High-strength bolts play an essential role in connecting steel structural components across bridges, buildings, and machinery, thanks to their low cost and broad adaptability. However, prolonged exposure to complex loading and harsh environments can cause loosening, which increases the risk of structural distortion or catastrophic collapse. Conventional monitoring tools, such as strain gauges or fiber-optic sensors, often suffer from high expense, short service life, and poor early-warning performance. To overcome these challenges, this study presents a novel surface-mounted piezoelectric sensor built on lead magnesium niobate–lead titanate (PMN-PT) single crystals, selected for their superior piezoelectric constants, energy density, and low-frequency sensitivity. The sensor integrates a PMN-PT wafer, monolithic washer, epoxy resin encapsulation, and shielded cabling. It was experimentally validated through waterproofing and electrical tests across five working conditions, maintaining capacitance stability within 3.25% and infinite insulation resistance. Preload monitoring experiments were conducted using a time-reversal method, and a tightening index based on focused signal amplitude was proposed to quantify bolt-loosening conditions. Results confirmed a strong linear association (R² = 0.986) between the focused signal amplitude and preload torque (0–120 N·m), with a maximum error of only 5.7% in validation trials under unknown torque. Overall, this PMN-PT-based sensor offers a cost-efficient, sensitive, and durable solution for early detection of bolt loosening in steel structures.

Many cities in coastal areas are prone to typhoon disasters due to their location on the Pacific storm path, and the direct effect of catastrophic winds can lead to the destruction of low-rise buildings and severe damage to high-rise structures. The purpose of this study was to enhance the understanding of boundary layer wind profiles of strong typhoons in coastal areas and reduce property losses and casualties caused by wind disasters. Based on the field measurements of wind profile acoustic radar in coastal areas, the variation characteristics of the boundary layer wind profile during the passage of super typhoon Lekima were first studied in depth, and the evolution law of the typhoon boundary layer profile was summarized. Then, the effects of typhoon horizontal structure, topography, wind speed, and time distance on the characteristics of the typhoon profile were discussed, respectively. Finally, the evolution characteristics of wind profile parameters were obtained by fitting three wind profile theoretical models. Due to the strong variability of typhoon profile morphology, the theoretical model of wind profile is only applicable to the wind profile from the bottom to the low-level jet height of typhoons, while wind parameters are closely related to the spatial location of the typhoon wind field.

As the onshore wind farm technology matures, offshore wind energy has attracted increasing attention. Zhejiang has coastal areas with massive potential for wind resources because of its geographical location. Therefore, understanding the wind resources in these areas can lay a foundation for future development and utilization. On this basis, this study used the measured wind field data of a wind farm along the coast of Zhejiang from March 2014 to February 2015 and from March 2016 to February 2018 to investigate and compare the characteristics of wind energy resources, including average wind speed, Weibull shape and scale factors, wind direction variation, and wind energy density. Then, the capacity coefficient of a wind turbine predicted using the wind farm data was compared with the actual capacity coefficients of two wind turbines in the wind farm in 2019. Results revealed the following observations: The overall variations in the evaluation indicators followed clear patterns over the 3 years. For example, the main wind direction in the same season was the same, and the variations in the monthly average wind speed, the monthly wind power density, and the theoretical capacity factors were highly similar. The time-series data indicated that the difference in the indicators between summer and autumn was significantly larger than that between other seasons, with the maximum difference in monthly average wind speed of 1.46 times and the maximum difference in monthly wind power density of 1.5 times. The comparison results of the capacity coefficient showed that the theoretical and actual capacity coefficients were extremely close when the monthly average wind speed was less than 6 m/s, with the average difference being less than 9%. When the monthly average wind speed was greater than 6 m/s, the proximity between the theoretical and actual capacity coefficients was reduced, with an average difference of more than 9% and a maximum value of 28%. In general, the overall characteristics of wind resources in coastal areas of Zhejiang exhibited similar trends but fluctuated considerably in some months. Wind energy forecasts had significant discrepancies from the actual operation indicators of the wind farm when the wind speed was high.

