Automated Model Updating of Historical Masonry Structures Based on Ambient Vibration Measurements
Erişim
info:eu-repo/semantics/closedAccessTarih
2018Erişim
info:eu-repo/semantics/closedAccessÜst veri
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Finite-element (FE) model updating is a very effective procedure for determining the uncertainty parameters in a structural model and minimize differences between experimentally and analytically identified dynamic characteristics. This procedure can be practiced with manual and global/local automatic model updating procedures. Automatic model updating is very popular because of its applicability to all kinds of engineering structures to minimize differences, and its damage localization and structural health monitoring. This paper considers a historical masonry bastion to obtain its dynamic characteristics numerically with the finite-element method and experimentally with ambient vibration tests (AVTs). Sensitivity analyses for the uncertainty parameters and manual and automated model updating to minimize differences are also performed. A castle bastion located in Trabzon, Turkey is selected as an application. The FE model of structure is developed in commercial software. The first three natural frequencies are obtained between 5 and 10Hz, and AVTs are performed under natural excitation using enhanced frequency domain decomposition (EFDD) and subspace structural identification (SSI) techniques. The first three natural frequencies are obtained between 4 and 7Hz. The comparison of analytically and experimentally identified dynamic characteristics shows a close agreement between mode shapes, but 26% difference in natural frequencies. To minimize the difference, the FE model of the bastion is updated using manual and global/local automated updating, changing its material properties of Young's modulus and material density. The maximum differences are reduced from 27 to 3% with manual updating, and to 0.02% with automated model updating.
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https://doi.org/10.1061/(ASCE)CF.1943-5509.0001108https://hdl.handle.net/20.500.12440/3424