Digital Twin-enabled and Knowledge-driven decision support for tunnel electromechanical equipment maintenance

Abstract

<p>Urban tunnel infrastructure plays critical roles in sustaining the wellbeing of a society. The operation of tunnels relies on a diverse range of tunnel electromechanical equipment (TEE), such as ventilation, drainage, and the lighting system. However, effectively and proactively maintaining TEE to prevent unforeseen failures using limited resources remains an unresolved challenge. The utilization of digital twin technology, which combines Building Information Modeling (BIM), Internet of Things (IoT) and Semantic Web technologies, offers a knowledge-rich environment for the development of improved maintenance strategies. This study proposes a digital twin-enabled and knowledge-driven decision support method for proactive TEE maintenance. Initially, a digital twin conceptual framework for proactive TEE maintenance is presented, which integrates a knowledge-driven approach to support decision making. Subsequently, a controlled vocabulary-based method is developed to extract and update maintenance knowledge. Finally, a novel combinatory reasoner, including a rule selection algorithm and an inference algorithm, is devised to automatically generate maintenance schemes. Based on the proposed method, a decision support tool was developed and applied to Wenyi Road Tunnel in Hangzhou, China. The results demonstrated the effectiveness of the method, which can continuously update maintenance knowledge and assist fault detection and TEE state prediction with the combinatory reasoner. Moreover, the inference efficiency achieved by our method surpasses that of traditional approaches by approximately 162 ms.</p>