A New Formulation on Seismic Risk Assessment for Reinforced Concrete Structures with Both Random and Bounded Uncertainties

• Published in: Discrete dynamics in nature and society 2018-01, Vol.2018 (2018), p.1-15
• Main Authors: Liu, Xiao-Xiao, Wang, Yuan-Sheng
• Format: Article
• Language: English
• Subjects: Active learning; Computer simulation; Concrete; Concrete construction; Concrete structures; Dependent variables; Design optimization; Earthquakes; Failure; Ground motion; Kriging; Limit states; Mathematical models; Methods; Monte Carlo method; Nonlinear analysis; Parallelepipeds; Probability; Random variables; Reinforced concrete; Risk assessment; Seismic engineering; Seismic hazard
• ISSN: 1026-0226; 1607-887X
• DOI: 10.1155/2018/5027958

A new formulation on seismic risk assessment for structures with both random and uncertain-but-bounded variables is investigated in this paper. Limit thresholds are regarded as random variables. The median of random variables is described through an improved multidimensional parallelepiped (IMP) convex model, in which the uncertain domain of the dependent bounded variables can be explicitly expressed. The corresponding Engineering Demand Parameters are taken to be dependent and follow a multidimensional lognormal distribution. Through matrix transformation, a given performance function is transformed into the regularized one. An effective method based on active learning Kriging model (ALK) is introduced to approximate the performance function in the region of interest rather than in the overall uncertain space. Based on ALK model, the failure probabilities for different limit states are calculated by using Monte Carlo Simulation (MCS). Further, the failure probabilities for different limit states in 50 years can be obtained through coupling the seismic failure probability with the ground motion hazard curve. A six-story reinforced concrete building subjected to ground motions is investigated to the efficiency and accuracy of the proposed method. The interstory drift and the acceleration as two responses of the case study are, respectively, obtained by utilizing Incremental Dynamic Analysis and nonlinear history analysis.