Transforming Hospital HVAC Design with BIM and Digital Twins: Addressing Real-Time Use Changes

Fengchang Jiang, Haiyan Xie (), Sai Ram Gandla and Shibo Fei
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Fengchang Jiang: School of Architectural Engineering, Taizhou Polytechnic College, Taizhou 225300, China
Haiyan Xie: Department of Technology, Illinois State University, Turner 5100, Normal, IL 61790, USA
Sai Ram Gandla: Department of Technology, Illinois State University, Turner 5100, Normal, IL 61790, USA
Shibo Fei: School of Architectural Engineering, Taizhou Polytechnic College, Taizhou 225300, China

Sustainability, 2025, vol. 17, issue 8, 1-26

Abstract: Traditional HVAC designs often struggle to respond promptly and accurately to dynamic changes in complex environments like hospital usage. This paper introduces a novel framework that integrates Building Information Modeling (BIM), digital twin technology, and practical medical processes to transform HVAC design for hospital construction. The framework ensured a smarter (with a reduction of 90% in calculation time and an improvement of 38.20–53.24% in respondence speed) and cleaner environment after identifying and calculating the rational layout of functional areas and optimizing intersecting flow lines. A key innovation of this research was the application of Support Vector Machine (SVM) and deep learning algorithm (Long Short-Term Memory) networks for real-time pedestrian traffic prediction. The implementation was validated through multiple simulations and applications including horizontal and vertical traffic flow and negative pressure analyses for three distinct departments. The findings underline the potential of BIM and digital twins to optimize HVAC systems and hospital design, providing adaptive, data-driven solutions for both routine operations and emergency scenarios. This framework offers a scalable approach for modernizing healthcare infrastructure, ensuring resilience and efficiency in diverse operational contexts.

Keywords: Building Information Modeling; pattern identification; digital twin; machine learning; indoor environment (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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