The purpose of this paper was to investigate the influence of long-term corrosion on the deterioration of wind resistance of a steel transmission tower during its service life. An analytical model for predicting the long-term corrosion depth of carbon steel was established, and the corrosion depth of carbon steel in the Ningbo area was predicted based on the local atmospheric environment data. With the help of typhoon full-track simulation and wind field simulation technology, a joint probability distribution model of multidirectional extreme wind speeds was constructed using the t-Copula function to determine the typhoon climate of the transmission tower site. Finite element models of the ZM4 cathead transmission tower under 30/60/90 corrosion years were then established, respectively, according to the predicted corrosion depth of carbon steel in Ningbo. Three damage modes, i.e., minor damage, moderate damage and severe damage, corresponding to the transmission tower under wind loads, were defined, and pushover analyses were used to determine the limit values of each damage mode so as to obtain the typhoon-induced fragility curves of the transmission tower within 30/60/90 corrosion years. The results show that the increase in corrosion age leads to a deterioration in the nominal mechanical properties of the transmission tower components, making the damage probability to the transmission tower increase. Under the typhoon wind loads of a 50-year return period in the most unfavorable wind direction in Ningbo, the probability of moderate damage of the tower is within 10% and the probability of minor damage is controlled between 10% and 40%.

With the rapid development of the construction industry, tower cranes are increasingly used in coastal engineering. However, due to the complexity of their operating environment, tower cranes are vulnerable to typhoons, thunderstorms, and other extreme natural disasters. Therefore, it is becoming increasingly important to carry out safety warnings for the tower crane structure under the action of strong winds. 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. Based on the long-term monitoring data and the machine learning algorithm, two tower response prediction models were established. 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. 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. This is mainly due to the weathercock effect of the tower in the non-working condition. The prediction model proposed in this paper can provide timely and effective safety warnings for the tower structure. It also can provide useful engineering references and scientific structural safety warning suggestions for the same type of tower cranes that do not have IoT monitoring systems installed.

The purpose of this paper was to provide a new approach to achieve quantitative and accurate typhoon loss assessment of disaster-bearing bodies at township-level resolution. Based on the policy insurance data of Ningbo city, this paper took rural housing as the target disaster-bearing body and analyzed the aggregated data of disaster losses such as payout amount and insured loss rate of rural housing in Ningbo area under the influence of 25 typhoons during 2014–2019. The intensity data of disaster-causing factors such as the maximum average wind speed in Ningbo area under the influence of 25 typhoons were simulated and generated with the wind field engineering model, and a township-level high-resolution rural housing typhoon loss assessment model was established using a RBF artificial neural network. It was found that the insured loss rate of rural housing under wind damage was higher in the townships of southern Ningbo than in the townships of northern Ningbo, and the townships with larger insured loss rates were concentrated in mountainous or coastal areas that are prone to secondary disasters under the attack of the typhoon’s peripheral spiral wind and rain belt. The RBF neural network can effectively establish a typhoon loss assessment model from the causal factors to the losses of the disaster-bearing bodies, and the RBF neural network has a faster convergence speed and a smaller overall prediction error than the commonly used BP neural network.