Transforming facility management with BIM, IoT, and Digital Twin: a data-driven approach to air quality monitoring

Document Type

Article

Publication Date

Summer 8-11-2025

Abstract

Poor indoor air quality in healthcare facilities can significantly impact patient recovery, increase the risk of airborne disease transmission, and compromise staff well-being. Conventional monitoring methods typically rely on manual or static measurements, lacking the real-time responsiveness required for proactive facility management. This study proposes an integrated Building Information Modeling (BIM), Internet of Things (IoT), and Digital Twin (DT) framework to enable continuous air quality monitoring and data-driven decision-making. A case study was conducted in an Egyptian healthcare facility, where IoT sensors captured critical parameters, including airflow (CFM), pressure (Pa), CO₂ levels (ppm), and temperature (°C). These data were transmitted to ThingSpeak, a cloud-based analytics platform, and dynamically integrated into a BIM model using Dynamo scripts in Autodesk Revit. This integration enabled real-time, color-coded visualization of environmental conditions, supporting rapid, data-driven responses to changing air quality. A pilot study with a BIM expert validated the framework, highlighting challenges such as internet dependency, data security risks, interoperability limitations, and the limited availability of as-is BIM models. Despite these constraints, the system demonstrated its effectiveness in real-time monitoring and proactive facility management, providing a scalable, data-driven approach for improving operational efficiency and indoor air quality in healthcare environments.

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