A sparse data-driven stochastic damage model for seismic reliability assessment of reinforced concrete structures. (July 2022) - He, Jingran; Gao, Ruofan; Chen, Jianbing
Highlights: A sparse inspection data based seismic reliability analysis framework is proposed. The BCS-KL is used for statistical uncertainty quantification. The accuracy of the BCS-KL is testified for different scale of fluctuation. Shake table test is conducted to verify the proposed analytical framework. Abstract: It is important to study the seismic reliability of concrete structures based on real measured data of the material properties. The data of material properties collected in practice is usually sparse in spatial distribution. When assessing the seismic performance of the structures based on data, the statistical inference is usually firstly conducted to evaluate the material properties of the whole structure from the sparse data, and the nonlinear seismic simulation of the structures can be performed. The coupling effect of uncertainty and nonlinearity has not been explained properly. In the present study, the Bayesian compressive sensing – Karhunen Loève expansion (BCS-KL) method is combined with the stochastic damage model (SDM) to build a sparse data-driven stochastic damage model. The model deals with nonlinear seismic stochastic analysis of concrete structures based on sparse data, in which the BCS-KL is applied for uncertainty quantification of the statistical inference and the SDM is used as a physical constitutive model of concrete. The simulation is performed with sophisticated modeling using stochastic finite element method, and the physical synthesis method is applied to assess the seismic reliability of the structure. Finally, a shake table test is conducted to verify the proposed simulation framework.