Monitoring Information and Probabilistic-Based Prediction Models for the Performance Assessment of Concrete Structures
Publication: Journal of Performance of Constructed Facilities
Volume 30, Issue 4
Abstract
The lifecycle analysis and assessment of concrete structures requires an efficient evaluation and prediction of time-variable mechanical and chemical degradation processes. Inspection systems and novel monitoring methods are of valuable support in these important tasks, but due to their practical feasibility and the costs that they entail, they also present limitations. Therefore, information gathered with inspection methods for structural performance assessment needs to be used in the most effective manner possible, e.g., with respect to serviceability limits, bearing capacity, robustness, and redundancy of a given structure, among others. The aim of this paper is to present a framework for the prediction of time-dependent performance indicators of concrete structures prone to fatigue, with emphasis on a wind turbine foundation, including reliability. A theoretical background and selected structural performance indicators as well as associated lifecycle prediction methods—incorporating inspection and monitoring information—are presented.
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Acknowledgments
The authors wish to acknowledge the support of the Austrian Research Agency FFG and the “Zentrum für Innovation und Technologie GmbH–ZIT” within the project “Comprehensive Infrastructure Life-Cycle Assessment” (CILIA) and “Monitoring basiertes Softwareprodukt für die Lebenszeitbewertung ermüdungsgefährdeter Betonstrukturen” (MSLEB). The opinions and conclusions presented in this paper are those of the author and do not necessarily reflect the views of the sponsoring organizations.
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Published In
Journal of Performance of Constructed Facilities
Volume 30 • Issue 4 • August 2016
Copyright
© 2015 American Society of Civil Engineers.
History
Received: Sep 11, 2014
Accepted: Aug 4, 2015
Published online: Oct 12, 2015
Discussion open until: Mar 12, 2016
Published in print: Aug 1, 2016
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