Biologically inspired soft computing methods in structural mechanics and engineering

Structural Engineering and Mechanics

Volumn 11, Issue 5, 2001, Pages 485-502

  Ghaboussi, J ^a  

^a UNIVERSITY OF ILLINOIS AT URBANA CHAMPAIGN   (United States)

Author keywords

Genetic algorithm; Inverse problems; Newral networks; Soft computing; Structural mechanics

Indexed keywords

COMPUTATIONAL METHODS; FUZZY SETS; GENETIC ALGORITHMS; INVERSE PROBLEMS; MATHEMATICAL MODELS; NEURAL NETWORKS; PROBLEM SOLVING;

SOFT COMPUTING METHODS; STRUCTURAL MECHANICS;

STRUCTURAL DESIGN;

EID: 0035334960     PISSN: 12254568     EISSN: None     Source Type: Journal    
DOI: 10.12989/sem.2001.11.5.485     Document Type: Article

References (21)

1. Damage tolerance of laminated composites containing an open hole and subjected to compressive loadings. Part I: Analysis
2. Studies in bridge damage detection using genetic algorithm
3. Application of neural networks to the inspection of railroad rail
4. Knowledge-based modeling of material behavior with neural networks
5. Application of neural networks in acoustic wayside fault detection in railway engineering
6. Nested adaptive neural networks: A new architecture
7. New nested adaptive neural networks (NANN) for constitutive modeling
8. A new method of generating earthquake accelerograms using neural networks
9. Autoprogressive training of neural networks in complex systems
10. Autoprogressive training of neural network constitutive models
11. Soft computing with neural networks for engineering applications: Fundamental issues and adaptive approaches
12. The self organizing map
13. Generating multiple spectrum compatible accelerograms using stochastic neural networks
14. Implicit representation in genetic algorithm using redundancy
15. Evolving structural design solutions using an implicit redundant genetic algorithm
16. Evolution of optimal structural shapes using genetic algorithm
17. Constitutive modeling of geomaterials from non-uniform material tests