A subset simulation based approach with modified conditional sampling and estimator for loss exceedance curve computation. (September 2018)

• Record Type: Journal Article
• Authors: Bansal, Sahil; Cheung, Sai Hung
• Is Part Of: Reliability engineering & system safety. Volume 177(2018)
• Page Start: 94
• Page End: 107
• Publication Date: 2018-09
• Subjects: Reliability (Engineering) -- Periodicals; System safety -- Periodicals; Industrial safety -- Periodicals; Fiabilité -- Périodiques; Sécurité des systèmes -- Périodiques; Sécurité du travail -- Périodiques; 620.00452
• DOI: 10.1016/j.ress.2018.05.003
• Languages: English
• ISSNs: 0951-8320

Abstract

Abstract: A new stochastic simulation-based approach for the evaluation of loss exceedance curve without repeated reliability analyses, and the generation of samples of input random variables and any function of them conditioned on different levels of loss exceedance is proposed for a comprehensive risk and loss analysis, and investigation. The proposed approach involves the modification of the simulation algorithms in the Subset Simulation and the development of new estimators. It allows for a more comprehensive characterization of the probability distribution of the loss including the tail parts due to combinations of scenarios which can lead to extreme and catastrophic consequences. The approach is robust to the number of random variables involved. The effectiveness and efficiency of the proposed method are shown by an illustrative example involving a seismic loss analysis of a multi-story inelastic structure. A stochastic ground motion model coupled with a stochastic nonlinear dynamic model, and probabilistic fragility and loss functions are considered. Highlights: Literature establishes motives behind interest in stochastic simulation-based approach for loss exceedance curve computation. Proposed approach involves modification of Subset Simulation and development of new estimators. Comprehensive characterization of the loss exceedance curve including the tail parts. Illustrative example on seismic loss analysis considering stochastic ground motion model, nonlinear stochastic dynamic model, probabilistic fragility and loss functions and their uncertainties.