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.
| Original language | English |
|---|---|
| Pages (from-to) | 311-326 |
| Number of pages | 16 |
| Journal | Computer-Aided Civil and Infrastructure Engineering |
| Volume | 12 |
| Issue number | 5 |
| DOIs | |
| Publication status | Published - 1997 Jan 1 |
Fingerprint
All Science Journal Classification (ASJC) codes
- Civil and Structural Engineering
- Computer Science Applications
- Computer Graphics and Computer-Aided Design
- Computational Theory and Mathematics
Cite this
}
Data parallel neural dynamics model for integrated design of large steel structures. / Park, Hyo Seon; Adeli, Hojjat.
In: Computer-Aided Civil and Infrastructure Engineering, Vol. 12, No. 5, 01.01.1997, p. 311-326.Research output: Contribution to journal › Article
TY - JOUR
T1 - Data parallel neural dynamics model for integrated design of large steel structures
AU - Park, Hyo Seon
AU - Adeli, Hojjat
PY - 1997/1/1
Y1 - 1997/1/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=18444365305&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=18444365305&partnerID=8YFLogxK
U2 - 10.1111/0885-9507.00066
DO - 10.1111/0885-9507.00066
M3 - Article
VL - 12
SP - 311
EP - 326
JO - Computer-Aided Civil and Infrastructure Engineering
JF - Computer-Aided Civil and Infrastructure Engineering
SN - 1093-9687
IS - 5
ER -