Data Parallel Neural Dynamics Model for Integrated Design of Large Steel Structures

Abstract

We have two objectives in creating novel design theories and computational models: automation and optimization. These two aspects are particularly important in design of complex and large engineering structures. In this article, a robust data parallel neural dynamics model is presented for discrete optimization of large steel structures based on the AISC ASD or LRFD specifications. The computational model has been implemented on a CM–5 supercomputer and applied to integrated minimum–weight design of two steel high–rise building structures. The largest example is a 144–story modified tube–in–tube super–high–rise building structure with 20,096 members. Optimization of such a large structure subjected to the highly nonlinear constraints of actual design codes, such as the AISC LRFD code, where nonlinear second–order effects have to be taken into account, has never been attempted before. The computational model developed in this research finds the minimum–weight design for this very large structure subjected to multiple dead, live, and wind loadings in three different directions automatically. This research demonstrates how a new level in design automation is achieved through the ingenious use of a novel computational paradigm and new high–performance computer architecture.