Updating Finite Element Model Using Stochastic Subspace Identification Method and Bees Optimization Algorithm

Many transmission towers have collapsed under typhoons in recent years, mainly due to the unclear behaviors of their structural properties, introducing many deficiencies in the design process. Therefore, implementing structural health monitoring is of great importance for investigating the structural features of large-span transmission lines. This study develops a stochastic subspace identification method to identify the modal parameters of transmission towers, and a finite element model of a transmission tower-line system is established based on a case in Guangdong Province, China. Moreover, a MATLAB program is written using the stochastic subspace identification method to perform a modal analysis on the wind-induced responses of a transmission tower, and the results are compared with those of the finite element model to verify the program’s reliability. A structural health monitoring system installed on a transmission tower recorded the wind field data around the tower and its vibration responses during Typhoon Khanun. The characteristics of the typhoon wind field and the changes in the acceleration responses under different wind speeds were calculated, and the developed stochastic subspace identification method was used to identify the frequencies and damping ratios of the tower. The results show that the identified frequencies under different wind speeds in the longitudinal and transverse directions remain essentially unchanged, indicating that the monitoring tower was safe and suffered no damage during Typhoon Khanun. The damping ratios of the monitoring tower range from 1% to 4%, where the larger values may be caused by bolt slippage.

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Vibration-based damage localization and quantification in a pretensioned concrete girder using stochastic subspace identification and particle swarm model updating

Structural Health Monitoring ◽

10.1177/1475921718820015 ◽

2019 ◽

Vol 19 (2) ◽

pp. 587-605 ◽

Cited By ~ 10

Author(s):

Alessandro Cancelli ◽

Simon Laflamme ◽

Alice Alipour ◽

Sri Sritharan ◽

Filippo Ubertini

Keyword(s):

Finite Element ◽

Finite Element Model ◽

Stiffness Matrix ◽

Particle Swarm ◽

Element Model ◽

Subspace Identification ◽

Reduced Order ◽

Stochastic Subspace Identification ◽

Modal Properties ◽

Pretensioned Concrete

A popular method to conduct structural health monitoring is the spatio-temporal study of vibration signatures, where vibration properties are extracted from collected vibration responses. In this article, a novel methodology for extracting and analyzing distributed acceleration data for condition assessment of bridge girders is proposed. Three different techniques are fused, enabling robust damage detection, localization, and quantification. First, stochastic subspace identification is used as an output-only method to extract modal properties of the monitored structure. Second, a reduced-order stiffness matrix is reconstructed from the stochastic subspace identification data using the system equivalent reduction expansion process. Third, a particle swarm optimization algorithm is used to update a finite element model of the bridge girder to match the extracted reduced-order stiffness matrix and modal properties. The proposed approach is first verified through numerically simulated data of the girder and then validated using experimental data obtained from a full-scale pretensioned concrete beam that experienced two distinct states of damage. Results show that the method is capable of localizing and quantifying damages along the girder with good accuracy, and that results can be used to create a high-fidelity finite element model of the girder that could be leveraged for condition prognosis and forecasting.

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