Abstract
The rising importance of cranes in modern construction has led to the need for more efficient crane monitoring and operational management. Previous studies have focused on acquiring crane monitoring data through Internet of Things (IoT) devices. However, they offer limited data reasoning capacity and only understand a few particular construction activities with distinct patterns. These limitations restrict the applicability and generalisability of crane monitoring systems in real-world projects. This study proposes a Semantic Web-based method to enhance the reasoning of crane monitoring data by correlating as-is and as-planned information of crane operations from different IoT devices and information systems. The proposed method was validated through laboratory experiments, where the transient crane behaviours during a 242.7-second lift operation were accurately detected with an average error of 0.32 seconds, and all recognised lifts were successfully matched to the six lift orders. The outcome of this study is expected to advance crane lift monitoring and management practices, leading to increased crane utilisation and project performance.
Published Version
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More From: Proceedings of the Institution of Civil Engineers - Smart Infrastructure and Construction
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