Rational Method for FE-Model Updating: Comparison Between Conventional and AI Methods: A case-study of Tvärbanan, stage Solvalla
Publicerad
Författare
Typ
Examensarbete för masterexamen
Master's Thesis
Master's Thesis
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
The aim of this thesis is to evaluate and develop alternative and rational methods for
model updating. Ordinary and already established optimisation methods (Nelder-
Mead simplex, pattern search, genetic algorithm and particle swarm) were compared
against the contemporary and emerging principles of neural networks and AI. Their
performance was compared using three different beam test cases, with increased
complexity, with intention of solving unknown variables based on the corresponding
“true” deflection. Through the test cases, each method was tested and evaluated
based on accuracy, computational demand and scalability against complexity.
The results indicated that the traditional and already established methods performed
best, yielding high accuracy for low computational demand. Therefore, the
developed optimisation methods based on neural networks and use of the ChatGPT
AI could not compete.
However, findings indicate that neural networks methods and AI have future potential
in other areas then initially assumed. The conclusion of this thesis is that
neural network methods could be useful, particularly when implemented as a tool
rather than a replacement for model updating. Neural networks with the capacity to
predict deflections based on input variables could be simultaneously trained based
on information gained trough a traditional model updating routine. Once trained,
re-evaluation of model behaviour could be produced without a FEM-model, requiring
just a few seconds, providing improved abilities in engineering judgements.
Beskrivning
Ämne/nyckelord
Model updating, Optimization, Nelder-Mead simplex, Pattern search, Genetic alghoritm, Particle swarm, Neural Network, PINN, ChatGPT, Structural Engineering