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Monitoring systems for the safety of building structure (SBS) can provide people with important data related to main supporting points in a building and then help people to make a reasonable maintenance schedule. However, more and more data bring a challenge for data management and data mining. In order to meet this challenge, under the framework of Wisdom Web of Things (W2T), we design a monitoring system for the SBS by using the semantic and the multisource data fusion technologies. This system establishes a dynamical data cycle among the physical world (buildings), the social world (humans), and the cyber world (computers) and provides various services in the monitoring process to alleviate engineers' workload. Furthermore, all data in the cyber world are organized as the raw data, the semantic information, and the multisource knowledge. Based on this organization, we can concentrate on the data fusion from the viewpoints of time, space, and multisensor. At last, a prototype system powered by the semantic platform LarKC is tested from the aspects of sample performance and time consumption. In particular, noisy data (i.e., inconsistent, abnormal, or error data) are detected through the fusion of multisource knowledge, and some rule-based reasoning is conducted to provide personalized service

An Intelligent Monitoring System for the Safety of Building Structure under the W2T Framework