Abstract
In the context of transportation infrastructures management, bridges are a critical asset due to their potential of becoming network’s bottlenecks. Unfortunately, this aspect has been emphasized due to several bridge failures, occurred in the last years worldwide, resulting from climate change-related hazards. Given this, it is important to establish accurate tools for predicting the structural condition and behavior of bridges during their lifetime. The present paper addresses this topic taking into account one of the statistical models most used and generally accepted in existing bridge management systems—Markov’s stochastic approach, which is further described. These statistical models are highly susceptible to the data that feeds them. Quite often, the step related with data cleaning and clustering is not properly conducted, being the most commonly available data sets adopted in bridge’s performance prediction. This paper presents a comparative analysis between different performance predictions. The only different between consecutive scenarios corresponds to the subset of bridges database used in each analysis. It was found that the development of good data clusters is of utmost importance. Contrarily, the use of poor clusters can lead to deceiving results which hinder the actual deterioration tendency, thus leading to wrong maintenance decisions.
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Acknowledgments
The authors would like to thank ISISE—Institute for Sustainability and Innovation in Structural Engineering (PEst-C/ECI/UI4029/2011 FCOM-01-0124-FEDER-022681). This work was co-financed by the Interreg Atlantic Area Programme through the European Regional Development Fund under SIRMA project (GrantNo. EAPA_826/2018).
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Santos, C., Fernandes, S., Coelho, M., Matos, J.C. (2021). The Impact of Clustering in the Performance Prediction of Transportation Infrastructures. In: Matos, J.C., et al. 18th International Probabilistic Workshop. IPW 2021. Lecture Notes in Civil Engineering, vol 153. Springer, Cham. https://doi.org/10.1007/978-3-030-73616-3_62
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DOI: https://doi.org/10.1007/978-3-030-73616-3_62
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