Modeling uncertainty of specimens employing spines and force‐limiting connections tested at E‐defense shake table

Abstract

In light of the significant damage observed after earthquakes in Japan and New Zealand, enhanced performing seismic force‐resisting systems and energy dissipation devices are increasingly being utilized in buildings. Numerical models are needed to estimate the seismic response of these systems for seismic design or assessment. While there have been studies on modeling uncertainty, selecting the model features most important to response can remain ambiguous, especially if the structure employs less well‐established lateral force‐resisting systems and components. Herein, a global sensitivity analysis was used to address modeling uncertainty in specimens with elastic spines and force‐limiting connections (FLCs) physically tested at full‐scale at the E‐Defense shake table in Japan. Modeling uncertainty was addressed for both model class and model parameter uncertainty by varying primary models to develop several secondary models according to pre‐established uncertainty groups. Numerical estimates of peak story drift ratio and floor acceleration were compared to the results from the experimental testing program using confidence intervals and root‐mean‐square error. Metrics such as the coefficient of variation, variance, linear Pearson correlation coefficient, and Sobol index were used to gain intuition about each model feature's contribution to the dispersion in estimates of the engineering demands. Peak floor acceleration was found to be more sensitive to modeling uncertainty compared to story drift ratio. Assumptions for the spine‐to‐frame connection significantly impacted estimates of peak floor accelerations, which could influence future design methods for spines and FLC in enhanced lateral‐force resisting systems.

Reinforced concrete wall buildings with force‐limiting connections: Modeling effects and uncertainty propagation

“…Previous research has quantified the uncertainty in the model parameters by performing uncertainty propagation, sensitivity, Bayesian model updating, or reliability analyses to assess the effects of the uncertainty in the model parameters on the seismic response and performance of structures. 43,[45][46][47][48][49][50] They have explicitly considered the uncertainty in the model parameters by modeling it through suitable PDFs. They have demonstrated that considering this source of uncertainty introduces significant variability in the results of the seismic performance-based design and assessment of structures.…”