Accounting for model form uncertainty in Bayesian calibration of linear dynamic systems

Ramancha, Mukesh K.; Conte, Joseph Le; Parno, Matthew

doi:10.1016/j.ymssp.2022.108871

Cited by 11 publications

(4 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Section: Numerical Simulation Resultsmentioning

confidence: 99%

“…Previous research has quantified the uncertainty in the model parameters by performing uncertainty propagation, sensitivity, Bayesian model updating, or reliability analyses to assess the effects of the uncertainty in the model parameters on the seismic response and performance of structures. 43,[45][46][47][48][49][50] They have explicitly considered the uncertainty in the model parameters by modeling it through suitable PDFs. They have demonstrated that considering this source of uncertainty introduces significant variability in the results of the seismic performance-based design and assessment of structures.…”

Section: Importance Of Uncertainty Quantificationmentioning

confidence: 99%

See 1 more Smart Citation

Reinforced concrete wall buildings with force‐limiting connections: Modeling effects and uncertainty propagation

Mayorga,

Tsampras

2024

Earthq Engng Struct Dyn

View full text Add to dashboard Cite

This paper assesses the effects of (1) the gravity load‐resisting system (GLRS) modeling approach, (2) the seismic force‐resisting system (SFRS) modeling approach, and (3) the uncertainty of the model parameters of the constitutive law of the longitudinal reinforcing steel of the SFRS on the seismic responses of a 12‐story reinforced concrete wall building with force‐limiting connections. This is achieved by conducting nonlinear numerical earthquake simulations. The seismic responses of the building models with force‐limiting connections using two GLRS modeling approaches, (1) a moment frame system and (2) a pin‐base lean‐on‐column system, are compared. The seismic responses of the building models with conventional connections and force‐limiting connections, respectively, using two SFRS modeling approaches, (1) a distributed‐plasticity modeling approach and (2) a lumped‐plasticity modeling approach, are compared. A joint probability density function for the ASTM‐A615 Grade 60 steel available in the literature is used to conduct an uncertainty propagation analysis through Monte Carlo simulation. The uncertainty in the steel model parameters is propagated to the seismic responses of the building models with conventional connections and force‐limiting connections, respectively. The distributions of the mean values of the peak structural responses of the building models are studied. The effects of the GLRS modeling approach on the seismic responses are not significant in the context of seismic performance‐based design and assessment of buildings with force‐limiting connections. The effects of the SFRS modeling approach and the uncertainty in the steel model parameters on the floor total acceleration and force responses are reduced by including force‐limiting connections.

show abstract

Section: Numerical Simulation Resultsmentioning

confidence: 99%

Section: Importance Of Uncertainty Quantificationmentioning

confidence: 99%

Reinforced concrete wall buildings with force‐limiting connections: Modeling effects and uncertainty propagation

Mayorga,

Tsampras

2024

Earthq Engng Struct Dyn

View full text Add to dashboard Cite

show abstract

“…This is because the relationship between the gap length 𝜃 and the strain measurements 𝑥 𝑧 is highly nonlinear and only available numerically through the finite element simulation. One can rely on numerical approximation techniques like Markov chain Monte Carlo (MCMC) methods or sequential Monte Carlo (SMC) methods to solve the inference problem (refer to [64,66,67,68]). However, these numerical techniques demand evaluation of the likelihood function 𝑓 𝑋 𝑧 |Θ (𝑥 𝑧 |𝜃) at numerous values of 𝜃.…”

Section: Inferring the Gap Length Using Bayesian Inferencementioning

confidence: 99%

“…The inherent architecture of the TMCMC algorithm allows for parallel computing and hence is ideal for inference when dealing with computationally expensive high-fidelity FE models. The algorithmic details of the TMCMC can be found in [64,69,67,68]. Besides, TMCMC has been applied to the miter gate model in [66].…”

Section: Inferring the Gap Length Using Bayesian Inferencementioning

confidence: 99%

The modeling of risk perception in the use of structural health monitoring information for optimal maintenance decisions

Chadha

Ramancha²,

Vega³

et al. 2023

Reliability Engineering & System Safety

View full text Add to dashboard Cite

A comprehensive review of digital twin—part 2: roles of uncertainty quantification and optimization, a battery digital twin, and perspectives

Thelen

Fink

et al. 2022

Struct Multidisc Optim

View full text Add to dashboard Cite

Accounting for model form uncertainty in Bayesian calibration of linear dynamic systems

Cited by 11 publications

References 22 publications

Reinforced concrete wall buildings with force‐limiting connections: Modeling effects and uncertainty propagation

Reinforced concrete wall buildings with force‐limiting connections: Modeling effects and uncertainty propagation

The modeling of risk perception in the use of structural health monitoring information for optimal maintenance decisions

A comprehensive review of digital twin—part 2: roles of uncertainty quantification and optimization, a battery digital twin, and perspectives

Contact Info

Product

Resources

About