An efficient method for incorporating modeling uncertainties into collapse fragility of steel structures

•A practical method is proposed to consider model uncertainties in collapse capacity.•Combined Monte Carlo with response surface method is used as reliability method.•Central composite with fractional factorial design is used to design experiments.•Bayesian inferential approach is adapted to conside...

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Bibliographic Details
Published in:Structural safety Vol. 88; p. 102009
Main Authors: Pourreza, F., Mousazadeh, M., Basim, M. Ch
Format: Journal Article
Language:English
Published: Amsterdam Elsevier Ltd 01.01.2021
Elsevier BV
Subjects:
Bayesian analysis
Bayesian inferential approach
Coefficient of variation
Collapse
Collapse fragility
Computer applications
Computing costs
Computing time
Design of experiments
Deterioration
Endurance time method
Fatigue tests
Fragility
Monte Carlo sampling
Monte Carlo simulation
Nonlinear analysis
Nonlinear dynamics
Parameter uncertainty
Probabilistic models
Probability theory
Random variables
Regression analysis
Reliability analysis
Response surface
Response surface methodology
Statistical analysis
Steel beams
Steel columns
Steel frames
Steel structures
Collapse fragility
Endurance time method
Response surface
Bayesian inferential approach
Monte Carlo sampling
Design of experiments
ISSN:0167-4730, 1879-3355
Online Access:Get full text
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Summary:•A practical method is proposed to consider model uncertainties in collapse capacity.•Combined Monte Carlo with response surface method is used as reliability method.•Central composite with fractional factorial design is used to design experiments.•Bayesian inferential approach is adapted to consider derived models uncertainty.•Endurance time method is used to conduct time history nonlinear dynamic analyses. Deterioration parameters that are commonly used to simulate nonlinear behavior of steel components were mainly calibrated based on the results from experiments on steel beams. Recently, the state of knowledge for deterioration behavior of steel columns has been improved by experimental and analytical studies on the wide-flange steel columns. These deteriorating characteristics are introduced as regression relationships in which the associated uncertainties are represented by the coefficient of variation (COV). Accounting for these uncertainties in estimating collapse fragility curves through the incremental dynamic analysis (IDA) and simulation-based reliability methods is impractical due to the large amount of required computational effort. In this study, two main goals are pursued. The first goal is comprehensive evaluation of the main, interaction, and quadratic effects of the modeling random variables on the collapse capacity of steel structures. The second goal is to propose an efficient approach to create response surface (RS), which in combination with the Monte Carlo (MC) sampling method will be used to incorporate modeling uncertainties into the collapse fragility. This efficiency will be achieved by employing screening design techniques to reduce the amount of analysis required to create a quadratic RS and also endurance time (ET) analysis as an efficient alternative nonlinear dynamic analysis method with less computational time to estimate the structural responses. In order to develop a reliable probabilistic model, the Bayesian model inference approach is applied to account for the uncertainties in the created model. The proposed procedure is performed with both IDA and ET methods on a prototype 5-story steel frame. Results indicate that collapse capacity is highly influenced by the strength modeling variables of beam as well as the ultimate rotation capacity of column components. In addition, ET method by a considerable reduction in the computational costs provides comparable responses with IDA in a probabilistic framework.
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ISSN:0167-4730
1879-3355
DOI:10.1016/j.strusafe.2020.102009