Keywords: structural engineering, semi-rigid joints, steel structures, genetic algorithms, artificial intelligence, structural optimisation.

Focusing on the objective of minimizing the effort spent in the structural joint analysis, this paper propose the use of genetic algorithms [1] to evaluate the most suitable joint layout, i.e., adjusting the bending moment resistance and the initial stiffness, respectively, according to design specifications, but, obviously, reducing costs. The developed software, SRJTool [2,3], is able to start the design process by performing an elastic analysis thus obtaining a first estimation of the joint response. The second step is associated with the use of these initial information data in the genetic algorithm system to obtain a new joint layout to be compared to the desirable target values. This interactive procedure is repeated until the optimum target values are reached. These procedures were implemented with the aid of genetic algorithm software, Evolver [4] producing very promising results in terms of cost saving and design efficiency performed with low computational effort requirements.

The semi-rigid joint design is a repetitive and exhaustive task because a lot of geometrical and mechanical properties need to be considered. It is very difficult to obtain the optimum joint configuration in the first attempt due to the various joint parameter interdependencies. This fact was the main motivation to the development of the system proposed in this work.

In order to evaluate the global response of the joint, their full geometrical and mechanical properties should be considered. With these results in hand, the mechanical model of the joint can be characterised according to the type of joint presented in the Eurocode 3 [5]. Finally, the component resistance is evaluated and the moment versus rotation curve of the joint may be obtained. Therefore, the Figure 1 presents the variables used in the optimisation process that are manipulated by the genetic algorithm.

In this work, two examples where used to demonstrated the optimisation procedure. The first example considers a flush endplate joint and the second, an extended endplate. The fitness function considered the adjustment of , minimization of the cost (euro) and finally c) adjustment of the initial stiffness by rotation () control. The obtained results showed the efficiency of the method and this optimisation procedure may easily help the joint designers.

1

Koza, J.R., "Genetic Programming on the Programming of Computers by Means of Natural Selection", The MIT Press, 1992.

2

SRJ Tool, Beta Version 1.0 - Semi-Rigid Joint Tool - DEC, PUC-Rio, Rio de Janeiro, Brazil, MSc Dissertation, 2003.

3

Ramires, F.B. "Avaliação Estrutural de Ligações Semi-Rígidas em Aço com Placa de Extremidade", DEC, PUC-Rio, Rio de Janeiro, Brazil, MSc Dissertation, 2003 (in portuguese).

4

Evolver 4.0 - Genetic Optimisation Add-in for Microsoft Excel - Palisade Decision Tools (1999).

5

Eurocode 3, PREN - 1993-1-8: 20, Part 1.8: "Design of Joints, Eurocode 3: Design of Steel Structures", CEN, European Com. for Standardisation, 2000.

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