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Amadi, C, Carrillo, P and Tuuli, M (2019) PPP projects: improvements in stakeholder management. Engineering, Construction and Architectural Management, 27(02), 544–60.

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Huo, X, Yu, A T W, Zezhou, W and Jayantha, W M (2019) Site planning and design of green residential building projects: case studies in China. Engineering, Construction and Architectural Management, 27(02), 525–43.

Hyung, W, Kim, S and Jo, J (2019) Improved similarity measure in case-based reasoning: a case study of construction cost estimation. Engineering, Construction and Architectural Management, 27(02), 561–78.

Johari, S and Jha, K N (2019) Challenges of attracting construction workers to skill development and training programmes. Engineering, Construction and Architectural Management, 27(02), 321–40.

Lam, K C and Chow, W S (1999) The significance of financial risks in BOT procurement. Building Research & Information, 27(02), 84–95.

Li, X, Wang, X and Lei, L (2019) The application of an ANP-Fuzzy comprehensive evaluation model to assess lean construction management performance. Engineering, Construction and Architectural Management, 27(02), 356–84.

Mellado, F, Lou, E C and Becerra, C L C (2019) Synthesising performance in the construction industry. Engineering, Construction and Architectural Management, 27(02), 579–608.

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Robinson, H S and Scott, J (2009) Service delivery and performance monitoring in PFI/PPP projects. Construction Management and Economics, 27(02), 181–97.

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Salvado, F, Almeida, N and Vale e Azevedo, A (2019) Aligning financial and functional equivalent depreciations rates of building assets. Engineering, Construction and Architectural Management, 27(02), 441–57.

van Mossel, H-J and Straub, A (2009) The need for customizing maintenance services in social housing. Construction Management and Economics, 27(02), 135–51.

Wang, X, Shi, L, Wang, B and Kan, M (2019) A method to evaluate credit risk for banks under PPP project finance. Engineering, Construction and Architectural Management, 27(02), 483–501.

Warsame, A (2009) Organizational modes in the residential building sector in Sweden. Construction Management and Economics, 27(02), 153–63.

Wei, H, Minggang, H and Qun, H (1999) Quasi-static testing of composite masonry construction. Building Research & Information, 27(02), 120–3.

Yevu, S K and Yu, A T W (2019) The ecosystem of drivers for electronic procurement adoption for construction project procurement. Engineering, Construction and Architectural Management, 27(02), 411–40.

Zarghami, S A and Gunawan, I (2019) A domain-specific measure of centrality for water distribution networks. Engineering, Construction and Architectural Management, 27(02), 341–55.

  • Type: Journal Article
  • Keywords: Betweenness centrality; Centrality measures; Closeness centrality; Demand centrality; Eigenvector centrality; Percolation centrality; Water distribution networks;
  • ISBN/ISSN: 0969-9988
  • URL: https://doi.org/10.1108/ECAM-03-2019-0176
  • Abstract:
    In recent years, centrality measures have been extensively used to analyze real-world complex networks. Water distribution networks (WDNs), as a good example of complex networks, exhibit properties not shared by other networks. This raises concerns about the effectiveness of applying the classical centrality measures to these networks. The purpose of this paper is to generate a new centrality measure in order to stick more closely to WDNs features. Design/methodology/approach This work refines the traditional betweenness centrality by adding a hydraulic-based weighting factor in order to improve its fit with the WDNs features. Rather than an exclusive focus on the network topology, as does the betweenness centrality, the new centrality measure reflects the importance of each node by taking into account its topological location, its demand value and the demand distribution of other nodes in the network. Findings Comparative analysis proves that the new centrality measure yields information that cannot be captured by closeness, betweenness and eigenvector centrality and is more accurate at ranking the importance of the nodes in WDNs. Practical implications The following practical implications emerge from the centrality analysis proposed in this work. First, the maintenance strategy driven by the new centrality analysis enables practitioners to prioritize the components in the network based on the priority ranking attributed to each node. This allows for least cost decisions to be made for implementing the preventive maintenance strategies. Second, the output of the centrality analysis proposed herein assists water utilities in identifying the effects of components failure on the network performance, which in turn can support the design and deployment of an effective risk management strategy. Originality/value The new centrality measure, proposed herein, is distinct from the conventional centrality measures. In contrast to the classical centrality metrics in which the importance of components is assessed based on a pure topological viewpoint, the proposed centrality measure integrates both topological and hydraulic attributes of WDNs and therefore is more accurate at ranking the importance of the nodes.

Zhou, S, Ng, S T, Lee, S H, Xu, F J and Yang, Y (2019) A domain knowledge incorporated text mining approach for capturing user needs on BIM applications. Engineering, Construction and Architectural Management, 27(02), 458–82.

Zohrehvandi, S, Vanhoucke, M, Soltani, R and Javadi, M (2019) A reconfigurable model for implementation in the closing phase of a wind turbines project construction. Engineering, Construction and Architectural Management, 27(02), 502–24.