Article

From Risk Matrices to Risk Networks in Construction Projects

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Abstract

Risk management is considered as a vital process contributing to the successful outcome of a complex construction project in terms of achieving the associated project objectives. The widely used industrial practice in managing construction project risks is to assign probability and impact values to each risk and to map risks on a risk matrix. The main criticism of this practice relates to ignoring complex interdependencies between risks and using point estimates for probability and impact values. Furthermore, risks mapped on a matrix are deemed to influence a specific objective and there is a challenge involved in aggregating the impact of risks across multiple (conflicting) project objectives. Utilizing a data-driven Bayesian Belief Network methodology, in this paper we introduce a new process where the risks mapped on a risk matrix corresponding to each project objective are aggregated and modeled as a risk network, and a holistic impact of each risk is captured across the network by means of new risk metrics. The proposed methodology is demonstrated through a real application. The results specific to the two ranking schemes (assuming independence/interdependence of risks) are found to be negatively correlated, which substantiates the importance of utilizing an interdependency-based risk management process.

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... Consequently, there is a shortage of historical data pertaining to privacy breaches, which poses a significant challenge for conducting thorough and reliable privacy risk assessments [29]. While there exists a significant corpus of studies delving into risk assessment for SH [11], [12], [13], [14], [15], [16], [17], [18], [19], [20], [21], and a subset specifically focusing on HRA [18], [28], [30], [31], [32], [33], [34], [35], [36], [37], [38], [39], [40], [41], [42], [43], current methodologies fall short in effectively addressing the above three characteristics. This shortcoming in the literature highlights the critical gaps that our study seeks to fill. ...
... Sturgess et al. [18] proposed a capability-oriented framework to evaluate the HPR associated with a certain SHE, as previously noted and will be further discussed in the next sections. For the literatures that incorporate risk interactions, researchers have employed a range of methodologies to analyze risk interactions, including network theory [36], [37], [38], [39], analytic network process [40], Bayesian belief network [39], [42], [43], and Decision-Making Trial and Evaluation Laboratory [41]. ...
... Sturgess et al. [18] proposed a capability-oriented framework to evaluate the HPR associated with a certain SHE, as previously noted and will be further discussed in the next sections. For the literatures that incorporate risk interactions, researchers have employed a range of methodologies to analyze risk interactions, including network theory [36], [37], [38], [39], analytic network process [40], Bayesian belief network [39], [42], [43], and Decision-Making Trial and Evaluation Laboratory [41]. ...
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... So far, the traditional tools and different models used and developed in risk assessment in supply chain management ignore the complex interdependencies between risks and use point estimates for probability and impact values Dikmen, 2019, Qazi et al., 2021). A study by (Qazi and Dikmen, 2019) was conducted on construction projects, in which they proposed a novel methodology that is grounded in the theoretical framework of BBNs to prioritize risks, their methodology accounts for interdependent interactions of risks unlike the conventional risk matrix based tools. They demonstrated their methodology through a real application. ...
... They successfully proved the importance of utilizing an interdependencybased risk management process, as the results of two ranking schemes, assuming independence and interdependence of risks were correlated negatively. Therefore, for the purpose of this study, this research methodology will be based on the study conducted by (Qazi and Dikmen, 2019). ...
... This is done by shifting each risk to both extremes (high, low) and recording the overall risk exposure impact on the network. This concept is operationalized by means of a new risk metric, namely network propagation impact (NPI) (Qazi and Dikmen, 2019); equation (3) shows how to calculate the NPI: ...
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... There are several structure learning algorithms developed for constructing data-driven BN models, including PC, GTT, BS, Essential Graph Search, and Naïve (Qazi and Dikmen, 2021). PC algorithms are categorized into the typical constraint-based structure learning (Kjaerulff and Madsen, 2013). ...
... Through the capability of network propagating, we introduced several risk metrics to segregate the low-probability, highimpact, and high-probability, high-impact risks in the constructed BN. Thence, we instantiated each risk to its extreme state (yes and no) and captured the intensity of this spread by comparing the corresponding risk exposures of the entire network (Qazi and Dikmen, 2021). ...
... Risks that have a negligible impact on the network will not be prioritized regardless of their probability value (Qazi and Dikmen, 2021). To identify high-impact risks stemming from low-probability and high-probability, we quoted a risk metric namely network propagation impact (NPI) (Qazi and Dikmen, 2021). ...
