A Multi-volume constraint approach to diverse form designs from topology optimization

Topology optimization methods gain extensive attention from many engineering fields for their capacities of meeting the diverse requirements of structural performances and innovative appearances. While most classical topology optimization techniques focus on globally optimal form generation around the whole design domain, there are still many demands for controlling local material proportion. This paper introduces a multi-volume constraint approach and its parameter configuration schemes to help users artificially pre-design the topologically optimized structure with the Bi-directional Evolutionary Structural Optimization (BESO) method. The numerical examples in this paper demonstrate that the presented method can be successfully applied in the computational structural form-finding for several building designs in various projects, e.g., high-rise building facades, circular shell domes, and nest-type stadium structures. It can provide the designers with diverse finely controlled structural layouts based on prescribed local material volume fractions. The structural performances of the diverse designs are very close to that of the globally optimal design. This study aims to make a bridge linking the computational optimization method to the human-centric design requirements, and it holds an enormous application potential in industrial or building designs.

Automated summary

Topology optimization methods are highly valued in engineering fields for their ability to meet diverse structural performance and innovative appearance requirements. This paper introduces a multi-volume constraint approach and its parameter configuration schemes to pre-design the topologically optimized structure. Numerical examples demonstrate its successful application in computational structural form-finding for various building designs. It provides designers with diverse and finely controlled structural layouts based on prescribed local material volume fractions, which perform closely to the globally optimal design. This study aims to bridge the gap between computational optimization methods and human-centric design requirements, holding great potential in industrial or building designs.