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Published May 2012 | public
Journal Article

Parallelized Implicit Nonlinear FEA Program for Real Scale RC Structures under Cyclic Loading


Parallel computing in civil engineering has been restricted to monotonic shock or blast loading with explicit algorithm which is characteristically feasible to be parallelized. In the present paper, efficient parallelization strategies for the highly demanded implicit nonlinear finite-element analysis (FEA) program for real scale reinforced concrete (RC) structures under cyclic loading are proposed. Quantitative comparison of state-of-the-art parallel strategies in terms of factorization were carried out, leading to the problem-optimized solver, which successfully embraces the penalty method and banded nature. Particularly, the penalty method employed imparts considerable smoothness to the global response, which yields practical superiority of the parallel triangular system solution over those of advanced solvers such as the parallel preconditioned conjugate gradient method. Other salient issues on parallelization are also addressed. By virtue of the parallelization, the analysis platform offers unprecedented access to physics-based mechanisms and probabilistic randomness at the entire system level and realistically reproduces global degradation and localized damage, as reflected from the application to a RC structure. Equipped with accuracy, stability and scalability, the parallel platform is believed to serve as a fertile ground for the introducing of further physical mechanisms into various research fields, as well as the earthquake engineering community.

Additional Information

© 2012 American Society of Civil Engineers. This manuscript was submitted on August 10, 2010; approved on June 7, 2011; published online on April 16, 2012. Discussion period open until October 1, 2012. All the numerical simulations were performed on GARUDA, high-performance computing cluster hosted within the Civil Engineering department at Caltech (600 CPUs, 75 Dual quad core processors at 2.33 GHz with 8 GB RAM). In large part, the purchase and installation of GARUDA was possible by virtue of the Ruth Haskell Research Fund, the Tomiyasu Discovery Fund, and Dell, Inc. The hospitality of Professor Daniel Palermo and Professor Frank J. Vecchio is appreciated in support of providing experimental data, and also the authors are deeply grateful to Professor S. Krishnan for his consistent support for parallel simulations.

Additional details

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