Fatigue and corrosion are the main reasons for the failure of stay cables. Traffic load also plays a significant role in cable fatigue. To study the fatigue life of stay cables under the combined action of traffic load and corrosion, this study originally deduced the fatigue probability model for steel wires, considering the stress range and average stress based on the nonlinear fatigue damage model. Then, the weight loss rate was introduced into the fatigue probability model for steel wires in order to obtain the fatigue life probability model for corroded steel wires. Subsequently, a Monte Carlo simulation (MCS) was conducted to predict the fatigue life of stay cables under different guaranteed probabilities. Finally, using a cable-stayed bridge on the Yangtze River in China as an example, the fatigue life of the stay cables in the bridge under the combined effects of different corrosive environments and measured traffic loads was evaluated. Results indicate that both the stress range and mean stress affect the fatigue life of the steel wire. The fatigue life of stay cables is determined by a few steel wires with shorter lives, and the stress redistribution accelerates the failure of the inner wire of the stay cables. The fatigue life of the stay cables on background engineering is much less than the design life of the bridge under the combined actions of a middle-level or high-level corrosive environment and traffic load.

In recent years, the energy crisis has become increasingly severe, and global attention has shifted towards the development and utilization of wind energy. The establishment of wind farms is gradually expanding to encompass forested regions. This paper aims to create a Weather Research and Forecasting (WRF) model suitable for simulating wind fields in forested terrains, combined with a long short-term time (LSTM) neural network enhanced with attention mechanisms. The simulation focuses on capturing wind characteristics at various heights, short-term wind speed prediction, and wind energy assessment in forested areas. The low-altitude observational data are obtained from the flux tower within the study area, while high-altitude data are collected using mobile radar. The research findings indicate that the WRF simulations using the YSU boundary layer scheme and MM5 surface layer scheme are applicable to forested terrains. The LSTM model with attention mechanisms exhibits low prediction errors for short-term wind speeds at different heights. Furthermore, based on the WRF simulation results, a wind energy assessment is conducted for the study area, demonstrating abundant wind energy resources at the 150 m height in forested regions. This provides valuable support for the site selection in wind farm development.

To address the limitations in accuracy, reliability, and efficiency of traditional cable tension measurement methods, this paper proposes a cable tension assessment method based on 3D laser scanning technology that considers point cloud density. This study first employed a point cloud plane projection algorithm to reduce a 3D point cloud model to a 2D plane, fitting the actual cable shape by considering point cloud density. Subsequently, the parabolic and catenary cable mechanics models were derived to characterize the relationship between cable tension and shape based on force analysis of cable segments and differential segments. The Broyden–Fletcher–Goldfarb–Shanno (BFGS) algorithm was applied to calculate cable tensions using the measured cable shape and the mechanic’s models, and the proposed cable tension assessment method was validated using practical cable point cloud models. Finally, the cable tension assessment method was applied to a specific sea‐crossing bridge and compared with the traditional frequency method. The results indicated that the 3D laser scanning cable tension assessment method, considering point cloud density, could quickly and accurately identify cable tensions, offering greater accuracy, reliability, and efficiency compared to the traditional frequency method.