In civil engineering, there is a growing concern regarding the vulnerability of empty cylindrical steel tanks to buckling caused by wind forces. Traditional stiffening methods, including ring and vertical stiffeners, have demonstrated certain limitations. For instance, while ring stiffeners are commonly utilized, they often lack innovative features. On the other hand, vertical stiffeners have proven ineffective in addressing buckling that arises specifically from wind pressure. Additionally, spiral stiffeners are designed primarily to mitigate axial, thermal, and torsional buckling in composite shells, which further limits their applicability in wind-related scenarios.
To address these challenges, a new data-driven neural network model has been proposed. This model aims to evaluate the performance of a unique stiffening solution, referred to as the stiffening-helix, which is specifically designed to combat wind-induced buckling in open-top tanks. By leveraging advanced machine learning techniques, this innovative approach seeks to enhance the structural integrity of these tanks under adverse wind conditions.
The development of this model represents a significant advancement in the field, potentially leading to safer and more resilient designs for storage tanks. It underscores the need for innovative engineering solutions that can effectively respond to the dynamic forces exerted by the environment.
