Abstract
Resilience indicators are a convenient tool to assess the resilience of engineering systems. They are often used in preliminary designs or in the assessment of complex systems. This paper introduces a novel approach to assess the time-dependent resilience of engineering systems using resilience indicators. The temporal dimension is tackled in this work using the Dynamic Bayesian Network (DBN). DBN extends the classical BN by adding the time dimension. It permits the interaction among variables at different time steps. It can be used to track the evolution of a system’s performance given an evidence recorded at a previous time step. This allows predicting the resilience state of a system given its initial condition. A mathematical probabilistic framework based on the DBN is developed to model the resilience of dynamic engineering systems. A case study is presented in the paper to demonstrate the applicability of the introduced framework.
Highlights
Research on disaster resilience has recently been fostered due to the noticeable increase in the number of natural and human-caused disasters
A Dynamic Bayesian Network (DBN) model can be obtained by expert knowledge, from a database using a combination of machine-learning techniques, or both
Unlike the static resilience analysis which assumes a constant state of a system and measures the resilience by a static quantity, the dynamic resilience analysis models the evolvement of the system with time
Summary
Research on disaster resilience has recently been fostered due to the noticeable increase in the number of natural and human-caused disasters. While most of the research work focused on analyzing engineering resilience from a static point of view [6,7,8,9,10,11,12], there is a significant gap in assessing the dynamic nature of resilience through quantitative approaches. This paper proposes a dynamic framework to quantitatively assess the resilience of systems of dynamic nature (i.e., critical infrastructures, buildings, communities, etc.) [13]. The framework can be used to assess the resilience of multiple systems at once and it adopts the DBN as an inference tool. A DBN model can be obtained by expert knowledge, from a database using a combination of machine-learning techniques, or both These properties make the DBN formalism very useful in the disaster resilience domain as this https://doi.org/10.10 51/matecconf /2019281010 08 domain has an abundance of both expert knowledge and databases records. Results show the ability of the framework to dynamically model complex systems, even when data is scarce
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