Article
The coupling of multiple factors stemming from propagation effects and interdependency relationships among risks is prone to generate major accidents. It is of necessity to develop a feasible model with limited cases, which can generate reliable causal relationship evolution. To prioritize risk-influencing factors (RIFs) and investigate their relationships, we proposed a data-driven Bayesian Network (BN) model integrating physical information for risk analysis. Based on collected data, we combined prior knowledge with structure learning and parameter learning to obtain a BN model. In structure learning, we compared three structure learning algorithms including Bayesian search (BS), greedy thick thinning (GTT), and PC algorithm to obtain a robust directed acyclic graph (DAG). In parameter learning, we selected the expectation maximization (EM) algorithm to quantify the dependence and determine the probability distribution of node variables. This study provides a method to capture crucial factors and their interdependent relationships. To illustrate the applicability of the model, we developed a data-driven BN by taking the blowout accident as the case study. Eventually, we introduced vulnerability and resilience metrics for prioritizing risks through network propagation to conduct emergency plans and mitigation strategies.
... Risk management theory defines the value of a risk as the product of its probability of occurrence and its impact on a project [36,37]. Given the nature of the data collected for this study (use of the 5-point Likert scale), risk was depicted and assessed using a risk matrix [38][39][40]. The matrix has some limitations [41][42][43], such as modelling risk without correlation of risk factors. ...
... On the horizontal axis, the matrix evaluated the degree of impact (in an adjusted interval according to the mean score: almost uncertain 1,00-1,80; unlikely 1,81-2,60; fifty-fifty 2,61-3,40; likely 3,41-4,20; almost certain 4,21-5,00), while the vertical axis, the probability of occurrence was evaluated (in an adjusted interval according to the mean score: negligible 1,00-1,80; minor 1,81-2,60; moderate 2,61-3,40; significant 3,41-4,20; severe 4,21-5,00). The available literature provided three [38,39,42], four [45], or five risk zones [46] for evaluating the risk level, depending on the size of the matrix. For this research, a 5×5 matrix with three risk zones was selected, as recommended by Duijm [42]: High-risk zone, medium-risk zone and low-risk zone. ...
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... Bayesian belief networks have similarly been applied in construction management to model complexity and uncertainty (Dehghan and Khoramshahi 2008), leverage risk-related knowledge and expert judgment in tunnel works (Cárdenas et al. 2013(Cárdenas et al. , 2014a, and address disruption factors in urban engineering projects (Kembłowski et al. 2017a). They are used for risk matrices versus project objectives (Siemaszko et al. 2018) and risk factors throughout the project life cycle (Qazi and Dikmen 2021). In cost modeling, they help address the uncertainty of cost overrun factors (Fahirah et al. 2015) and develop risk factor and influence diagrams (Khanzadi et al. 2017). ...
... One of its key advantages lies in its ability to incorporate prior knowledge and expert opinions, which is particularly valuable in the construction industry where historical data may be limited or incomplete (Jitwasinkul et al. 2016;Leu et al. 2023a;Zhu et al. 2020). Bayesian networks can effectively model the intricate relationships and dependencies among various factors influencing construction projects, such as risk factors, performance indicators, and decision variables (Dikmen et al. 2020;Luo et al. 2020;Qazi and Dikmen 2021). This probabilistic framework enables construction professionals to assess risks, support decision-making, and predict project performance under uncertainty (Arabi et al. 2022;Moghayedi and Windapo 2023;Wang et al. 2022b). ...
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... Furthermore, adaptability issues arise, as these models may not efficiently adjust to new or evolving risks-a critical drawback in the dynamic environment of O&G projects where risk factors can change rapidly. Finally, Bayesian networks, known for handling uncertainties and capturing risk dependencies [33], depend heavily on accurately formulated causal relationships and necessitate extensive, specific data [34] [35], as well as a high level of statistical and domainspecific knowledge, which can be difficult to obtain for such complex systems. To this end, there is a need to develop a risk assessment model that (1) reduces the subjectivity and biases inherent in expert-based methods, (2) addresses the oversimplification of complex risks by considering the multidimensional effects on project outcomes, and (3) is less reliant on complicated mathematical, statistical, or coding techniques that may discourage decision-makers from engaging thoroughly with quantitative risk assessment. ...
... This input, derived from their past project experience, insights, and intuitions, plays a significant role in managing project risks. However, there is considerable uncertainty and bias regarding the efficiency and efficacy of decisions when analyzing and ranking risks qualitatively [33], [45]. This uncertainty stems from the subjective nature of qualitative analysis, which is influenced by personal judgments, experiences, preferences, and cognitive biases, all of which can significantly affect the efficacy of the risk assessment process [46], [46]. ...
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The aim of this research is to enhance Oil and Gas (O&G) construction risk assessment using Fuzzy-based Failure Model Effect Analysis (FMEA) through the lens of O&G project managers in the U.S. A mixed-method approach was adopted for data collection, analysis, and processing, including semi-structured interviews with project managers to identify the key risks facing O&G construction projects; a Fuzzy-based FMEA to quantitatively analyse the level of significance of O&G risks; surveys to rank the assessment dimensions of the developed model and their components; and open-ended surveys to validate and verify the assessment model and its outputs, further expanding on the root causes of significant risks based on the assessment outputs, and to propose mitigation strategies for these risks. The research identified 41 risk factors classified under six categories, namely: management, technical and quality, financial and economic, health, safety, environmental, legal, and stakeholders’ risks. In addition, the risk assessment revealed that non-compliance with PPE regulations emerged as the most significant risk factor across all categories of O&G risks. This study offers valuable insights by assisting practitioners in better understanding the significant O&G risks that need to be addressed to ensure the successful execution and completion of O&G projects.
