Voronoi tessellations are a mathematical concept that appears in many examples in nature, such as the skin of giraffes, dry soil, and vegetable cells. In the context of biomimicry, these tessellations have been used to build impressive structures worldwide that are both aesthetically pleasing and structurally efficient. This paper proposes a methodology based on genetic algorithms (GA) to determine the structural topology of Voronoi flat roofs with tubular steel cross sections and a given boundary. The design variables correspond to the number and position of the Voronoi centers that form the tessellations within the roof, as well as the dimensions of the structural elements. This representation of the design variables creates an unstructured optimization problem. Such characteristic is addressed by an implicit redundant representation of possible solutions, which generates chromosomes with varying numbers of variables. The objective function relates to the weight of the roof, considering constraints raised in technical and constructive issues. The methodology was applied to four different roof boundaries: triangular, pentagonal, square, and rhombic. In general, the results provide optimal aesthetic solutions with a few Voronoi tessellations, based on the algorithm configuration and the multimodal nature of the search space. Convergence analysis indicates the possibility of the algorithm getting stuck in an optimum local and shows the progressive reduction of Voronoi centers. Lastly, it is observed that the maximum displacement constraint leads to the shape of the optimal roof.

New flood records are being set across the world as precipitation patterns change due to a warming climate. Despite the presence of longstanding water management infrastructure like levees and reservoirs, this rise in flooding has been met with property damage, loss of life, and hundreds of billions in economic impact, suggesting the need for new solutions. In this work, the authors suggest the active management of distributed networks of ponds, wetlands and retention basins that already exist across watersheds for the mitigation of flood damages. As an example of this approach, we investigate optimal control of the gated outlets of 130 such locations within a small watershed using linear programming, genetic algorithms, and particle swarm optimization, with the objective of reducing downstream flow and maximizing basin storage. When compared with passive operation (i.e., no gated outlets) and a uniformly applied active management scheme designed to store water during heavy rainfall, the optimal control techniques (1) reduce the magnitudes of peak flow events by up to 10%, (2) reduce the duration of flood crests for up to several days, and (3) preserve additional storage across the watershed for future rainfall events when compared with active management. Combined, these findings provide both a better understanding of dynamically controlled distributed storage as a flood fighting technique and a springboard for future work aimed at its use for reducing flood impacts.