Purpose
The purpose of this paper is to develop a novel risk analysis method named fuzzy critical risk analysis (FCRA) for assessing the infrastructure risks from a risk-community network perspective. The basis of this new FCRA method is the integration of existing risk magnitude analysis with the novel risk impact propagation analysis performed in specific infrastructure systems to assess the criticality of risk within specific social-infrastructure interrelated network boundary.
Design/methodology/approach
The FCRA uses a number of scientific methods such as failure mode effect and criticality analysis (FMECA), social network analysis (SNA) and fuzzy-set theory to facilitate the building of risk evaluation associated with the infrastructure and the community. The proposed FCRA approach has been developed by integrating the fuzzy-based social network analysis (FSNA) method with conventional fuzzy FMECA method to analyse the most critical risk based on risk decision factors and risk impact propagation generated by various stakeholder perceptions.
Findings
The application of FSNA is considered to be highly relevant for investigating the risk impact propagation mechanism based on various stakeholder perceptions within the infrastructure risk interrelation and community networks. Although conventional FMECA methods have the potential for resulting in a reasonable risk ranking based on its magnitude value within the traditional risk assessment method, the lack of considering the domino effect of the infrastructure risk impact, the various degrees of community dependencies and the uncertainty of various stakeholder perceptions made such methods grossly ineffective in the decision-making of risk prevention (and mitigation) and resilience context.
Research limitations/implications
The validation of the model is currently based on a hypothetical case which in the future should be applied empirically based on a real case study.
Practical implications
Effective functioning of the infrastructure systems for seamless operation of the society is highly crucial. Yet, extreme events resulted in failure scenarios often undermine the efficient operations and consequently affect the community at multiple levels. Current risk analysis methodologies lack to address issues related to diverse impacts on communities and propagation of risks impact within the infrastructure system based on multi-stakeholders’ perspectives. The FCRA developed in this research has been validated in a hypothetical case of infrastructure context. The proposed method will potentially assist the decision-making regarding risk governance, managing the vulnerability of the infrastructure and increasing both the infrastructure and community resilience.
Social implications
The new approach developed in this research addresses several infrastructure risks assessment challenges by taking into consideration of not only the risk events associated with the infrastructure systems but also the dependencies of various type communities and cascading effect of risks within the specific risk-community networks. Such a risk-community network analysis provides a good basis for community-based risk management in the context of mitigation of disaster risks and building better community resilient.
Originality/value
The novelty of proposed FCRA method is realized due to its ability for improving the estimation accuracy and decision-making based on multi-stakeholder perceptions. The process of assessment of the most critical risks in the hypothetical case project demonstrated an eminent performance of FCRA method as compared to the results in conventional risk analysis method. This research contributes to the literature in several ways. First, based on a comprehensive literature review, this work established a benchmark for development of a new risk analysis method within the infrastructure and community networks. Second, this study validates the effectiveness of the model by integrating fuzzy-based FMECA with FSNA. The approach is considered useful from a methodological advancement when prioritizing similar or competing risk criticality values.
