Integrated system identification and reliability evaluation of stochastic building structures |
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| Affiliation: | 1. Department of Civil and Structural Engineering, The Hong Kong Polytechnic University, Hong Kong;2. College of Civil Engineering, Tongji University, Shanghai, China;1. University College London Hospitals, London, UK;2. Castle Hill Hospital, Hull, UK;3. Imperial College Healthcare NHS Trust, London, UK;4. Bristol Cancer Institute, Bristol, UK;5. Lancashire Teaching Hospitals, Lancashire, UK;6. University of Sheffield, Sheffield, UK;7. Weston Park Hospital, Sheffield, UK;8. Guys and St Thomas'' NHS Trust, London, UK;9. Beatson West of Scotland Cancer Centre, Glasgow, UK;10. Royal Devon and Exeter Foundation NHS Trust, Exeter, UK;11. London Lung Cancer Group, London, UK;12. Cancer Research UK & UCL Cancer Trials Centre, UCL, London, UK;13. DataNova Ltd, London, UK;14. Cancer Research UK Lung Cancer Centre of Excellence, UCL, London, UK;1. LMT (ENS Cachan, CNRS, Université Paris Saclay) 61 avenue du Président Wilson, 94235 Cachan, France;2. Université Paris-Est, Institut de Recherche en Constructibilité, ESTP 28 avenue du Président Wilson, 94230 Cachan, France;3. AREVA, 10 rue Juliette Récamier, 69006 Lyon, France;4. Icam, Site de Toulouse, 75 avenue de Grande-Bretagne, 31076 Toulouse Cedex 3, France;5. Université de Toulouse, Institut Clément Ader (ICA), INSA, UPS, Mines Albi, ISAE 135 avenue de Rangueil, 31077 Toulouse Cedex, France |
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| Abstract: | System identification and reliability evaluation play a significant role in structural health monitoring to ensure the serviceability and safety of existing structures. Although the development of system identification methods has attained much attention and some degree of maturity, reliability evaluation of existing structures still remains a challenging problem especially when uncertainties in measurement data and inherent randomness, which are inevitably involved in civil structures, are considered. In this regard, this paper presents a framework for integrated system identification and reliability evaluation of stochastic building structures. Two algorithms are proposed to respectively evaluate component reliability and system reliability of stochastic building structures by combining a statistical moment-based system identification method and a probability density evolution equation-based reliability evaluation method. System identification is embedded in the procedure of reliability evaluation of a stochastic building structure. The uncertainties in both the structure and the external excitation are considered. Numerical examples show that the structural component and system reliabilities of a three-story shear building structure with three damage scenarios can be effectively evaluated by the proposed methods. |
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