AI-ENABLED EARLY-STAGE BUILDING DESIGN OPTIMIZATION FOR VIBRATION-SENSITIVE FACILITIES
DOI:
https://doi.org/10.22399/ijcesen.5039Keywords:
AI-enabled design, vibration-sensitive facilities, early-stage optimization, machine learning, genetic algorithms, finite element analysisAbstract
To maintain safety, functionality, and occupant comfort, vibration-sensitive facilities—such as hospitals, labs, and precision manufacturing plants—need strict structural vibration control. Conventional early-stage design methods frequently depend on laborious, time-consuming manual computations and simplistic simulations that might not be able to represent intricate dynamic dynamics. This paper suggests an AI-enabled approach that combines genetic algorithm-based optimization and machine learning predictive modeling for early-stage building design optimization. Models were trained using finite element simulations of 50 fictitious structural configurations, allowing for precise vibration response prediction and the selection of the best design options. The findings show that AI-optimized designs dramatically shortened evaluation time while reducing peak vibrations by up to 40% and RMS displacements by 25% when compared to conventional designs. Important design discoveries that demonstrated AI's capacity to produce high-performance, non-intuitive solutions included the best possible floor stiffness distribution, mass allocation, damping placement, and layout geometry. The potential of AI to speed up early design decisions and improve the structural performance and safety of vibration-sensitive facilities is highlighted by this study
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