Climate-induced deterioration prediction for bridges: an evolutionary computing-based framework

Bridge deterioration is attributed to inadequate maintenance budgets, ineffective restoration strategies, and rapidly changing climatic conditions. Considering that the latter can significantly exacerbate the deterioration initiated by the formers, there is a pressing need to develop climate change-informed management and resilience quantification/enhancement strategies for bridges. Although climate variability and bridge deterioration are logically related, explicit mathematical representation of their connection is still missing. The current study proposes a predictive climate-induced bridge deterioration framework based on data-driven approaches. Through the application of this framework, a closed-form expression linking bridge condition to its intrinsic characteristics, traffic volumes, and climate indices is generated. To demonstrate the framework’s utility, it was applied to evaluate the conditions of concrete bridges in Ontario, Canada. The generated model efficiently reproduced the actual bridge conditions between 2000 and 2020 considering a 70/30 training-to-testing splitting scheme. Further interpretation of the model demonstrated that the variability in bridge deterioration prediction is controlled by intrinsic characteristics followed by the climate characteristics and loading conditions, respectively. The model was subsequently applied between years 2022 and 2050, revealing the accelerated deterioration in the near future considering different climate change projections. Attributed to its generic nature, the presented framework can be applied to other infrastructure systems, when relevant data is available, to devise effective climate-informed management, resilience quantification and enhancement, and rehabilitation strategies.