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Published June 2014 | public
Journal Article

General Confinement Model Based on Nonlocal Information


The confinement effect has been of significant importance for improving the resilience against extreme compression loadings such as seismic excitations. Notwithstanding the accuracy of previous confinement models, some challenges remain regarding their applicability. The previous approaches often build on structure-dependent parameters necessitating intractable calibrations, and their formulations are defined on an integration point or a small portion of the structure, thereby precluding general applicability to complicated real-scale RC structures. Here a general confinement model is proposed in a novel way that it can harness physical information inside the real-scale system. The information is denoted nonlocal information, since it is processed by the nonlocal formulation for assuring the mesh-objectivity. Physically, the nonlocal information provides the proximity to adjacent stiff materials and boundaries through the information index suggested herein. Numerical issues regarding the parallel computing and the optimal selection of the length parameter for the nonlocal formulation are also addressed. The unprecedentedly broad applications include a solid column, a hollow column, a rectangular wall, a T-shaped wall, and even a wall with opening, which strongly bear out the promising potential and universality of the novel confinement model.

Additional Information

© 2014 ASCE. This manuscript was submitted on February 17, 2013; approved on October 9, 2013; published online on October 11, 2013. Discussion period open until June 23, 2014; separate discussions must be submitted for individual papers. Regarding experimental data for validation, the kind hospitality of Professor John W. Wallace is appreciated. All numerical simulations were run on GARUDA, a high-performance computing cluster hosted within the Mechanical and Civil Engineering Department at the California Institute of Technology. The purchase and installation of GARUDA was possible thanks to the Ruth Haskell Research Fund, the Tomiyasu Discovery Fund, and Dell Inc. Professor S. Krishnan's warm support with the cluster is deeply appreciated.

Additional details

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