Abstract

Introduction. Reinforcement is one of the most important processes in designing reinforced concrete structures. The need to optimize the reinforcement process arises from the constant increase in the volume and complexity of construction projects, as well as from the economy and efficiency requirements. In this regard, automatic design processes are becoming increasingly relevant, being conducive to significant acceleration and improvement of the quality of these works. Other areas of civil and industrial construction are also adopting automatic design processes in order to reduce construction and production costs. Mixed Integer Programming (MIP), Simplex Method (SM), and Genetic Algorithm (GA) are commonly used.Aim: to develop an algorithm for optimizing the reinforcement process of reinforced concrete structures of buildings and structures and to design a program for automating the process.Materials and methods. The paper relies upon the results of the calculations performed by the authors in the Lira program. Furthermore, the authors created a digital information model of the building in the Revit program. The Python programming language was used to test the results. A ready-made solution was created in C# using RevitAPI.Results. The paper substantiates the relevance and demonstrates the feasibility of optimization of technological solutions in the process of designing buildings and structures made of reinforced concrete according to the criterion “reliability – efficiency”. To this end, existing design software was integrated into a single modeling and design module, and the necessary software was proposed for development. The genetic algorithm was chosen as the main method for optimizing the reinforcement of structures. Tools for processing the calculation results, creating a duplicate of the calculation model in the design environment and analyzing the calculations were elaborated.Conclusion. The selected genetic algorithm accelerates the design process, and saves time when designing reinforced concrete structures. The results of the study are instrumental in developing the software for the automation of reinforcement.

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