This paper focuses on the weight minimization of planar steel trusses, with particular reference to square hollow sections. The design optimization refers to size, shape and topology. The design variables are represented by some geometrical parameters regarding the dimension of the cross sections of the different elements of the truss and the outer shape. The topology is also included in the optimization search in an indirect way, that is the designer at different runs of the algorithm can change the number of bays keeping constant the total length of the truss, to successively choose the best optimal solution. Minimum weight optimum design is posed as a single-objective optimization problem, subject to constraints coherent with the current Eurocode 3. The optimal solution is obtained by a Differential Evolutionary (DE) algorithm, by suitably combining mutation and crossover operators.
Optimum Design of Planar Steel Trusses by a Differential Evolutionary Approach / Fiore, Alessandra. - STAMPA. - (2015), pp. 152-157. (Intervento presentato al convegno 4th International Conference on Materials Engineering for Advanced Technologies, ICMEAT 2015 tenutosi a London, UK nel June 27-28, 2015).
Optimum Design of Planar Steel Trusses by a Differential Evolutionary Approach
Alessandra Fiore
2015-01-01
Abstract
This paper focuses on the weight minimization of planar steel trusses, with particular reference to square hollow sections. The design optimization refers to size, shape and topology. The design variables are represented by some geometrical parameters regarding the dimension of the cross sections of the different elements of the truss and the outer shape. The topology is also included in the optimization search in an indirect way, that is the designer at different runs of the algorithm can change the number of bays keeping constant the total length of the truss, to successively choose the best optimal solution. Minimum weight optimum design is posed as a single-objective optimization problem, subject to constraints coherent with the current Eurocode 3. The optimal solution is obtained by a Differential Evolutionary (DE) algorithm, by suitably combining mutation and crossover operators.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.