Numerical analysis of self-centring cross-laminated timber walls

居中 结构工程 工程类
作者
Christian Slotboom
标识
DOI:10.14288/1.0392505
摘要

Self-centring Cross-Laminated Timber (CLT) walls are a low damage seismic force resisting system, which can be used to construct tall wood buildings. This study examines two approaches to model self-centring CLT walls, one that uses lumped plasticity elements, and another that uses fibre-based elements. Finite element models of self-centring CLT walls are developed using the Python interpreter of Opensees, OpenSeesPy, and tested under monotonic and reverse cyclic loading conditions. Outputs from the analysis are compared with data from two existing experimental programs. Both models accurately predict the force displacement relationship of the wall in monotonic loading. For reverse cyclic loading, the lumped plasticity model could not capture cyclic deterioration due to crushing of CLT. Both models slightly overpredict the post-tension force. Sensitivity analyses were run on the fibre model, which show the wall studied is not sensitive to the shear stiffness of CLT. OpenSeesPy models are also created of a two-story structure, which is tested dynamically under a suite of ground motions. The structure is based on a building tested as part of the NHERI TallWood initiative. During testing the foundation of the building was found to be inadvertently flexible. To determine the appropriate model parameters for this foundation, calibrations were performed by running a sequence of OpenSeesPy analyses with an optimization algorithm. Outputs from the lumped plasticity and fibre models were compared to experimental results, which showed that both could capture the global behaviour of the system with reasonable accuracy. Both models overpredict peak post-tension forces. The suite of analyses is then run again on the building to predict the performance with a rigid foundation. Cyclic deterioration is more significant for the building with a rigid foundation, and as a result the fibre mode is more accurate.
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