... Secondly, the overall network characteristics show that block 1 (R 15 , R 3 , R 19 ), block 4 (R 12 , R 18 , R 24 , R 21 ), block 5 (R 6 , R 16 , R 5 ), and block 6 (R 8 , R 13 , R 14 ) are in the core position of the network risks and are core blocks of the whole life cycle risk network of international construction projects. Among the core risk factors, R 3 (political risk), R 19 (social risk), and R 15 (ecological and environmental risk) are located in core block 1; R 24 (market risk), R 21 (financial and tax risk), and R 18 (economic risk) are located in core block 4; and R 13 (management risk) and R 14 (contract risk) are located in core block 6. ...
... Through the analysis of the calculated results of the betweenness centrality of the lines of the whole life cycle risk network of international construction projects, it can be found that there are 103 lines with betweenness centrality larger than 0. Due to the limited space, only the top 10 relationships are taken and included in Table 5 below. As is shown in Table 5, 19 , R 21 !R 18 , R 3 !R 1 , and R 13 !R 22 are the 10 relationships with the greatest betweenness centrality of the lines, i.e., they are located with a high probability in other relationship lines and with 5strong control over the overall risk network transmission, and they are the key relationships for the whole life cycle risk transmission of international construction projects. Besides, the nodes corresponding to the relationship lines are closely related to the key risk factors R 3 (political risk), R 19 (social risk), R 13 (management risk), R 14 (contract risk), R 15 (ecological and environmental risk), R 24 (market risk), R 21 (financial and tax risk) and R 18 (economic risk) identified previously. ...
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The risk transmission process between international construction projects largely contributes to the dilemma of risk management of international construction projects. Firstly, this paper adopts methods such as literature review and brainstorming to identify the risks in international construction projects from all aspects and all stages. Connections between risks is built by the Delphi method and further construct the international construction project risk network. Combined with “ucinet”, a network visualization analysis tool, overall feature parameters and local feature parameters are presented for analysis as the focus. Starting from this, the risk transmission in complex construction projects is analyzed to identify key risks and transmission relationships and reveal inherent laws of risk transmission. Accordingly, when formulating risk prevention strategies for international engineering projects, it is proposed that measures to curb risk transmission should be effectively adopted from both key risks and their transmission relationships.
... During the simulation process, the project model is computed many times (iterated), with input values (e.g., cost estimate or activity duration) being randomly selected for each iteration from the probability distributions of these variables. A histogram (for example, total cost or project completion date) is calculated based on iterations [39][40][41]. Risk analysis (Oracle primavera) is used for Monte Carlo modeling and simulation. ...
... Determining the probability of each risk is the first step in the risk analysis stage [10,47,48]; the second stage is that each identified risk is assigned an estimated impact degree that can affect the final project scope (Table 7). Using the PRA software (P6 professional 16.1), the simulation is repeated hundreds or thousands of times, so the simulation result is very close to the expected degree of influence [40][41][42][43][44][45][46][47][48][49][50][51][52] (Table 8). The area colored in green indicates that the degree of importance of the risk (Probability × Impact) is very low, the area in yellow is of low importance, the area in dark green is of medium importance, the area in red is of high importance, and the area in dark red is of very high importance. ...
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Construction production in Russia and abroad (in Iraq) is facing various negative factors. The emergence of diverse factors in the implementation of investment and construction projects has an effect on the making of important decisions by the heads of construction enterprises, which may in the future be the cause of uncertainty and, as a result, the emergence of critical risks. The purpose of the study is to develop a methodology for identifying and assessing the influence of risk factors on the activities of construction enterprises in the implementation of investment projects. For the purposes of the study, mathematical and statistical models were used, such as the hierarchical analysis method and Monte Carlo, as well as the expert survey. The result of the study shows that the use of those models will significantly increase the success of construction enterprises by identifying various risk factors at the stage of construction and assessing their impact on these projects. The scientific and methodological approaches developed as a result of the study, methods for assessing risk factors, and appropriate compensatory measures to reduce or prevent the influence of these factors will significantly improve the organization of production activities of construction enterprises and will contribute to their successful development.
... The different likelihood and severity ratings can give the exact size of risk [32]. They independently evaluate the risk matrix and ignore the risks' interdependence [45]. In addition, as Qazi and Dikmen [45] stated, the risk matrix expresses the effects of risks such as time-cost-quality on the target of a specific project. ...
