Application of Generative Artificial Intelligence and Parametric Design in Architecture and Engineering: A Fusion of Computational Creativity and Design Optimization

Abstract

The rapid evolution of digital technologies has transformed design methodologies across disciplines, particularly in architecture, engineering, and industrial design. Among these technologies, Generative Artificial Intelligence (AI) and Parametric Design have emerged as revolutionary tools that enable designers to create adaptive, efficient, and innovative solutions. Generative AI leverages machine learning algorithms to produce creative design outcomes by learning from large datasets, while parametric design provides a structured, rule-based modeling environment that allows flexibility and real-time modification of design parameters. When integrated, these two paradigms enhance design intelligence, automate complex tasks, and enable data-driven decision-making processes. This paper explores the theoretical foundations, methodologies, applications, benefits, challenges, and future scope of integrating Generative AI with Parametric Design in architecture and engineering practices.

KEYWORDS: Generative Artificial Intelligence, Parametric Design, Computational Design, Machine Learning, Design Automation, Optimization, Digital Architecture, Smart Engineering