Optimization of geometric parameters of arch bridges using visual programming FEM components and genetic algorithm

• Published in: Engineering structures 2021-08, Vol.241, p.112465, Article 112465
• Main Authors: Korus, Kamil, Salamak, Marek, Jasiński, Marcin
• Format: Article
• Language: English
• Subjects: Algorithms; Arch bridge; Arch bridges; Automation; Bridges; Building Information Modeling; Civil engineering; Computer applications; Construction; Design optimization; Finite element method; Generative design; Genetic algorithm; Genetic algorithms; Iterative methods; Material properties; Mathematical models; Optimization; Parameters; Parametric modeling; Visual programming; Visual programming languages
• ISSN: 0141-0296; 1873-7323
• DOI: 10.1016/j.engstruct.2021.112465

[Display omitted] •FEM analysis components enrich visual programming functionalities.•One-environment geometry modeling and analysis enhance the design.•Visual programming and genetic algorithm establish a practical optimization framework.•Genetic algorithm effectively manages implicitly formulated design constraints. Arch bridges are essential components of transportation infrastructure. Their attractive geometry is based on a multitude of geometric parameters, which makes them a challenging design task. Therefore, arch bridges’ optimization should be aided by modern computational techniques and algorithms. This study presents an automated optimization process of steel through arch bridges. We merged visual programming, an accessible text programming alternative, with a genetic algorithm to establish an automated framework. We used Dynamo, an open-source civil engineering visual programming language (VPL), to develop a model generation script. Our finite element method (FEM) package enriched the basic VPL functions; it allowed geometry modeling and static strength analysis inside one parametric environment. Linked genetic algorithm replaced the designer in iterative, time-consuming optimization tasks, automating the process. The algorithm adjusted construction’s geometric parameters to provide solutions optimized for the typical objective: minimizing the material consumption while still fulfilling strength requirements. We evaluated the procedure with optimization of selected reference construction. The system dealt with cases of increasing complexity, adjusting cross-section dimensions, static scheme parameters, and material properties. The paper describes practical aspects of implementing and utilizing the visual programming-genetic algorithm solution, which can also be adapted for other structures, additional objectives, and constraints.