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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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

As a response to the growing operational and disruptive threats to water distribution networks (WDNs), researchers have developed a vast array of methods for the reliability analysis of WDNs. In order to follow this growing number of methods, this paper reviews and documents in one place the historical developments in the reliability analysis of WDN.

A systematic literature review (SLR) is carried out to summarize the state-of-the-art research on reliability analysis of WDNs. In conducting this systemic literature review, the authors adopted an iterative approach to define appropriate keywords, analyze and synthesize data and finalizing the classification results.

First, the hydraulic approach to reliability analysis is currently pervasive, and relatively little academic research has addressed the topological reliability analysis of WDNs. Second, in order to provide a comprehensive picture of the network reliability, a different approach that integrates topological and hydraulic attributes seems a more effective method. Third, the conventional reliability analysis methods are only effective for demonstrating a snapshot of these networks at a given point in time. The availability of methods that enable researchers to evaluate the reliability in response to changes in its variables is still a major challenge.

The present paper facilitates future research in the reliability analysis of WDNs by providing a source of references for researchers and water utilities. Further, this article makes a contribution to the literature by offering a roadmap for future reliability analysis of WDNs by reviewing the evolution of the current reliability analysis methods throughout history.

The methods presently used for project stakeholder analysis have typically followed two distinct patterns: (1) a project-centric approach that places a project at the center, and subsequently, concentrates on dyadic relationships between the project and its stakeholders; (2) a network theory-based approach that emphasizes on the interconnections within the network of project stakeholders. The main contention of this study is built upon the premise that neither the sole analysis of dyadic relationships between a project and its stakeholders nor the stand-alone use of the network theory methods is adequate for reliable analysis of stakeholders.

This article proposes a model that bases the salience of stakeholders on their relationships with the project as well as on their interdependencies in the project. In doing so, this work explores the potential of a Fuzzy Inference System (FIS) to provide a comprehensive picture of stakeholder analysis. Using a real-world biodiversity project, this paper analyses the project stakeholders based on their possession of various attributes as well as the extent to which each individual stakeholder influences the entire connected network of all stakeholders.

A salient feature of the proposed FIS model is its ability to provide a high capacity for analyzing the results. The model is able to generate the input–output relationship surface view for stakeholder analysis. Further, unlike the traditional project stakeholder analysis methods that are linear, the proposed model is strongly nonlinear. This implies that change in the input variables of the fuzzy-based model is not expected to lead to a proportional change in the model output.

Two practical implications can be drawn from the presented stakeholder analysis model. First, confronted with mounting pressure to understand the stakeholder environment and to effectively manage stakeholders, project managers need to establish a sound stakeholder management strategy. The stakeholder analysis model developed herein casts a wider net for the critical ranking of stakeholders in a project, thereby providing a more accurate prioritization of the stakeholders. Second, while stakeholders independently require managerial attention, understanding the effect of competing and cooperative stakeholder interactions are unarguably of great importance. The presented model prompts the project managers to recognize not only the influence of key stakeholders on the project but also the interactions of multiple stakeholders within the stakeholder network.

The proposed stakeholder analysis model possesses several desirable features. First, it is not constrained to capturing only stakeholder attributes discussed in the example project provided in this study. The model is flexible and adaptable to all business and management contexts. Second, the stakeholder mapping in the model is not a function of a sole attribute but rather a cumulative effect of multiple stakeholder attributes. In fact, the power of the suggested model lies in its ability to incorporate the three aspects of stakeholder theory into a single model. Third, the presented model builds a quantitative and qualitative picture of the stakeholder salience. The suggested FIS model is capable of processing both qualitative perception of stakeholder attributes and quantitative analysis of the network of stakeholder interactions. This allows for a more comprehensive and synergistic utilization of model inputs.

The increased complexity of water distribution networks (WDNs) emphasizes the importance of studying the relationship between topology and vulnerability of these networks. However, the few existing studies on this subject measure the vulnerability at a specific location and ignore to quantify the vulnerability as a whole. The purpose of this paper is to fill this gap by extending the topological vulnerability analysis further to the global level.

This paper introduces a two-step procedure. In the first step, this work evaluates the degree of influence of a node by employing graph theory quantities. In the second step, information entropy is used as a tool to quantify the global vulnerability of WDNs.

The vulnerability analysis results showed that a network with uniformly distributed centrality values exhibits a lower drop in performance in the case of partial failure of its components and therefore is less vulnerable. In other words, the failure of a highly central node leads to a significant loss of performance in the network.

The vulnerability analysis method, developed in this work, provides a decision support tool to implement a cost-effective maintenance strategy, which relies on identifying and prioritizing the vulnerabilities, thereby reducing expenditures on maintenance activities.

By situating the research in the entropy theory context, for the first time, this paper demonstrates how heterogeneity and homogeneity of centrality values measured by the information entropy can be interpreted in terms of the network vulnerability.

The purpose of this paper is to review the different interpretations of four key performance indicators of water distribution networks (WDNs): reliability, resilience, redundancy and robustness. It then addresses a range of metrics which have been developed to assess the performance of critical infrastructures, in particular WDNs.

The paper provides a comprehensive review and categorization of performance indicators of WDNs. The main focus is on papers addressing performance indicators of water distribution systems, additionally papers on application of complex system approach to critical infrastructures are also included.

Due to this complexity, a wide range of interpretation of WDNs performance indicators exists in the literature. This represents a significant impediment toward universally accepted interpretation of these indicators Accurate assessment of WDNs’ performance depends on clear definition of system performance indicators as well as accurate quantifying of these indicators. The application of 18 metrics as a basis for assessing the system performance have been reviewed in this paper and none are particularly significant as standalone values. Combination of these indicators are required to accurately indicate the performance of WDNs.

The authors believe that this paper can be a valuable source of information for academic researchers and practitioners and suggests a roadmap for future works.

Although the literature is clear on what comprises effective project stakeholder management, communication between key stakeholders and project managers is often ineffective. Research is silent on stakeholders' insufficient knowledge of project management terminology, which is a barrier for such effective communication. This paper aims to identify the project management concepts that key stakeholders should understand to improve effective communication.

This paper employs a three-step research design. In the first step, based on threshold concept theory, the authors identify the key project management concepts through interviews with 20 project management practitioners, trainers and trainees. In the second step, the authors confirm the findings from the first step and identify effective approaches to enhance project stakeholders' communication through seven additional interviews with project stakeholders. In the third step, the authors construct a functional model of the research findings by employing a system-level modeling tool.

This research identifies five project management threshold concepts (TC) that are challenging for project stakeholders to understand (1) project benefits, (2) the iron triangle, (3) the critical path, (4) uncertainty and (5) project leadership. Following these knowledge barriers, the paper proposes unique approaches to develop effective project stakeholder communication.

This paper advances project stakeholder management research by identifying knowledge barriers and providing project managers with more effective approaches to better engage with their stakeholders.