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Bayesian updating of truss structures using OpenSees
- 1 School of Construction Engineering, Dalian University of Technology, China
* Author to whom correspondence should be addressed.
Abstract
The study of Bayesian updating of truss structures using The Open System for Earthquake Engineering Simulation (OpenSees) can not only promote the application of Bayesian methods in structural engineering, but also provide a strong support for structural health monitoring in practical engineering. This paper proposes a method of Bayesian updating of truss structures using OpenSees to improve the accuracy of structural analysis and prediction. Based on OpenSees, a finite element model with a truss structure as an example is established, the basic parameters such as nodes, units, material properties and boundary conditions are defined, and the prior distributions of the initial parameters are set by empirical judgment. After collecting observational data such as structural response, Bayes' theorem is utilized to combine the a priori distribution with the observational data to update the parameter estimates. The posterior estimates of the parameters are obtained by constructing the likelihood function and applying the Monte Carlo method to sample from the posterior distribution. Numerical simulations are performed using the updated parameters, and the simulation results are compared with the observed data to verify the accuracy and reliability of the model. Finally, structural damage identification is performed based on the updated model. The methodology in this paper provides a systematic framework to dynamically update and optimize the truss structural model by continuously incorporating new data, significantly improving the prediction's accuracy and reliability.
Keywords
OpenSees, Truss structure, Bayesian updates, Structural damage identification
[1]. Wang X 2018 Modern shift (11) pp 25.
[2]. Huang Z 2022 Human Rights Research (04) pp 65-92.
[3]. Xiang J 2024 China Real Estate News (004).
[4]. Wang N R 2023 Construction Technology (in English) 52(21) pp 33-40.
[5]. Tan J L, Fang S E and Jiang X 2023 Building Structure 53(16) pp 91-97.
[6]. Song C L 2022 Tongji University.
[7]. Fu Z Y, Yin X Q and Wang G X 2023 Journal of Disaster Prevention and Mitigation Engineering 43(06) PP 1463-1475.
[8]. Xiao X, Wang Z Y and Li Q W, et al 2022 Engineering Mechanics 39(S1) pp 336-341.
[9]. Liang F Z, Song Z J and Zhang Y H 2004 Tianjin:Tianjin University Press.
[10]. Jiu Z T and Wang J C 2024 Urban Roads and Bridges and Flood Control (04) pp 228-233+25.
Cite this article
Wang,Z. (2024).Bayesian updating of truss structures using OpenSees.Applied and Computational Engineering,91,33-40.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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Volume title: Proceedings of the 2nd International Conference on Functional Materials and Civil Engineering
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