Comparison of stochastic prediction models based on visual inspections of bridge decks

Due to a considerable amount of information required to support the decision-making processes, an increasing number of infrastructure owners use computerized management systems. Bridges, being complex and having significant impact on society, have often been the foundation for the development of these systems. In order to manage bridges effectively, condition prediction models are incorporated to the core of decision-making processes. Many of developed and applied stochastic prediction models show certain limitations. The impact of these limitations on deterioration predictions cannot be objectively evaluated without direct comparison of prediction results. Hence, several stochastic prediction models based on condition ratings obtained from visual inspections of bridge decks are compared in this article. Models are described and implemented on the data of around 1100 reinforced concrete bridge decks from the ‘Infraestruturas de Portugal’, a state owned Portuguese general concessionaire for roadways and railways. The statistical analysis of different models revealed significant deviations, particularly in higher condition ratings. Results indicate limited prediction capability of a simple homogeneous Markov chain model when compared with time- and space-continuous models, such as the gamma process model.The authors of this paper would like to acknowledge the contribution to this research made by their colleagues at the Institute of Structural Engineering at University of Natural Resources and Life Sciences in Vienna, as well as by the colleagues at the Civil Engineering Department, University of Minho in Guimaraes. Likewise, the authors would like to acknowledge the Portuguese national roadway and railway manager "Infraestruturas de Portugal" for providing the support and necessary input data. The cooperation established in this article is based upon work from COST Action TU 1406, supported by COST (European Cooperation in Science and Technology). Moreover, the authors would also like to offer their gratitude for the support received through the project LeCIE "Lifecycle assessment for engineering structures in railway construction - strategies and methods", funded by OBB-Infrastruktur Bau AG, Vienna.info:eu-repo/semantics/publishedVersionhttp://www.tandfonline.com/doi/abs/10.3846/13923730.2017.132379