![]() The results show that under strong winds, the non-working tower has a response lag, resulting in the fact that its maximum displacement does not correspond to the maximum wind speed moment at the site. During the transit of super typhoon In-fa, the maximum displacement of the tower structure was predicted in advance, based on the measured wind speed data at the site, which is in good agreement with the displacement data monitored by the IoT. Based on the long-term monitoring data and the machine learning algorithm, two tower response prediction models were established. In this paper, a real-time monitoring system for tower responses based on the Internet of things (IoT), which realizes long-term monitoring of the whole process of tower crane operation, was built. ![]() Therefore, it is becoming increasingly important to carry out safety warnings for the tower crane structure under the action of strong winds. However, due to the complexity of their operating environment, tower cranes are vulnerable to typhoons, thunderstorms, and other extreme natural disasters. With the rapid development of the construction industry, tower cranes are increasingly used in coastal engineering.
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