... They independently evaluate the risk matrix and ignore the risks' interdependence [45]. In addition, as Qazi and Dikmen [45] stated, the risk matrix expresses the effects of risks such as time-cost-quality on the target of a specific project. Therefore, measuring risks with only likelihood and severity parameters will not provide efficient results in the complete sense. ...
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... With the integration of the risk matrix, Naik et al. (2022) proposed a systematic approach to evaluate potential attacks and security risks in Self-Sovereign Identity (SSI) systems, aiming to the identification of specific attacks (faking identity, identity theft, and distributed denial of service) and providing mitigation strategies. The risk matrix, compared to other methods, exhibited remarkable flexibility as it can be tailored to suit specific applications and was adept at evaluating both the likelihood and severity of risks across various dimensions (Bao et al., 2021;Ibrahim et al., 2022;Qazi & Dikmen, 2019). Its visual clarity, ease of use, and adaptiveness make it a valuable tool in risk assessments, suggesting it could effectively complement other methodologies by clarifying and prioritizing risk factors (Aq et al., 2020;Zheng & Liu et al., 2023). ...
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The success of tunneling projects is crucial for infrastructure development. However, the potential leakage risk is particularly challenging due to the inherent uncertainties and fuzziness involved. To address this demanding challenge, a hybrid approach integrating the copula theory, cloud model, and risk matrix, is proposed. The dependence of multiple risk‐related influential factors is explored by the construct of the copula‐cloud model, and the diverse information is fused by applying the risk matrix to gain a crisp risk result. A case study is performed to test the applicability of the proposed approach, in which a risk index system consisting of nine critical factors is developed and Sobol‐enabled global sensitivity analysis (GSA) is incorporated to investigate the contributions of different factors to the risk magnitude. Key findings are as follows: (1) Risk statuses of the studied three tunnel sections are perceived as under grade I (safe), II (low‐risk), and III (medium‐risk), respectively, and the waterproof material aspect is found prone to deteriorating the tunnel sections. Furthermore, the proposed approach allows for a better understanding of the trends in the risk statuses of the tunnel sections. (2) Strong interactions between influential factors exist and exert impacts on the final risk results, proving the necessity of studying the factor dependence. (3) The developed neutral risk matrix presents a strong robustness and displays a higher recognition capacity in risk assessment. The novelty of this research lies in the consideration of the dependence and uncertainty in multisource information fusion with a hybrid copula‐cloud model, enabling to perform a robust risk assessment under different risk matrices with varying degrees of risk tolerance.
... Risk management is important for project success (Dikmen et al., 2022;Qazi & Dikmen, 2021), especially risk response (Ben-David & Raz, 2001). There are many studies about RRDs for diverse projects. ...
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... Despite being a crucial method for project success, studies have revealed that few project managers use risk management (Silva et al., 2019). It is presumably that risk management will result in project success (Qazi & Dikmen 2019;Baptestone & Rabechini Jr, 2018). ...
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All organizations and stakeholders would ideally like to see an information technology (IT) project managed successfully. Many researchers have strongly debated the importance of risk management in project management about the size of the project since it gives project managers a forward-looking view of risks and chances to increase the project's success. The main aim of the study is to determine how risk management parameters and their mediated effects impact the effectiveness of IT projects. Data was collected from 261 IT professionals involved in projects through a structured questionnaire and analyzed using regression and SEM to test their statistical significance and prove the hypothesis. The study arrived at some significant results which showed the relationship of Risk Identification and Risk Analysis on Risk Assessment, which impacts Project Success. It also showed that the success of the project depended on Stakeholders Tolerance and Risk Implementation. In addition to this, the study provides evidence that risk management does not influence the success of the project. The study's discovery of the intervening impact of risk management practices clarifies preconceived conceptions in the risk management sector.
... Although Monte Carlo simulation is widely used in project risk assessments (Tong et al. 2018;Taroun, 2014), as far as we know, the literature still does not contain references that use the data obtained in a qualitative analysis (data related to the probability and impact of each identified risk) to perform a quantitative risk analysis integrated into the project model. Only one research line by A. Qazi Qazi and Dikmen, 2021;Qazi and Simsekler, 2021) appears, where the authors propose a risk indicator with which they determine the level of each identified risk that concerns the established threshold. Similarly, Krisper (2021) applies the qualitative data of risk factors to construct probability functions, but once again falls in the error of calculating the expected value of the risk for risk prioritisation. ...
