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.

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.

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.

The reliable evaluation of the interfacial bonding quality of steel structures strengthened with carbon fiber-reinforced polymer (CFRP) is crucial to ensuring the long-term service safety of the structures. Focusing on the active and passive detection methods based on piezoelectric sensing, this paper takes numerical simulation as the core research method to provide theoretical verification and mechanism explanation for subsequent key experiments, thus supporting the accurate detection of interfacial damage in CFRP–steel plate joints. A 3D piezoelectric–structural coupling finite element model and a 2D ultrasonic guided wave propagation finite element model were established via COMSOL Multiphysics 6.2 to systematically simulate the electromechanical response characteristics of three piezoelectric sensors (PMN-PT, PZT and PVDF). The research focused on analyzing the potential output and voltage–load response of the three sensors, and simultaneously explored the propagation laws and energy evolution mechanisms of ultrasonic waves in the presence of different debonding damages and groove defects in CFRP plates. The simulation results show that the PMN-PT sensor exhibits the optimal detection performance, with its peak potential output reaching 2.66 times that of the PZT sensor and 4.69 times that of the PVDF sensor, with a load sensitivity of 484.3 mV/kN. In the ultrasonic active detection of interfacial debonding damage, the first-wave amplitude has a significant positive correlation with the debonding length, and this characteristic is attributed to the strong reflection effect and energy accumulation caused by the acoustic impedance mismatch at the CFRP–air interface. For the internal groove defects in CFRP plates, the simulation clarifies that the increase in groove length leads to energy trapping in the plate, while the increase in groove depth intensifies ultrasonic wave energy reflection. The numerical simulation results were compared and verified with data from companion experiments conducted by the authors’ team, showing a high degree of consistency, which confirms the accuracy and reliability of the established finite element models. Meanwhile, the physical essence of damage detection is elucidated from the perspective of wave theory, providing a solid numerical analysis foundation and theoretical support for the intelligent monitoring of interfacial damage in CFRP–steel structures.

Aiming at the nonlinear and non-stationary characteristics of wind speed series, this study proposes a hybrid forecasting framework that integrates Variational Mode Decomposition (VMD), Sparrow Search Algorithm (SSA), and Long Short-Term Memory (LSTM) networks. First, VMD is employed to adaptively decompose the original wind speed series into multiple Intrinsic Mode Functions (IMFs) with distinct frequency features, thereby reducing the non-stationarity of the original sequence. Second, SSA is utilized to adaptively optimize key parameters of the LSTM network, including the number of hidden units, learning rate, and dropout rate, to enhance the model’s capability in capturing complex temporal patterns. Finally, the predictions from all modal components are integrated to generate the final wind speed forecast. Experimental results based on 10-min resolution measured data from a coastal wind farm in southeastern China in June 2020 show that the model achieves a Root Mean Square Error (RMSE) of 0.208, a Mean Absolute Error (MAE) of 0.161, and a Mean Absolute Percentage Error (MAPE) of 3.284% on the test set, with its comprehensive performance significantly surpassing benchmark models such as LSTM, VMD-LSTM, MLP, XGBoost, and ARIMA. The limitations of this study mainly include the use of only one month of data for validation, the lack of segmented performance analysis across different wind speed regimes, and a fixed prediction horizon of 10 min. The results indicate that the proposed hybrid forecasting framework provides an effective approach with practical engineering potential for ultra-short-term wind power prediction.

Steel pipelines for oil and gas transportation serve as the lifeline of energy conveyance, and their long-term safe operation constitutes a crucial safeguard for energy security. Nevertheless, in complex service environments, local defects formed on the inner pipe wall due to medium corrosion have emerged as a prominent hidden danger endangering pipeline integrity. Accurate evaluation of the residual strength of pipelines with corrosion defects is not only the technical foundation for ensuring the safe operation of pipelines, but also the key basis for formulating scientific maintenance strategies and prolonging the service life of pipelines. Taking three grades of steel pipelines (X52, X65 and X80), which represent the typical strength grades commonly used in long-distance oil and gas transmission pipelines, as the research objects, this paper establishes a three-dimensional finite element model of single-point uniform corrosion defects considering the nonlinear material behavior, and systematically investigates the influence laws of geometric parameters (depth, length and width) of corrosion defects on the failure pressure of pipelines under the action of monotonic internal pressure load. The accuracy of the proposed finite element model is verified by comparison with the test data from thirteen groups of full-scale burst experiments. On the basis of parametric analysis results, an explicit and high-precision predictive model for failure pressure is developed. The research findings reveal that corrosion depth acts as the dominant factor affecting pipeline failure pressure with a distinctly nonlinear influence characteristic: the load-bearing capacity of pipelines drops drastically when the relative depth d/t exceeds 0.6, where d is the corrosion depth and t is the pipe wall thickness. There exists a critical value for the impact of corrosion length, beyond which its weakening effect on failure pressure tends to level off. Within the commonly encountered engineering range (20~100°), corrosion width exerts a negligible influence on pipeline failure pressure and thus can be overlooked in engineering evaluation. In comparison with conventional industry assessment methods such as ASME B31G, DNV RP-F101, PCORRC and SY/T 6151, the newly established predictive model features higher prediction accuracy and broader applicability, which provides on-site engineers with a powerful theoretical tool and practical formula for the rapid and accurate evaluation of the residual strength of corroded pipelines.