updating, Artificial neural network, Aimed multilevel sampling, Structural vibration, Modal parameters

Damage identification, Model updating, Artificial neural network, Aimed multilevel sampling, Structural vibration, Modal parameters

LEHKÝ, D., ŠPLÍCHAL, B., LAMPEROVÁ, K., SLOWIK, O.,

2025

25.09.2023

Ernst & Sohn

Berlin, Germany

EUROSTRUCT 2023 - European Association on Quality Control of Bridges and Structures: Digital Transformation in Sustainability

2509-7075

ce/papers

6

5

Federal Republic of Germany

1228

1233

6

https://onlinelibrary.wiley.com/doi/epdf/10.1002/cepa.2136

Lehky_EuroStruct2023-Inverse Analysis and Optimization-based Model Updating for Structural Damage Detection