The basic wind pressure or the reference wind pressure for structural design varies greatly across complex terrain. Since only a few meteorological stations can provide adequate extreme wind speed records, it is very difficult to appropriately determine the basic wind pressure for a specific site without a long history of meteorological records. To solve this problem, a mesoscale CFD model was developed and optimized based on geographic information data for Taizhou and suitable turbulence models were selected for CFD simulation. The mean extreme wind speed and the corresponding direction at five main weather stations with long observation histories in Taizhou were used as the verification conditions to perform the CFD simulation of the extreme wind field. The maximum wind speeds of the rural areas, cities, and streets of Taizhou were obtained from the results of the mesoscale CFD simulations. Then, the 50-year return period reference wind pressures were calculated and could be used for the wind-resistant structural design of buildings for sites without a long history of meteorological records. The reliability of the results was verified by comparing the simulation results with the observation data at five main stations with a long history.

The Beijing-Shanghai high-speed railway is one of the milestones of China’s high-speed railway development and its security plays a significant role in China’s economic and social development. However, the evaluation methods used for large-scale security operations and important infrastructure systems, such as the high-speed railways, are discrete and nonlinear; thus they cannot issue emergency warnings in a timely manner. The emergence of optical fiber sensing technology can solve this problem. This technology has progressed rapidly in its application to the monitoring of railway security and it has attracted much attention within the industry. This study considers the newly built passenger railway line between Shijiazhuang and Jinan as an example. The web-based, all-in-one fiber Bragg grating static level is described as well as a set of online monitoring systems, which is automated, real-time, remote, visual, and adaptable to the standards of the Beijing-Shanghai high-speed railway. According to our theoretical analysis, the planned automated monitoring of settlement deformation for the Beijing-Shanghai high-speed railway and the real-time analysis and calculation of monitoring data can ensure the operational security of this section of China’s high-speed railway system.

In this study, a numerical model of the cable cabin of a comprehensive pipe gallery was established to study the smoke flow diffusion behaviour of a comprehensive pipe gallery fire under a rectangular cross-section. The effects of fire source power (Q = 0.5, 1.0, 1.5, 2.0, 2.5, 3.0 MW) and fire source location (D = 10, 20, 40, 50, 60, 80, 100 m) on the smoke flow characteristics—such as smoke layer height and thickness, longitudinal airflow velocity, and ceiling temperature distribution—were analysed, and the corresponding prediction model was fitted. The results show the following: (1) The height of the smoke layer decreases with increasing fire power, and the predictive model of the smoke layer thickness obtained from the fitting is proportional to the smoke mass flow rate and inversely proportional to the aspect ratio of the pipe gallery. (2) Longitudinal air velocity prediction models of D < 50 m and D ≥ 50 m are fitted, and the average error between them and the numerical simulation values is 9.611%. (3) The temperature decay gradient of the smoke decreases gradually with increasing distance from the fire source, while there is a significant temperature difference between the two sides of the fire source. The average relative errors of the dimensionless temperature rise models fitted upstream and downstream of the fire source in the form of $\frac{\Delta T}{T_0}=A{e}^{B\left(\frac{D-X}{H}\right)+C}$ exponentials with respect to the numerical simulations were 11.688% and 7.296%, respectively. The results of the study can provide a reference for smoke flow and fire prevention and control in comprehensive pipe galleries.

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.