Machine Learning-Based Prediction of Dynamic Responses of a Tower Crane under Strong Coastal Winds

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