Machine Learning + Funicular Floor Systems
This research was supported by the PennPraxis and National Science Foundation (NSF) CAREER AWARD (NSF CAREER-1944691- CMMI) to Dr. Masoud Akbarzadeh.
This research proposes a geometry-based generative design method that utilizes Machine Learning to generate various funicular floor structures with simple sketch input. The force subdivision method has been used in generating compression-dominant floor structures. In addition, machine learning techniques such as artificial neural networks have been used to evaluate and optimize the reciprocal structural forms after subdividing the force diagram. Subsequently, a computational algorithm has been developed to identify the relationship between columns and walls in a floor plan and generate a force diagram for the construction of a network of funicular systems between the walls and columns. Subsequently, the algorithm was combined with the machine learning technique to automatically generate the structure from a sketch input of columns and walls on a plan drawing. This interdisciplinary approach combines computer science, and structural and architectural design to provide flexible design solutions for building structures.