Impact of climate change on long-term damage detection for structural health monitoring of bridges

Abstract

The effects of operational and environmental variability have been posed as one of the biggest challenges to transit structural health monitoring (SHM) from research to practice. To deal with that, machine learning algorithms have been proposed to learn from experience based on a reference data set. These machine learning algorithms work well if the operational and environmental conditions under which the bridge operates do not change over time. Meanwhile, climate change has been posed as one of the biggest concerns for the health of bridges. Although the uncertainty associated with the magnitude of the change is large, the fact that our climate is changing is unequivocal. Therefore, it is expected that climate change can be another source of environmental variability, especially the temperature. So, what happens if the mean temperature changes over time? Will it significantly affect the dynamics of bridges? Will the reference data set used for the training of algorithms become outdated? Are machine learning algorithms robust enough to deal with those changes? This is a pioneering work on the impact of climate change on the long-term damage detection in the context of bridge SHM. A classifier rooted in machine learning is trained using one-year data from the Z-24 Bridge in Switzerland and tested with current and future data. Three climate change scenarios are assumed, centered in three future periods, namely 2035, 2060, and 2085. This study concludes that climate change may be seen as another source of operational and environmental variability to be considered when using machine learning algorithms for long-term damage detection.