An innovative framework for probabilistic-based structural assessment with an application to existing reinforced concrete structures. (15th March 2016)

• Record Type: Journal Article
• Authors: Matos, José C.; Cruz, Paulo J.S.; Valente, Isabel B.; Neves, Luís C.; Moreira, Vicente N.
• Is Part Of: Engineering structures. Volume 111(2016:Mar. 15)
• Page Start: 552
• Page End: 564
• Publication Date: 2016-03-15
• Subjects: Structural assessment -- Uncertainty sources -- Model identification -- Optimization algorithm -- Reliability assessment -- Bayesian inference -- Reinforced concrete structures; Structural engineering -- Periodicals; Structural analysis (Engineering) -- Periodicals; Construction, Technique de la -- Périodiques; Génie parasismique -- Périodiques; Pression du vent -- Périodiques; Earthquake engineering; Structural engineering; Wind-pressure; Periodicals; 624.105
• DOI: 10.1016/j.engstruct.2015.12.040
• Languages: English
• ISSNs: 0141-0296

Abstract

Highlights: Allocated budget in engineering structures maintenance is lower than recommended. There is the need of novel assessment tools which integrate observation systems data. An innovative framework for probabilistic-based structural assessment is presented. This framework integrates model identification and reliability assessment algorithms. Measurement data is considered in the framework, through Bayesian inference. All sources of uncertainty are explicitly considered within this framework. Structure safety is assessed in an accurate and continuous basis during its lifetime. Framework is validated with a set of reinforced concrete beams, loaded up to failure. Abstract: A 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.