In cold regions, the power generation efficiency of wind turbines is affected by blade icing. Heavy icing on blades will change the aerodynamic configuration of the blades and can even cause blades to crack or break. Therefore, monitoring and deicing technologies are important for the safe operation of wind turbines. This study proposes a novel strain ratio index based on mechanical analysis of icing, which causes the neutral axis shift and different strain ratio change between waving and shimmy directions. Data from the 5 kW wind turbine blade model in a low-temperature laboratory and the 1.5 MW full-scale field wind turbine monitoring over 1 year are used to validate the effectiveness of the proposed method. The proposed strain ratio index and icing detection criteria are derived from mechanical analysis with clear interpretability while reducing ambiguity from structural damage. The relationship between the strain ratio index and ice thickness is quantified through laboratory tests and validated by field applications, demonstrating the effectiveness and robustness under complex real-world service scenarios.

With the widespread implementation of bridge structural health monitoring (SHM) systems, monitored bridge networks have gradually formed. Understanding vehicle loads and considering spatio-temporal correlations within bridge networks is critical for structural condition assessment and maintenance decision making. This study aims to predict traffic flows by investigating traffic flow correlations within a bridge network using multi-bridge data, thereby supporting bridge network-level SHM. A transformer-based traffic flow prediction model considering spatio-temporal correlations of bridge networks (ST-TransNet) is proposed. It integrates external factors (processed via fully connected networks) and multi-period traffic flows of input bridges (captured by self-attention encoders) to generate traffic flow predictions through a self-attention decoder. Validated using weigh-in-motion data from an 8-bridge network, the proposed ST-TransNet reduces prediction root mean square error (RMSE) to 12.76 vehicles/10 min, outperforming a series of baselines—SVR, CNN, BiLSTM, CNN&BiLSTM, ST-ResNet, transformer, and STGCN—with significant relative reductions of 40.5%, 36.9%, 36.6%, 37.3%, 35.6%, 31.1%, and 22.8%, respectively. Ablation studies confirm the contribution of each component of the external factors and multi-period traffic flows, particularly the recent traffic flow data. The proposed ST-TransNet effectively captures underlying the spatio-temporal correlations of traffic flow within bridge networks, offering valuable insights for enhancing bridge assessment and maintenance.