Many structural engineering problems, e.g. parameter identification, optimal design and topology optimisation, involve the use of optimisation algorithms. Genetic algorithms (GA), in particular, have proved to be an effective framework for black-box problems and general enough to be applied to the most disparate problems of engineering practice. In this paper, the code TOSCA, which employs genetic algorithms in the search for the optimum, is described. It has been developed by the authors with the aim of providing a flexible tool for the solution of several optimisation problems arising in structural engineering. The interface has been developed to couple the programme to general solvers using text input/output files and in particular widely used finite element codes. The problem of GA parameter tuning is systematically dealt with by proposing some guidelines based on the role and behaviour of each operator. Two numerical applications are proposed to show how to assess the results and modify GA parameters accordingly, and to demonstrate the flexibility of the integrated approach proposed on a realistic case of seismic retrofitting optimal design.

TOSCA: a Tool for Optimisation in Structural and Civil engineering Analyses

CHISARI, Corrado
;
2018

Abstract

Many structural engineering problems, e.g. parameter identification, optimal design and topology optimisation, involve the use of optimisation algorithms. Genetic algorithms (GA), in particular, have proved to be an effective framework for black-box problems and general enough to be applied to the most disparate problems of engineering practice. In this paper, the code TOSCA, which employs genetic algorithms in the search for the optimum, is described. It has been developed by the authors with the aim of providing a flexible tool for the solution of several optimisation problems arising in structural engineering. The interface has been developed to couple the programme to general solvers using text input/output files and in particular widely used finite element codes. The problem of GA parameter tuning is systematically dealt with by proposing some guidelines based on the role and behaviour of each operator. Two numerical applications are proposed to show how to assess the results and modify GA parameters accordingly, and to demonstrate the flexibility of the integrated approach proposed on a realistic case of seismic retrofitting optimal design.
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11591/416641
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 9
  • ???jsp.display-item.citation.isi??? ND
social impact