| description abstract | Genetic algorithms (GAs), based on a multipoint search method and a crossover operation, are useful search procedures for combinatorial optimization problems. They also are applied to many kinds of practical optimization problem. However, in general, the GAs have a tendency to decrease rapidly of population diversities in processes of searching. In order to address this drawback, some researchers have proposed new algorithms for maintaining the population diversity. On the other hand, immune algorithms (IAs) are optimization techniques that imitate immune systems in an organism. The IAs are able to obtain a multiple quasi-optimum solution while maintaining the population diversity compared with GAs. In this paper, in order to consider the application of the IAs to optimum structural design problems, we developed a useful design support system for reinforced concrete (RC) slabs by combining a three-dimensional (3D) nonlinear dynamic finite element method (FEM) and an improved immune algorithm (IA). For the analysis of impact failure behavior of RC slabs, layered nonlinear FEM models with thin plate bending elements, and 3D elastoplastic FEM models with eight-node isoparametric hexahedral elements were used. First, an existing IA was modified to make it suitable for RC slab design, and both genetic and immune algorithms were applied to optimization problems with multiple optimum solutions. This modified IA was also applied to quasi-optimization problems with multiple maximum values. By comparing the above two approaches, the usefulness of the IA for design problems with discrete parameters was verified. Next, indices for evaluating impact resistance were discussed, and design simulation was conducted, based on these indices, by combining the layered nonlinear FEM and the IA. From results obtained, the accuracy and usefulness of the IA-based design support system were discussed. For obtaining a more practical design support system, shear reinforcement will be taken into account by combining the IA with the 3D elastoplastic FEM. Furthermore, the effectiveness and future potential of the design support system were confirmed by investigating an effect of introducing a database of design plans. | |
