This article proposes a statistical framework for the development of design models for concrete sandwich panels with glass-fiberreinforced polymer shear grids. The framework is developed by integrating the Bayesian parameter estimation method and the Eurocode-based capacity reduction factor calibration method. In the first part of the framework, probabilistic and deterministic shear flow prediction models are proposed based on 32 experimental data. It is seen that the contribution of glass-fiber-reinforced polymer grids is dominant, although parameters on the geometrical and material properties of insulation and concrete wythes also contribute. Different constant terms for bias correction of the proposed models are proposed according to the insulation type, and the prediction error of the developed model was reduced. In the second part of the framework, the capacity reduction factor for the proposed deterministic formulas is calculated for design purposes. Statistical calibrations for capacity factors are carried out to meet a target reliability level, and the value is estimated to be approximately 0.75 for all proposed models. Further data collection will improve the applicability of the proposed models and clarify quantification of the contribution of parameters.
Bibliographical noteFunding Information:
Funding The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work is supported by the Australian Research Council (ARC) under its Linkage project (Project No. LP140100030) and the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP) (No. 2014-11-0869).
© The Author(s) 2016.
All Science Journal Classification (ASJC) codes
- Civil and Structural Engineering
- Building and Construction