This study focuses on optimizing the cross-sections of free-standing concrete rectangular water tanks. The primary goal is to minimize material usage while maintaining structural integrity. Traditional design methods often lead to conservative estimates of material requirements. By employing evolutionary and global gradient-free algorithms, this research aims to find more efficient design parameters. Water tanks are essential structures in civil engineering, and their design requires careful consideration to ensure safety and cost-effectiveness. The design challenge increases with the aspect ratio of the tank, as walls behave more like cantilevers with higher width-to-height ratios.

We employed evolutionary algorithms, specifically genetic algorithms, to optimize the thickness of the tank walls and the placement of vertical ribs. These algorithms are well suited for this application due to their ability to handle complex non-linear optimization problems without the need for gradient information. The optimization variables included wall thickness, which varies with height, and rib placement, considering different tank dimensions. For the modeling of wall behavior, the finite difference method was utilized, incorporating an orthotropic description of the material to accurately represent the directional properties of reinforcing ribs. The optimization process demonstrated that evolutionary algorithms could effectively identify optimal cross-sectional parameters.

The results indicated a potential material saving of 10-15% compared to traditional design methods. The optimized designs maintained structural integrity while using less concrete, making them more economical and sustainable. This research validates the effectiveness of evolutionary algorithms in optimizing the design of reinforced concrete water tanks. The findings suggest significant material savings, contributing to more cost-effective and environmentally friendly construction practices. Future work will extend these methods to other structural elements and consider additional constraints such as seismic loads. This study provides a robust framework for engineers to adopt advanced optimization techniques, enhancing the efficiency and sustainability of civil engineering projects.