Does Climate Change Impact Long-Term Damage Detection in 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 based on the premise that the basis of the reference data does 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 Will the reference data set used for the training algorithms become outdated? Are machine learning algorithms robust enough to deal with those changes? This paper summarizes a preliminary study about the impact of climate change on the long-term damage detection performance of classifiers rooted in machine learning algorithms trained with one-year data from the Z-24 Bridge in Switzerland. The performance will be tested for three climate change scenarios in three future periods centered in 2035, 2060, and 2085.