An Intelligent Natural Language Processing (NLP) Workflow for Automated Smart Building Design

Abstract

The automation of smart building design processes remains a significant challenge, particularly in translating complex natural language requirements into structured design parameters within Computer-Aided Design (CAD) environments. Traditional design workflows rely heavily on manual input, which can be inefficient, error-prone, and time-consuming, limiting the integration of adaptive, real-time inputs. To address this issue, this study proposes an intelligent Natural Language Processing (NLP)-based workflow for automating the conversion of design briefs into CAD-readable parameters. This study proposes a five-step integration framework that utilizes NLP to extract key design requirements from unstructured inputs such as emails and textual descriptions. The framework then identifies optimal integration points—such as APIs, direct database connections, or plugin-based solutions—to ensure seamless adaptability across various CAD systems. The implementation of this workflow has the potential to enable the automation of routine design tasks, reducing the reliance on manual data entry and enhancing efficiency. The key findings demonstrate that the proposed NLP-based approach may significantly streamline the design process, minimize human intervention while maintaining accuracy and adaptability. By integrating NLP with CAD environments, this study contributes to advancing intelligent design automation, ultimately supporting more efficient, cost-effective, and scalable smart building development. These findings highlight the potential of NLP to bridge the gap between human input and machine-readable data, providing a transformative solution for the architectural and construction industries.