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The project managers who deal with risk management are often faced with the difficult task of determining the relative importance of the various sources of risk that affect the project. This prioritisation is crucial to direct management efforts to ensure higher project profitability. Risk matrices are widely recognised tools by academics and practitioners in various sectors to assess and rank risks according to their likelihood of occurrence and impact on project objectives. However, the existing literature highlights several limitations to use the risk matrix. In response to the weaknesses of its use, this paper proposes a novel approach for prioritising project risks. Monte Carlo Simulation (MCS) is used to perform a quantitative prioritisation of risks with the simulation software MCSimulRisk. Together with the definition of project activities, the simulation includes the identified risks by modelling their probability and impact on cost and duration. With this novel methodology, a quantitative assessment of the impact of each risk is provided, as measured by the effect that it would have on project duration and its total cost. This allows the differentiation of critical risks according to their impact on project duration, which may differ if cost is taken as a priority objective. This proposal is interesting for project managers because they will, on the one hand, know the absolute impact of each risk on their project duration and cost objectives and, on the other hand, be able to discriminate the impacts of each risk independently on the duration objective and the cost objective.
... Although Monte Carlo simulation is widely used in project risk assessments (Tong et al. 2018;Taroun, 2014), as far as we know, the literature still does not contain references that use the data obtained in a qualitative analysis (data related to the probability and impact of each identified risk) to perform a quantitative risk analysis integrated into the project model. Only one research line by A. Qazi Qazi and Dikmen, 2021;Qazi and Simsekler, 2021) appears, where the authors propose a risk indicator with which they determine the level of each identified risk that concerns the established threshold. Similarly, Krisper (2021) applies the qualitative data of risk factors to construct probability functions, but once again falls in the error of calculating the expected value of the risk for risk prioritisation. ...
Article
Full-text available
The project managers who deal with risk management are often faced with the difficult task of determining the relative importance of the various sources of risk that affect the project. This prioritisation is crucial to direct management efforts to ensure higher project profitability. Risk matrices are widely recognised tools by academics and practitioners in various sectors to assess and rank risks according to their likelihood of occurrence and impact on project objectives. However, the existing literature highlights several limitations to use the risk matrix. In response to the weaknesses of its use, this paper proposes a novel approach for prioritising project risks. Monte Carlo Simulation (MCS) is used to perform a quantitative prioritisation of risks with the simulation software MCSimulRisk. Together with the definition of project activities, the simulation includes the identified risks by modelling their probability and impact on cost and duration. With this novel methodology, a quantitative assessment of the impact of each risk is provided, as measured by the effect that it would have on project duration and its total cost. This allows the differentiation of critical risks according to their impact on project duration, which may differ if cost is taken as a priority objective. This proposal is interesting for project managers because they will, on the one hand, know the absolute impact of each risk on their project duration and cost objectives and, on the other hand, be able to discriminate the impacts of each risk independently on the duration objective and the cost objective.
... The performance (response) of the higher scale of construction safety risk behavior is typically controlled by the characteristics of the next higher scale, and a "factorlevel → event-level → system-level" risk chain is created when changes in risk factors cause the occurrence of risk events, which in turn cause changes in the risk level of the entire tunnel construction system [4]. Typical single-risk evaluation techniques and qualitative analysis including the risk matrix method [5] or hierarchical analysis [6], and quantitative analysis techniques, such as fault tree analysis [7], Monte Carlo simulation [8], and event tree analysis [9], are no longer able to fully describe the correlation characteristics between the scales of security risk behavior. The multiscale analysis method integrates the relevant scales to create a bridge that spans many scales by taking into account the cross-scale and cross-level properties of space and time. ...
Article
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Based on the WBS-RBS method, in this study, the risk factors corresponding to the construction risk events of an offshore tunnel foundation pit in Ningbo were identified, and the fuzzy comprehensive evaluation method was used to evaluate the construction safety risk of the project. The system-level risk value was obtained by using the risk event deformation resulting from changes in two factors, namely, “mechanical property of soil” and “stiffness of the envelope structure”, to calculate the new event-level risk value corresponding to the deformation using a finite element numerical model. The findings indicate that the tunnel project has a risk evaluation score of 62.78 and thus falls within the category of high-risk projects. A change in risk factors will alter the likelihood that risk events will occur, which affects the safety risk status of the entire project. When two factors are coupled, a project’s system-level risk can increase dramatically.
... The prevailing approach, criticized for oversimplifying risk assessments and neglecting interdependencies between risks, has spurred a call for more sophisticated methodologies. This critique resonates with the findings of Qazi and Dikmen (2021) 25 , who assert that the conventional risk matrix approach fails to capture the nuanced nature of project risks, particularly their complex interrelationships. ...
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Construction site hazards and safety issues present substantial risks to both construction workers and the wider public. Improper construction practices or cost-cutting measures can lead to severe consequences, such as structural failures, collapses, and the endangerment of both workers and nearby residents. This paper focuses on enhancing construction site safety by employing advanced data analysis methods, with a specific emphasis on the power of Bayesian Network analysis. Our research pioneers the integration of Bayesian Networks, augmented by data collected from wearable sensors, to comprehensively model accidents, particularly those involving auxiliary equipment. Through the incorporation of Bayesian Network analysis and real-time sensor data into the safety data integration process, our work addresses the pressing need for heightened safety measures within the construction industry. We aspire to exceed current safety standards and catalyze a transformation in construction practices, guided by a data-driven methodology that thoroughly investigates the multifaceted factors contributing to accidents, including those related to falls from heights.
