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This paper aims to develop a novel risk analysis model that uses both participatory and computerized techniques to capture and model the dynamic of risk impact propagation and interaction pattern.

In this research, an integrated model, applying modified participatory method and novel dichotomize procedure in the perspectives of social network topological analysis, is developed.

Based on the analysis output, it is found that; (i) the risk propagation is characterized by its dynamic and non-linear impact pattern, and (ii) the risk interaction is distinguished based on the degree of connectedness between various risks.

This research assumes that the risk impact propagation and interaction pattern within the risk network are static. Further research is required to analyze the risk network in dynamic circumstances.

This research contributes in delivering practical tools that could potentially provide a further path for developing mitigation strategy and policies that seek to address the complexity of risk phenomena, and thus enhance community resilience.

This research reveals some underlying patterns of how the risk impact propagation and interaction pattern are structured. Thus, it can help decision-makers make formal arrangements of particular urban infrastructure (UI) governance visible toward building risk plan and mitigation strategies.

This research contributes to filling the risk management knowledge gap. It is suggested that analyzing risk using a network approach is suited to capture the intricate processes that shape the complexity of UI risk structural network. By validating the model, this research shows the applicability and capability of the model to improve both the RA accuracy and decision making effectiveness towards risk mitigation plan and strategy.

The complexities in strait-crossing cable-stayed bridge project are increasing the risks. This study aims to identify and analyze the significant and worth-considered construction risks of the first, biggest and longest spanned strait-crossing bridge project in Indonesia.

As many as 32 risk events were identified and determined as the risks that exist and can be represented in the Suramadu bridge project context. Data was collected through a design-based questionnaire disseminated to experts involved in the project as well as semi-formal interviews. Several quantitative methods were applied to analyze the significant risks, such as relative importance index, Spearman’s rank correlation test and Mann–Whitney U test.

The analyses reveal that “unexpected natural behavior” confirmed by both contractor and consultant parties is the most significant and crucial risk event. Another risk event found to be significant is the “delayed payment.” On the other hand, it is also found that several risks within the legal category are found to be less significant compared to other major risk events.

The results of the present research should be interpreted in the context of several limitations. Given these possible concerns regarding the generalizability of the findings, along with the relatively low rate of participants in the current research, additional studies are needed to provide a more complete picture of stakeholder perceptions who are involved directly in the construction environment as well as to identify more construction risks specifically in the large-scale bridge project.

This study has provided fundamental contributions to the body of knowledge and practical implication to promote and assist decision-makers toward developing a comprehensive risk assessment of a large-scale bridge project.

The analyses of outcomes and discussion, as well as the findings of this research, have shed light on the construction risks understanding, which contributes to delivering a theoretical framework for achieving large-scale bridge project success.

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.

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.

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.

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.

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.

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.

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.