Suchergebnisse

Natural disasters are unavoidable and can cause serious damage to bridges, which may lead to catastrophic losses, both human and economic. Therefore, the assessment of bridges exposed to these events is of paramount importance to identify possible mitigation needs. The objective of the present work is to present consistent tools that may allow us to obtain the failure probability of a masonry arch bridge under a flood event, leading to local scour. Surrogate models were implemented to ease the computational cost of the probabilistic analysis. Moreover, a stochastic parametric analysis based on the geotechnical properties of the soil components of masonry arch bridges located in Portugal was performed. The results show the failure mechanism of the masonry arch bridges when subjected to scour-induced settlements and the influence of soil density on the failure probability obtained for different flow discharge values and angles of attack. The presented methodology and derived fragility curves can be used to assess bridge performance under a flood event, thus providing useful information for bridge management and monitoring. ; This work was partly financed by FCT/MCTES through national funds (PIDDAC) under the R&D Unit Institute for Sustainability and Innovation in Engineering Structures (ISISE), under reference UIDB/04029/2020. The second author would like to thank FCT—Portuguese Scientific Foundation—for research grant SFRH/BD/144749/2019. This project has received funding from the European Union's Horizon 2020 research and innovation programmed under grant agreement No 769255. This document reflects only the views of the author(s). Neither the Innovation and Networks Executive Agency (INEA) nor the European Commission is in any way responsible for any use that may be made of the information it ...

The accuracy of forecasting models for the prediction of an infrastructure's deterioration process plays a significant role in the estimation of optimal maintenance, rehabilitation, and replacement strategies. Numerous approaches have been developed to overcome the limitations of existing forecasting models. In this article, a direct comparison is made between different models using the same input data to derive conclusions of their distinct performance. The models selected for the comparison were Markov, semi-Markov, and hidden Markov models together with artificial neural networks (ANNs), which have been reported in literature as reliable deterioration prediction models. A quality of fit was performed to measure how well the observed data corresponded to the predicted values, and therefore objectively compare the performance of each model. The results demonstrated that the most accurate prediction was accomplished by the ANN model. Nevertheless, all models presented differences with respect to typical values of concrete decks life expectancy, which is attributed to the inherent difficulties of the database. Additionally, the problem of the visual inspection subjectivity was also regarded as one of the potential causes for the found deviations. Therefore, this article also discusses the shortcomings of current condition assessment practices and encourages future bridge management systems to replace the classical methods by more sophisticated and objective tools. ; The first, third and fourth authors also acknowledge that, this work was partly 574 financed by FEDER funds through the Competitivity Factors Operational 575 Programme - COMPETE and by national funds through FCT Foundation for 576 Science and Technology within the scope of the project POCI-01-0145-FEDER- 577 007633. This project has received funding from the European Union's Horizon 578 2020 research and innovation programme under grant agreement No 769255. This 579 document reflects only the views of the author(s). Neither the Innovation and 580 ...

As bridges provide crossing at critical points such as waterways, valleys and other type of physical obstacles, they are invaluable assets within a rail network. However, these infrastructures are exposed to several hazards of different natures that compromise their structural safety and ultimately lead to critical events. Accord-ing to literature, there are commonly five levels of assessment of safety with increasing complexity per level. Level 1, consisting on an assessment based on partial coefficients, is the simplest one, whereas level 5 is the most complex involving a combination of non-linear analysis and probabilistic assessment. Within that scope, this paper analyses the structural safety of a pre-stressed reinforced concrete railway bridge located in Portugal, by using a combination of linear elastic and probabilistic analysis procedures. The proposed framework is adaptable and suitable for common safety analysis. The obtained reliability index is compared to threshold values given by different standards which validates that the bridge ensures the safety at ultimate limit state. The reliability levels along time are also determined assuming a reduction in the cross-section area of the steel reinforcement, resulting from corro-sion initiation based on onsite inspection evidences. From the analysis of this scenario over time, it may be concluded that the bridge under study exhibits sufficient load carrying capacity during its planned service life even under the proposed damage. ; The third author would like to thank FCT – Portuguese Scientific Foundation for the research grant PD/BD/128015/2016 under the PhD program "Innovation in Railway System and Technologies- iRail". This work was partly financed by FEDER funds through the Competitivity Factors Operational Programme - COMPETE and by national funds through FCT Foundation for Science and Technology within the scope of the project POCI-01- 0145-FEDER-007633. This project has received funding from the European Union's Horizon 2020 research and innovation ...