Toward Intelligent Digital Twins-Based Underfloor Heating Pipeline Maintenance Using IoT and BIM

This research aims to revolutionize traditional maintenance strategies by integrating a sensor-equipped smart ball with vision intelligence capabilities into the pipeline infrastructure. This smart device traverses the pipeline network, capturing critical data and imagery to identify potential issues such as obstructions, wear, and leaks. The information gathered is subsequently synchronized with a BIM framework, providing a digital twin of the pipeline system that facilitates precise issue localization and optimizes maintenance planning using appropriate machine learning models. This integration promises significant enhancements in the accuracy and efficiency of defect detection, thereby minimizing human error and inspection times, but also offers improved data visualization and management through BIM, leading to informed decision-making and optimized resource allocation. The proposed system underscores a significant leap toward advanced pipeline maintenance practices, presenting a scalable model for the proactive management of infrastructure health with broader implications for the future of intelligent infrastructure maintenance and management.