Research on Optimization Design Method of Building Structure Based on Genetic Algorithm

Abstract Building structural design is a complex process essential for producing safe, functional and visually stunning procedures. An additional important aspect of this design strategy is the choice of building materials and techniques. Ensuring that the chosen materials follow the building's goals requires considering variables including the availability of materials, economics and structural integrity. The current developments in the use of algorithms in the field of building structural design and performance evaluation are examined in this study. Different model architectures may be appropriate for training datasets with particular feature sets. Daily weather profiles in the history database contain a variety of characteristics. A collection of rules for development-based feature extraction allows the use of Restricted Boltzmann Machines (RBM). The study demonstrates how Genetic Algorithms (GA) operators work on potential converting solutions, while the concept could include member and structure-level approaches. The findings indicate that 97% of the localization accuracy is categorized. To evaluate the recommended classification technique, five distinct construction types (concrete sheer wall, steel, brick masonry, concrete frame and concrete frame-sheer wall) were categorized using the precision, recall and F1-score evaluation criteria. When the suggested approach is contrasted with existing algorithms, our method produces sufficient findings.