Integrating BIM and AIoT for Smart Engineering Laboratory Monitoring and Management

Automatic indoor environmental quality (IEQ) monitoring plays a pivotal role in the management of green building operations. Traditional monitoring methods that integrate building information modeling (BIM) and the Internet of Things (IoT) are unable to perform automatic detection. This study addresses the limitation by introducing a BIM-AIoT-based “LabMonitor” approach for real-time IEQ monitoring and prediction. To enhance the accuracy of detecting occupants’ comfort, a convolutional neural network (CNN)-based YOLOv8 model is utilized. The effectiveness of the proposed approach is validated in diverse engineering laboratories within a university setting. Results demonstrated that the BIM-AIoT-based “LabMonitor” approach achieves a high-mean-Average Precision (mAP) of 0.939 in real-time indoor comfort level detection tasks. This work provides a scalable and interoperable solution for smart engineering laboratory management, addressing key challenges in multimodal data fusion and automatic real-time monitoring.