A probabilistic framework for estimating the residual drift of idealized SDOF systems of non‐degrading conventional and damped structures. (14th September 2017)

• Record Type: Journal Article
• Authors: Guo, Jack Wen Wei; Christopoulos, Constantin
• Is Part Of: Earthquake engineering and structural dynamics. Volume 47:Number 2(2018)
• Page Start: 479
• Page End: 496
• Publication Date: 2017-09-14
• Subjects: residual drift -- SDOF systems -- supplemental damper; Structural dynamics -- Periodicals; Earthquake engineering -- Periodicals; 624.1762
• DOI: 10.1002/eqe.2975
• Languages: English
• ISSNs: 0098-8847

Abstract

Summary: This paper presents a general framework for predicting the residual drift of idealized SDOF systems that can be used to represent non‐degrading structures, including those with supplemental dampers. The framework first uses post‐peak oscillation analysis to predict the maximum ratio of residual displacement to the peak transient displacement in a random sample. Then, residual displacement ratios obtained from nonlinear time‐history analyses using both farfield and near‐fault‐pulse records were examined to identify trends, which were explained using the oscillation mechanics of SDOF systems. It is shown that large errors can result in existing probability models that do not capture the influence of key parameters on the residual displacement. Building on the observations that were made, a general probability distribution for the ratio of residual displacement to the peak transient displacement that more accurately reflects the physical bounds obtained from post‐peak oscillation analysis is proposed for capturing the probabilistic residual displacement response of these systems. The proposed distribution is shown to be more accurate when compared with previously proposed distributions in the literature due to its explicit account of dynamic and damping properties, which have a significant impact on the residual displacement. This study provides a rational basis for further development of a residual drift prediction tool for the performance‐based design and analysis of more complex multi‐degree‐of‐freedom systems.