... First, uncertainty is witnessed in the risk assessment process. A large portion of studies focused on risk assessment employ the use of probability (P) and impact (I) matrix to determine the influence levels of risks (Qazi and Dikmen, 2019). However, using the P-I matrix suffers from the limitation of exact quantification of expert knowledge to estimate risk P and I (Yazdani-Chamzini, 2014). ...
Article
Purpose Design cost overrun is one of the prominent factor that can impact the sustainable delivery of the project. It can be encountered due to a lack of information flow, design variation, etc. thereby impacting the project budget, waste generation and schedule. An overarching impact of this is witnessed in the sustainability dimensions of the project, mainly in terms of economic and environmental aspects. This work, therefore, aims to assess the implications of a technological process, in the form of building information modelling (BIM), that can smoothen the design process and mitigate the risks, thus impacting the sustainability of the project holistically. Design/methodology/approach The identified design risks in construction projects from the literature were initially analysed using a fuzzy inference system (FIS). This was followed by the focus group discussion with the project experts to understand the role of BIM in mitigating the project risks and, in turn, fulfilling the sustainability dimensions. Findings The FIS-based risk assessment found seven risks under the intolerable category for which the BIM functionalities associated with the common data environment (CDE), data storage and exchange and improved project visualization were studied as mitigation approaches. The obtained benefits were then subsequently corroborated with the achievement of three sustainability dimensions. Research limitations/implications The conducted study strengthens the argument for the adoption of technological tools in the construction industry as they can serve multifaceted advantages. This has been shown through the use of BIM in risk mitigation, which inherently impacts project sustainability holistically. Originality/value The impact of BIM on all three dimensions of sustainability, i.e. social, economic and environmental, through its use in the mitigation of critical risks was one of the important findings. It presented a different picture as opposed to other studies that have mainly been dominated by the use of BIM to achieve environmental sustainability.
... The probability-impact matrix is not only standard in sectors as varied as an industry, health and safety, or chemicals (among others) (Li, Bao and Wu, 2018) but also appears as a primary tool in risk analysis within project management (Qazi and Dikmen, 2021). ...
Chapter
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Qualitative project risk assessment is standard practice in project management and involves prioritising risks using a probability and impact matrix. Due to the shortcomings of using this tool for risk prioritisation (poor resolution, errors, suboptimal resource allocation or ambiguous inputs and outputs, among others), we propose a quantitative prioritisation of project risks in this article, analysing the impact of each risk on the project’s duration and cost objectives
... A plethora of approaches have been propagated to derive effective RRSs, including matrix-based method (Qazi & Dikmen, 2019), multi-criteria evaluation approach (Samadi et al., 2014), case-based reasoning approach (Okudan et al., 2021), work breakdown structure-based method (Cerezo-Narvaez et al., 2020) and optimization-based approach (Yan et al., 2022;Zhang & Fan, 2014). This section focuses primarily on the optimization-based approach, as the model proposed here is relevant. ...
Article
To promote the dynamic risk management of construction project portfolios (CPPs) and improve the effect of risk response, a simulation-optimization model is proposed. The model integrates System Dynamics and optimization to dynamically select risk response strategies (RRSs) and facilitate more refined resource allocation. Specifically, a System Dynamics sub-model simulates the risk level by capturing one-stage and 2 cross-stage risk interactions. Second, develop an optimization sub-model to select RRSs with resource allocation, which allows for partially allocated resources for RRSs. Afterward, integrate them into a simulation-optimization model. The closed-loop information flow between the two sub-models enables the assessment of the initial risk at a specific stage, the selection of optimal RRSs, and the updating of the dynamic impact of the risks of that stage on later stages. Finally, the proposed model is validated using a numerical example. The findings accentuate the significance of considering cross-stage risk interactions and optimizing resource allocation in the risk response of CPPs, and validate the feasibility and superiority of the model for solving the dynamic selection of RRSs.
... BBNs have been successfully used in different domains and industries, such as healthcare [4,50,51], education [52], safety analysis [53], finance [54], risk management [55], disaster management [56], and traffic management [57]. BBNs account for noise in stochastic events, ensuring that strong interactions are highlighted across the data. ...
Article
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Despite the exponential transformation occurring in the healthcare industry, operational failures pose significant challenges in the delivery of safe and efficient care. Incident management plays a crucial role in mitigating these challenges; however, it encounters limitations due to organizational factors within complex and dynamic healthcare systems. Further, there are limited studies examining the interdependencies and relative importance of these factors in the context of incident management practices. To address this gap, this study utilized aggregate-level hospital data to explore the influence of organizational factors on incident management practices. Employing a Bayesian Belief Network (BBN) structural learning algorithm, Tree Augmented Naive (TAN), this study assessed the probabilistic relationships, represented graphically, between organizational factors and incident management. Significantly, the model highlighted the critical roles of morale and staff engagement in influencing incident management practices within organizations. This study enhances our understanding of the importance of organizational factors in incident management, providing valuable insights for healthcare managers to effectively prioritize and allocate resources for continuous quality improvement efforts.
