Abstract
Whilst conventional approach in structural design is based on reliability-calibrated factored design formula, performance-based design customizes a solution to the specific circumstance. In this work, an artificial neural network approach is employed to determine implicit limit state functions for reliability evaluations in performance-based design and to optimally evaluate a set of design variables under specified performance criteria and corresponding desired reliability levels in design. Case examples are shown for reliability design. Through the establishment of the response and reliability databases, for specified target reliabilities, structural response computations are integrated with the evaluation of design parameters and design can be accomplished. By employing this methodology, with the same performance requirements, pertinent design parameters can be altered in order to evaluate feasible design alternatives, to explore the usage of various structural materials and to define required material quality control.
| Original language | English |
|---|---|
| Pages (from-to) | 145-149 |
| Number of pages | 5 |
| Journal | Advances in Engineering Software |
| Volume | 38 |
| Issue number | 3 |
| DOIs | |
| Publication status | Published - 1 Jan 2007 |
Keywords
- Design parameters
- Neural network
- Performance-based design
- Structural reliability
ASJC Scopus subject areas
- Software
- General Engineering