Deakin University
ongkowijoyo-riskbased-2018.pdf (1.19 MB)

Risk-based resilience assessment model focusing on urban infrastructure system restoration

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journal contribution
posted on 2018-01-01, 00:00 authored by Citra Ongkowijoyo, Hemanta Doloi
A number of metrics in the past studies have been proposed and numerically implemented to assess particular system resilience during natural disaster and their recovery in the aftermath of the events. Among such performance measures, resilience is a reliable metric. The resilience assessment on the urban infrastructure system facing disturbances depends on comprehensive risk assessment. Nonetheless, it is found that previous studies lack of putting the risk assessment processes within the resilience assessment bodies. This study proposes a risk criticality-based resilient assessment model for scenario-based resilience assessment of infrastructure systems. The model accounts for uncertainties in the process including; the people expressions towards risks measures, risks magnitude and its impact to community estimation, and the dynamic of causality propagation pattern simulation. The proposed model is applied to water supply infrastructure case study with a hypothetical restoration scenario. The resilience level is assessed and determined based on the maximum resilience level the system can reach. Results of this analysis have shown that a holistic and integrated mitigation plans and strategies that seek to address complex phenomena towards system restoration is a critical requirement. The model will enable stakeholders to systemically assess the most-likely performance of the system during expected risk events.



Procedia engineering




1115 - 1122




Amsterdam, The Netherlands






The article was presented at the 7th International Conference on Building Resilience; Using scientific knowledge to inform policy and practice in disaster risk reduction, ICBR2017, 27 - 29 November 2017, Bangkok, Thailand

Publication classification

C1.1 Refereed article in a scholarly journal