... BBN has been successfully employed in different fields such as medicine Medina et al., 2013), energy analysis (Tian et al., 2018), safety analysis (Zhang et al., 2014), traffic management (Pascale & Nicoli, 2011), and project and risk management (Qazi & Dikmen, 2021;Yet et al., 2016). In addition, various BBN applications were recently developed in the healthcare management context. ...
Article
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Patient experience is a key quality indicator driven by various patient- and provider-related factors in healthcare systems. While several studies provided different insights on patient experience factors, limited research investigates the interdependencies between provider-related factors and patient experience. This study aims to develop a data-driven Bayesian belief network (BBN) model that explores the role and relative importance of provider-related factors influencing patient experience. A BBN model was developed using structural learning algorithms such as tree augmented Naïve Bayes. We used hospital-level aggregated survey data from the British National Health Service to explore the impact of eight provider-related factors on overall patient experience. Moreover, sensitivity and scenario-based analyses were performed on the model. Our results showed that the most influential factors that lead to a high patient experience score are: (1) confidence and trust, (2) respect for patient-centered values, preferences, and expressed needs, and (3) emotional support. Further sensitivity and scenario analyses provided significant insights into the effect of different hypothetical interventions and how the patient experience is affected. The study findings can help healthcare managers utilize and allocate their resources more effectively to improve the overall patient experience in healthcare systems.
... Salado and Nilchiani, for example, have explained the importance of modeling risk dependences [50]. Several techniques have been applied to interdependency modeling of project risks, including structural equation modeling [21], [51], analytic network process [52], [53], [54], causal mapping [55], and Bayesian belief networks [56], [57]. In risk management, interdependency refers to the relationship and possible causeeffect relationship between risk items. ...
Article
Identifying and evaluating risks is one of the most essential steps in risk management in construction projects. When technical and managerial complexity increases in major transportation projects, this becomes even more important. Currently, project teams are assumed to identify risks mostly based on their experience and expertise. It is a major issue that some state departments of transportation (DOT) project teams lack the risk management experience. This study proposes using a data-driven approach to unify and summarize existing risk documents to create a comprehensive risk breakdown structure (RBS). As a preliminary risk identification framework, a consolidated RBS were developed, using content analysis of public risk reports by various DOTs. Then, comparison was made between the developed RBS with 70 US transportation projects' risk registers. Natural language processing techniques, bidirectional encoder representations from transformers, were employed to calculate semantic text similarity to determine what percentage of risks are covered by generic RBS. The results showed that 70 generic risk templates cover almost 81% of the identified risks in the database of 70 major projects which is about 6000 individual risks. Project parties can use these results to discuss and identify context-specific risks as a starting point. The study also determined the interactions between risk items based on their co-occurrence using historical data. Research findings revealed the importance of considering interdependencies between risks in future studies.
... The same principle can be extended to a portfolio of projects, except there are the added imperatives to recognize the interdependencies of the projects and the respective risk tradeoffs within the portfolio [2]. Cognizant of the debilitating effect of project risks on good project outcomes, which can severely impact cost and schedule variances [3], there is a need to exercise robust risk response decisions (RRDs) in projects, to select suitable response strategies to mitigate the risks through the practice of project portfolio management (PPM) [4], [5]. For instance, Geely became the second largest car maker in China, using goal-based PPM techniques focusing on the success of the single projects within project portfolios (SPPPs) [6]. ...
Article
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Project portfolio (PP) risk management entails making risk response decisions (RRDs) on single projects within PPs (SPPPs). However, the presence of uncertain project interdependencies (UPIs) increases the difficulty of RRDs. This study provides a decision model to make robust RRDs for SPPPs under UPIs, using a four-stage interval optimization model based on a two-tier risk-project network. A two-tier network is first constructed where the upper nodes denote the risks and the lower nodes denote the alternative projects. Next, a PP selection model is developed to obtain the probability of the projects being selected through simulation, in which the project interdependencies (PIs) are measured by the additional returns from the PP. Polytopic fuzzy linguistic term sets are formed to explore the strength of the relationships between the risks, and between the projects and risks. Third, the gravity model and the decision-making trial and evaluation laboratory are applied to prioritize the risks. Fourth, an interval optimization model under the uncertain response budget is constructed to yield the RRD outcome. The proposed model is validated through the RRDs for a Chinese construction firm. The results inform that, first, the mean utility of a decision maker (DM) considering UPIs fares better than those ignoring PIs and those considering PIs with certainty, second, the DM's mean utility considering PIs with certainty is still better than those ignoring PIs, and third, relying on the minimum budget threshold is more effective for reducing the uncertainty and improving the mean utility of a DM.