Based on wind field data measured during the landfall of Typhoon Jangmi in Wenzhou in 2008, this study analyzes wind field characteristics, including wind speed, wind direction, probability density, turbulence intensity, gust factor, peak factor, power spectrum, turbulence integral scale, coherence, and the autocorrelation coefficient of Typhoon Jangmi. Results showed that the wind field characteristics for the east and west measuring points were basically the same and followed an approximately similar pattern. The probability density of fluctuating wind tends to obey a Gaussian distribution. The turbulence intensity gradually decreases with increasing 10 min averaged wind speed, but the reduction rate gradually drops. The turbulence intensity is affected by the change in a time interval because turbulence intensity decreases as the time interval increases. With an increase in the 10 min average wind speed and time interval, the gust factor decreases. The peak factor decreases, though insignificantly, with increasing mean wind speed, and the distribution of peak factors is greatly scattered. The variation in the peak factor with time is in good agreement with the Durst curve. The gust factor increases as the turbulence intensity increases and is in line with the empirical curves of Ishizaki, Choi, and Cao. The power spectra of the fluctuating wind speed of Typhoon Jangmi in all directions agree well with Von Karman’s empirical spectrum. The turbulence integral scale increases slightly with increasing average wind speed, and the distribution is relatively scattered. The coherence of the fluctuating wind speed components matches the exponential function proposed by Davenport, and the autocorrelation coefficient decreases as $\mathsf{\tau }$ increases.

The accurate characterization of typhoon wind profile properties is of great importance in the field of wind engineering and wind design of high-rise structures. In this paper, the average typhoon wind profile characteristics are investigated using the 930 m height measurement data of Typhoon Lekima 2019 obtained from the observations of the mobile acoustic profiling radar deployed in the coastal area. Specifically, this paper adopts a cohesive hierarchical cluster analysis method to classify the mean wind profiles of Super Typhoon Lekima 2019, and the optimal number of clusters is obtained as two classes by the profile coefficient with the sum of squares of clustering errors, the Calinski–Harabasz index, and the Davies–Bouldin index, and the two classes of typical wind profiles are named as cluster 1 type and cluster 2 type. The model fitting analysis of the two types of typical wind profiles was carried out in the height range of 0~300 m after classification, and the effects of fitting the cluster 1-type mean wind profiles with the Vickery model and the Snaiki and Wu model and the cluster 2-type mean wind profiles with the Power-law model, the Log-law model, and the Deaves–Harris and Gryning models were discussed. The results show that the cohesive hierarchical cluster analysis method used in this paper can effectively categorize the mean typhoon profiles. In addition, this paper has some reference significance for future research on the characteristics of measured typhoon wind profiles and engineering applications such as the wind-resistant design of high-rise structures.

Two WJ‐3 anemometers placed at the same height on the top of an architectural engineering building in Wenzhou University are used to determine the wind speed of Typhoon Morakot during its landing in real time. This study aims to explore Typhoon Morakot’s wind field characteristics, including mean wind speed, probability density distribution of fluctuating wind speed, power spectral density, correlation analysis, and coherence, on the basis of data measured by the two anemometers. Results show that the probability density distribution of the fluctuating wind speed of the typhoon follows the Gaussian distribution, and the measured cross‐power spectrum of fluctuating wind speed is in good agreement with the modified Karman spectrum. The autocorrelation decreases with the increase in time interval (τ). The longitudinal autocorrelation coefficient decays rapidly with the increase in τ, and the lateral autocorrelation coefficient decays at an unchanged rate. The exponential attenuation coefficients of the longitudinal and transverse fluctuating wind speeds increase with the increase in the mean wind speed, and their mean values are 10.86 and 15.33, respectively. The change trends of the coherence coefficients of the two wind speed components with the mean wind speed are the same. The measured coherence coefficients of the two wind speed components are in good agreement with the exponential function.