Bayesian Probabilistic Framework for Damage Identification of Steel Truss Bridges under Joint Uncertainties
The vibration-based structural health monitoring has been traditionally implemented through the deterministic approach that relies on a single model to identify model parameters that represent damages. When such approach is applied for truss bridges, truss joints are usually modeled as either simple...
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| Format: | Article |
| Language: | English |
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Wiley
2013-01-01
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| Series: | Advances in Civil Engineering |
| Online Access: | http://dx.doi.org/10.1155/2013/307171 |
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| author | Wei Zheng Yi Yu |
| author_facet | Wei Zheng Yi Yu |
| author_sort | Wei Zheng |
| collection | DOAJ |
| description | The vibration-based structural health monitoring has been traditionally implemented through the deterministic approach that relies on a single model to identify model parameters that represent damages. When such approach is applied for truss bridges, truss joints are usually modeled as either simple hinges or rigid connections. The former could lead to model uncertainties due to the discrepancy between physical configurations and their mathematical models, while the latter could induce model parameter uncertainties due to difficulty in obtaining accurate model parameters of complex joint details. This paper is to present a new perspective for addressing uncertainties associated with truss joint configurations in damage identification based on Bayesian probabilistic model updating and model class selection. A new sampling method of the transitional Markov chain Monte Carlo is incorporated with the structure’s finite element model for implementing the approach to damage identification of truss structures. This method can not only draw samples which approximate the updated probability distributions of uncertain model parameters but also provide model evidence that quantify probabilities of uncertain model classes. The proposed probabilistic framework and its applicability for addressing joint uncertainties are illustrated and examined with an application example. Future research directions in this field are discussed. |
| format | Article |
| id | doaj-art-3b1d2050b0cd4f91bdc296cdba890419 |
| institution | Kabale University |
| issn | 1687-8086 1687-8094 |
| language | English |
| publishDate | 2013-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Civil Engineering |
| spelling | doaj-art-3b1d2050b0cd4f91bdc296cdba8904192025-02-03T06:05:22ZengWileyAdvances in Civil Engineering1687-80861687-80942013-01-01201310.1155/2013/307171307171Bayesian Probabilistic Framework for Damage Identification of Steel Truss Bridges under Joint UncertaintiesWei Zheng0Yi Yu1Department of Civil and Environmental Engineering, Jackson State University, 1400 J. R. Lynch Street, P.O. Box 17068, Jackson, MS 39217, USADepartment of Civil and Environmental Engineering, Jackson State University, 1400 J. R. Lynch Street, P.O. Box 17068, Jackson, MS 39217, USAThe vibration-based structural health monitoring has been traditionally implemented through the deterministic approach that relies on a single model to identify model parameters that represent damages. When such approach is applied for truss bridges, truss joints are usually modeled as either simple hinges or rigid connections. The former could lead to model uncertainties due to the discrepancy between physical configurations and their mathematical models, while the latter could induce model parameter uncertainties due to difficulty in obtaining accurate model parameters of complex joint details. This paper is to present a new perspective for addressing uncertainties associated with truss joint configurations in damage identification based on Bayesian probabilistic model updating and model class selection. A new sampling method of the transitional Markov chain Monte Carlo is incorporated with the structure’s finite element model for implementing the approach to damage identification of truss structures. This method can not only draw samples which approximate the updated probability distributions of uncertain model parameters but also provide model evidence that quantify probabilities of uncertain model classes. The proposed probabilistic framework and its applicability for addressing joint uncertainties are illustrated and examined with an application example. Future research directions in this field are discussed.http://dx.doi.org/10.1155/2013/307171 |
| spellingShingle | Wei Zheng Yi Yu Bayesian Probabilistic Framework for Damage Identification of Steel Truss Bridges under Joint Uncertainties Advances in Civil Engineering |
| title | Bayesian Probabilistic Framework for Damage Identification of Steel Truss Bridges under Joint Uncertainties |
| title_full | Bayesian Probabilistic Framework for Damage Identification of Steel Truss Bridges under Joint Uncertainties |
| title_fullStr | Bayesian Probabilistic Framework for Damage Identification of Steel Truss Bridges under Joint Uncertainties |
| title_full_unstemmed | Bayesian Probabilistic Framework for Damage Identification of Steel Truss Bridges under Joint Uncertainties |
| title_short | Bayesian Probabilistic Framework for Damage Identification of Steel Truss Bridges under Joint Uncertainties |
| title_sort | bayesian probabilistic framework for damage identification of steel truss bridges under joint uncertainties |
| url | http://dx.doi.org/10.1155/2013/307171 |
| work_keys_str_mv | AT weizheng bayesianprobabilisticframeworkfordamageidentificationofsteeltrussbridgesunderjointuncertainties AT yiyu bayesianprobabilisticframeworkfordamageidentificationofsteeltrussbridgesunderjointuncertainties |