An innovative framework for probabilistic-based structural assessment with an application to existing reinforced concrete structures

Abstract

A note on versions:The version presented here may differ from the published version or from the version of record. If you wish to cite this item you are advised to consult the publisher's version. Please see the repository url above for details on accessing the published version and note that access may require a subscription. ABSTRACTA novel framework for probabilistic-based structural assessment of existing structures, which combines model identification and reliability assessment procedures, considering in an objective way different sources of uncertainty, is presented in this paper. A short description of structural assessment applications, provided in literature, is initially given. Then, the developed model identification procedure, supported in a robust optimization algorithm, is presented. Special attention is given to both experimental and numerical errors, to be considered in this algorithm convergence criterion. An updated numerical model is obtained from this process.The reliability assessment procedure, which considers a probabilistic model for the structure in analysis, is then introduced, incorporating the results of the model identification procedure. The developed model is then updated, as new data is acquired, through a Bayesian inference algorithm, explicitly addressing statistical uncertainty.Finally, the developed framework is validated with a set of reinforced concrete beams, which were loaded up to failure in laboratory.

Comprehensive Bayesian structural identification using temperature variation

“…The methodology can also be expanded to consider multiple parameters, which is a common scenario on civil infrastructures [26,27,13].…”

“…the model is successfully calibrated) when the model parameters θ take the values θ * . Although our example only updates one parameter, the methodology can also consider multiple parameters, which is a common scenario in civil infrastructures [26,27,13].…”

“…Component (a) is extremely difficult to quantify. Most of the present research [13,14] usually disregard this form of uncertainty or consider it as zero mean Gaussian distributed [6]. Only a limited number of authors in the SHM community, namely Higdon [15] and Simoen [16] have applied Bayesian methodologies under this scope.…”

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“…In order to evaluate the influence of each model parameter and to reduce the computational cost of the probabilistic analysis, a sensitivity analysis was performed. Thus, for each parameter, an importance measure b k , was obtained (Matos et al 2016), see expression 4:…”

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