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

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Publicado en:Buildings vol. 15, no. 14 (2025), p. 2413-2430
Autor principal: Okonta Ebere Donatus
Otros Autores: Okeke, Francis Ogochukwu, Mgbemena Emeka Ebuz, Nnaemeka-Okeke, Rosemary Chidimma, Guo Shuang, Awe, Foluso Charles, Eke Chinedu
Publicado:
MDPI AG
Materias:
Language
Software
Accuracy
Green buildings
Workflow
Adaptability
Design engineering
Architecture
Automation
Design
Energy modeling
Energy consumption
Repair & maintenance
Efficiency
Translating
Construction
Building design
Construction industry
Building automation
Computer aided design > CAD
Natural language processing
Real time
Design parameters
Building information modeling
Integration
Information retrieval
Acceso en línea:Citation/Abstract
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Full Text - PDF
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Resumen: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.
ISSN:2075-5309
DOI:10.3390/buildings15142413
Fuente:Engineering Database