Computational Design of Self-Learning Eco-Friendly Buildings with Grasshopper Scripting Algorithmic Design

With the rapid advancement in computational design, both the design and architectural representation processes have witnessed a revolutionary shift from analog to digital media, and by utilizing nature-based concepts in design, they have opened new doors for adaptability in the architectural design process. The computational design approach starts with defining a mathematical model based on numerical relationships and equations, thus replacing the standard visual representation. Our goal is to integrate computational design technologies to create self-learning buildings that can adapt to environmental challenges and adjust accordingly by collecting data from the surrounding environment through the implementation of sensors. The research method is descriptive-analytical with a parametric approach and using algorithmic computational method with Grasshopper Scripting to produce documents. The research findings indicate the potential for developing natural forms compatible with architecture and achieving environmentally friendly design conditions; to ensure that the generated form is adaptable, we conducted several analyses such as sunlight, radiation, and shadow analysis before selecting and finalizing the form. The results show that an environmentally friendly form that extends from the surrounding environment is characterized by high levels of adaptability.