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Fragility curves are a common tool to appraise the expected damage to critical infrastructure (CI) after an earthquake event. Previous studies offer fragility curve parameters for CI that are suitable for a vast range of systems, without an in-depth examination of the system architecture and subcomponents. These curves are applicable in cases where a thorough analysis is not required or when the information related to a single system is poor. This paper proposes an original approach and presents a comprehensive methodology for developing exclusive fragility curves for critical infrastructure systems. In the proposed methodology, the fragility curves are developed by a decomposition of the system into its main subcomponents and determination of the failure mechanisms. The derivation of the fragility parameters includes failure analysis for each damage state by a Fault Tree Analysis and approximation of the fragility parameters in accordance with the rate of exceedance. The implementation of the methodology is demonstrated by a case study with three alternatives of an oil pumping plant configuration. It was found that a change of a subcomponent has an effect on the derived values of the fragility parameters. Moreover, the variances in the fragility parameters have implications for the effectiveness of each alternative to resist different levels of severity.

The potential of machine learning (ML) tools for enhancing geotechnical analysis has been recognized by several researchers. However, obtaining a sufficiently large digital dataset is a major technical challenge. This paper investigates the use of transfer learning, a powerful ML technique, used for overcoming dataset size limitations. The study examines two scenarios where transfer learning is applied to tunnel support analysis. The first scenario investigates transferring knowledge between a ground formation that has been well-studied to a new formation with very limited data. The second scenario is intended to investigate whether transferring knowledge is possible from a dataset that relies on simplified tunnel support analysis to a more complex and realistic analysis. The technical process for transfer learning involves training an Artificial Neural Network (ANN) on a large dataset and adding an extra layer to the model. The added layer is then trained on smaller datasets to fine-tune the model. The study demonstrates the effectiveness of transfer learning for both scenarios. On this basis, it is argued that, with further development and refinement, transfer learning could become a valuable tool for ML-related geotechnical applications.