Adaptation of traditional risk-based methodology for slopes to probabilistic-based approach integrating surrogate models

Due to the economic importance that railways systems have in Europe, it is pertinent to ensure good performance and long-term safety. Failure of earth structures (e.g., slopes) often results in major economic consequences, and it is a result of the uncertainties associated with these structures and their failure modes due to a given hazard. Nowadays, different methodologies can be used to assess slopes during operational phases, but often the required information to achieve a reliable assessment may provide the methodologies inapplicable, especially when assessing multiple assets. This research uses methodologies that have been implemented in the industry and adapts them to a probabilistic approach toward risk assessment, supported by the implementation of kriging surrogate model, thus improving its reliability while maintaining the same level of information and computational cost required for its application. A soil cutting located in the Lisbon (Portugal) was selected as case study. Seismic fragility curves are obtained, and a moderate risk level is obtained. The derived fragility curves are based on peak ground acceleration and were developed for different combinations of geometric and geotechnical parameters. The methodology provides useful information for prioritizing assets and taking preventive actions to maintain the desired performance of the railway system. ; This research was conducted under the project ‘‘Ferrovia 4.0’’ (POCI-01-0247-FEDER-046111; Lisboa-01-0247-FEDER-046111), by ‘‘Ferrovia’’ Consortium, and financed by European Regional Development Fund (ERDF), through the Incentive System to Research and Technological development, within the Portugal2020 Competitiveness and Internationalization Operational Program. This work was partly financed by FCT/MCTES through national funds (PIDDAC) under the R&D Unit Institute for Sustainability and Innovation in Structural Engineering (ISISE), under reference UIDB/04029/2020, and under the Associate Laboratory Advanced Production and Intelligent Systems ...