Distributed Neural Dynamics Algorithms for Optimization of Large Steel Structures
Publication: Journal of Structural Engineering
Volume 123, Issue 7
Abstract
Optimization of large structures consisting of thousands of members subjected to the highly nonlinear constraints of the actual commonly used design codes, such as the American Institute of Steel Construction (AISC), Allowable Stress Design (ASD), or Load and Resistance Factor Design (LRFD) specifications (AISC 1989, 1994), requires high-performance computing resources. We have previously developed parallel optimization algorithms on shared memory multiprocessors where a few powerful processors are connected to a single shared memory. In contrast, in a distributed memory machine, a relatively large number of microprocessors are connected to their own locally distributed memories without globally shared memory. In this article, we present distributed nonlinear neural dynamics algorithms for discrete optimization of large steel structures. The algorithms are implemented on a recently introduced distributed memory machine, the CRAY T3D, and applied to the minimum weight design of three large space steel structures ranging in size from 1,310 to 8,904 members. The stability, convergence, and efficiency of the algorithms are demonstrated through examples. For an 8,904-member structure, a high parallel processing efficiency of 94% is achieved using a 32-processor configuration.
Get full access to this article
View all available purchase options and get full access to this article.
References
1.
Adeli, H. (ed.) (1992a). Supercomputing in engineering analysis. Marcel Dekker, Inc., New York, N.Y.
2.
Adeli, H. (ed.) (1992b). Parallel processing in computational mechanics. Marcel Dekker, Inc., New York, N.Y.
3.
Adeli, H.(1995). “Knowledge engineering.”Archives of Computational Methods in Engrg., 2(4), 51–68.
4.
Adeli, H. and Hung, S. L. (1995). Machine learning-neural networks, genetic algorithms, and fuzzy systems. John Wiley & Sons, Inc., N.Y.
5.
Adeli, H., and Kamal, O. (1993). Parallel processing in structural engineering. Elsevier Applied Science, New York, N.Y.
6.
Adeli, H., and Kumar, S.(1995). “Distributed genetic algorithms for structural optimization.”J. Aerosp. Engrg., ASCE, 8(3), 156–163.
7.
Adeli, H., and Park, H. S.(1995a). “Optimization of space structures by neural dynamics.”Neural Networks, 8(6), 769–781.
8.
Adeli, H., and Park, H. S.(1995b). “Counter propagation neural networks in structural engineering.”J. Struct. Engrg., ASCE, 121(8), 1205–1212.
9.
Adeli, H., and Park, H. S.(1996). “Hybrid CPN-neural dynamics model for discrete optimization of steel structures.”Microcomputers in Civ. Engrg., 11(5), 355–366.
10.
American Institute of Steel Construction. (1989). Manual of steel construction, allowable stress design, Chicago, Ill.
11.
American Institute of Steel Construction. (1994). Manual of steel construction, load, and resistance factor design, Chicago, Ill.
12.
Cray Research, Inc. (1993a). CRAY T3D system architecture overview manual, Eagen, Minn.
13.
Cray Research, Inc. (1993b). MPP Fortran programming model, Eagen, Minn.
14.
Cray Research, Inc. (1993c). Programming the CRAY T3D with Fortran, Eagen, Minn.
15.
Golub, G. H., and Loan, C. V. (1989). Matrix computations, 2nd Ed., The Johns Hopkins University Press, Baltimore, Md.
16.
Uniform building code, Vol. 2, structural engineering design provisions. (1994). International Conference of Building Officials, Whittier, Calif.
Information & Authors
Information
Published In
Copyright
Copyright © 1997 American Society of Civil Engineers.
History
Published online: Jul 1, 1997
Published in print: Jul 1997
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.