Mitigation of Climate Change-Induced Frost Effects on Bridge Dynamic Behaviour
Haowei Wang, Alireza Entezami, Hassan Sarmadi, Wenhao Li, Bahareh Behkamal
Climate change has become a critical challenge for maintenance and functionality of civil structures. Apart from global warming, climate change-induced frost periods can seriously affect dynamic behaviour of bridges. From a meteorological perspective, instability in the polar vortex and weakening of the jet stream are critical climatic phenomena that allow cold Arctic air to move toward mid-latitudes, leading to unexpected and severe freezing events. Under such circumstances, vibration features of bridges, especially modal frequencies, alter significantly. Generally, freezing temperatures cause sharp shifts in bridge modal frequencies due to sudden stiffening, particularly deck asphalts. Because these impacts can obscure true structural changes, produce false damage alarms, and reduce the reliability of long-term structural health monitoring (SHM), this paper proposes machine learning-aided data normalizers to mitigate climate change-induced frost conditions on modal frequencies of bridges during their SHM programs. Given the availability of temperature records, supervised data normalizers in terms of classifiers are trained by using both the modal frequencies and temperatures. In contrast, when temperature records are unavailable, unsupervised data normalizers in terms of reconstruction-based models are developed by the only bridge modal frequencies. Both types of data normalizers extract residuals between the measured and reconstructed modal frequencies, serving as normalized dynamic features free from frost effects. The proposed hybrid machine learning framework is validated using long-term monitoring data from concrete and steel bridges subjected to freezing conditions. Results show that this framework effectively eliminates frost-induced frequency jumps, enhancing the stability, accuracy, and climate resilience of long-term SHM programs.