Phd Degree / Doktora

Permanent URI for this collectionhttps://hdl.handle.net/11147/2869

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Subject: search.filters.subject.Bayesian statistical decision theory

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  • Doctoral Thesis
    Reliability Assessment Based on Structural Health Monitoring Data and Bayesian Updating of Structural Models
    (01. Izmir Institute of Technology, 2024) Uzun, Ertuğrul Türker; Aktaş, Engin; Hızal, Çağlayan
    Finite element (FE) models are commonly used in numerical modeling of structures, but their assumptions can lead to inaccuracies and uncertainties. To address this, FE model update methods have been developed, calibrating the model based on structural health monitoring (SHM) data. However, a general framework for realistic life cycle performance assessment of structures using monitored data has not yet been presented. Bayesian modeling can characterize uncertain structural parameters as random variables, but it is complex and time-consuming. Metamodeling techniques, which are effective stochastic predictors, can be used to decrease the computational burden of model updating. Adapting a Polynomial-Chaos-Kriging (PCK) metamodeling technique to Bayesian model updating in order to reduce uncertainty and circumvent computational challenges using SHM data in order to assess the reliability of structures more precisely is the objective of this research. Therefore, the effectiveness of the proposed method has been tried and demonstrated through experimental and numerical studies. An experimental study of a bridge column is used to evaluate the reliability of structures subjected to various corrosion effects. As a result, the proposed solution method reduces computational costs and enables an updated FE model to be closer to real structure measurements. The updated models are found to be more reliable in reliability evaluations, providing more accurate predictions on issues like structure safety, service life, and maintenance cost compared to non-updated models.