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Aka, A, Isah, A D, Eze, C J and Timileyin, O (2019) Application of lean manufacturing tools and techniques for waste reduction in Nigerian bricks production process. Engineering, Construction and Architectural Management, 27(03), 658–79.

Andary, E G, Abi Shdid, C, Chowdhury, A and Ahmad, I (2019) Integrated project delivery implementation framework for water and wastewater treatment plant projects. Engineering, Construction and Architectural Management, 27(03), 609–33.

Golizadeh, H, Hosseini, M R, Martek, I, Edwards, D, Gheisari, M, Banihashemi, S and Zhang, J (2019) Scientometric analysis of research on “remotely piloted aircraft”. Engineering, Construction and Architectural Management, 27(03), 634–57.

Gravina da Rocha, C, El Ghoz, H B and Jr Guadanhim, S (2019) A model for implementing product modularity in buildings design. Engineering, Construction and Architectural Management, 27(03), 680–99.

Liao, L, Teo Ai Lin, E and Low, S P (2019) Assessing building information modeling implementation readiness in building projects in Singapore. Engineering, Construction and Architectural Management, 27(03), 700–24.

Liu, Q, Ye, G and Feng, Y (2019) Workers’ safety behaviors in the off-site manufacturing plant. Engineering, Construction and Architectural Management, 27(03), 765–84.

Rodriguez, F S, Spilski, J, Hekele, F, Beese, N O and Lachmann, T (2019) Physical and cognitive demands of work in building construction. Engineering, Construction and Architectural Management, 27(03), 745–64.

Xu, Y and Turkan, Y (2019) BrIM and UAS for bridge inspections and management. Engineering, Construction and Architectural Management, 27(03), 785–807.

Zarghami, S A and Gunawan, I (2019) A fuzzy-based vulnerability assessment model for infrastructure networks incorporating reliability and centrality. Engineering, Construction and Architectural Management, 27(03), 725–44.

  • Type: Journal Article
  • Keywords: Centrality; Fuzzy inference system; Infrastructure networks; Reliability; Vulnerability;
  • ISBN/ISSN: 0969-9988
  • URL: https://doi.org/10.1108/ECAM-10-2018-0437
  • Abstract:
    The purpose of this paper is to attempt to shift away from an exclusive probabilistic viewpoint or a pure network theory-based perspective for vulnerability assessment of infrastructure networks (INs), toward an integrated framework that accounts for joint considerations of the consequences of component failure as well as the component reliability. Design/methodology/approach This work introduces a fuzzy inference system (FIS) model that deals with the problem of vulnerability analysis by mapping reliability and centrality to vulnerability. In the presented model, reliability and centrality are first fuzzified, then 16 different rules are defined and finally, a defuzzification process is conducted to obtain the model output, termed the vulnerability score. The FIS model developed herein attempts to explain the linkage between reliability and centrality so as to evaluate the degree of vulnerability for INs elements. Findings This paper compared the effectiveness of the vulnerability score in criticality ranking of the components against the conventional vulnerability analysis methods. Comparison of the output of the proposed FIS model with the conventional vulnerability indices reveals the effectiveness of the vulnerability score in identifying the criticality of components. The model result showed the vulnerability score decreases by increasing reliability and decreasing centrality. Practical implications Two key practical implications for vulnerability analysis of INs can be drawn from the suggested FIS model in this research. First, the maintenance strategy based on the vulnerability analysis proposed herein will provide an expert facilitator that helps infrastructure utilities to identify and prioritize the vulnerabilities. The second practical implication is especially valuable for designing an effective risk management framework, which allows for least cost decisions to be made for the protection of INs. Originality/value As part of the first contribution, we propose a novel fuzzy-based vulnerability assessment model in building a qualitative and quantitative picture of the vulnerability of INs. The second contribution is especially valuable for vulnerability analysis of INs by virtue of offering a key to understanding the component vulnerability principle as being constituted by the component likely behavior as well as the component importance in the network.