... Such data are hard to obtain in construction industry therefore, according to Dikmen et al. (2007), the difficulties have led experts to base their decisions on personal experience and subjective judgments, in fact qualitative tools are more common among practitioners. Above all, the qualitative risk matrix is extensively used in the construction industry (Qazi and Dikmen, 2019;Qazi et al., 2021). It is a two-dimensional mapping of probability and impact ratings associated with individual risks, where the product of these ratings yields the overall exposure of individual risks (Aven, 2017). ...
Article
The real estate redevelopment process is an important route for achieving the sustainable development goals established worldwide, but at the same time it represents a complex and not very transparent decision-making issue for the public and private subjects involved. In particular, for the private entrepreneurs it is generally considered more risky than new construction, therefore it requires a careful evaluation for avoiding losses. Most of the existent risk assessment tools provide for the analysis at the aggregated scales or require knowledge of many financial data of the project which are often not yet known in an ex-ante evaluation condition. Aim of the work is to define a structured framework for creating a Spatial Real Estate Risk Index (ISRR) through a spatial decision support system based on an innovative model that allows public and private subjects to carry out an effective ex-ante risk assessment at the sub-municipal territorial scale for public-private partnerships (PPP) risks. The proposed model adopts the flexibility of the Analytic Hierarchy Process multicriteria technique for managing qualitative and quantitative real estate data, the capability of indicators system to reduce the complexity of the real estate risk issues and the sleight of the Geographic Information System to clearly show the spatial distribution of the real estate risk. The ISRR is a territorial synthetic index that represents the “base risk”, i.e. the risk level that is expressed by the different features that characterize the demand and supply of the several urban areas within the city at the time of the evaluation. In order to test the usefulness of the proposed model, the application to the city of Rome (Italy) is described. The obtained results highlight the immediate ability to recognize the riskiest urban areas located on the northern and eastern boundaries of the city. The innovative contribution of the work is mainly represented by the analysis of the real estate risk carried out at the sub-municipal scale by using both quantitative and qualitative real estate data, therefore the proposed structured framework for creating the ISRR allows to immediately recognize the riskiest and least risky sub-municipal areas through an adequate risk map.
... For instance, in the study of Atasoy et al. (2022), the participants of the visualization workshop stated that the risk checklists and matrices are rather insufficient to transfer the strategies and underlying information behind the risk ratings assigned by the decision-makers. Similarly, Qazi and Dikmen (2021) criticized risk matrices for their incapability of utilizing knowledge-driven risk management and focusing on the individual point estimate of risks instead of considering a combination of different interdependent risk factors. Risk assessment schemes need to present different strategies, uncertainties, and connections between risk-related factors (Berner and Flage 2017). ...
Conference Paper
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Project risk communication comprises exchanging risk-related data and knowledge between different stakeholders so that they can comprehend the level of uncertainty and its consequences on project performance. Effective project risk communication enables decision-makers to interact and create a shared meaning using the available risk information. Information visualization can improve the communication process. Visual representations such as risk matrices, maps, bar charts, influence diagrams, and bow tie diagrams are widely utilised in the construction industry to convey findings of qualitative risk assessment based on subjective risk ratings, whereas decision trees, probability distribution diagrams, and Bayesian belief networks are used to present probabilistic risk analysis results. In this paper, a tool developed for visualizing risk and complexity data is introduced, and how it may be used to improve risk-based decision-making in a project is demonstrated with a case study. Findings demonstrate that visual aids may help decision-makers to make sense of risk and complexity in a project as well as their relative magnitudes and consequences. Visual representations such as the complexity and risk map depicted in this study may help convey risk-related information to top management in a concise and effective way. Although the findings cannot be generalised, ideas depicted in this paper may be used by other researchers to design visuals to communicate risk-related information for different contexts.
... Fu et al. investigated ways to develop the green building industry by analyzing the complex relationships between green building stakeholder behaviors using complex networks [30]. Qazi, A and Dikmen applied complex networks to risk assessment of complex construction, a data-driven Bayesian belief network approach to capture the impact of each risk overall [31]. Wambeke, BW and Liu, M applied complex networks to risk assessment in the management domain and concluded that overcommitment is the most significant factor leading to project schedule disruptions and reduced productivity [32]. ...
Article
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The industry chain of industrialized construction is a key strategy for promoting the sustainable performance of China’s construction industry. Its risk identification is the fundamental step to promote the development of the industry chain. The study was conducted in two phases. The first phase included an extensive literature review and case study analysis to document 32 key factors affecting the process of the industry chain of industrialized construction. In the second phase, 22 key factors influencing the development of the industry chain of industrialized construction in Shandong Province were screened through data collection and expert consultation. A complex network of industrialized construction risk associations (CNICRA) was developed to assess these risks by considering the interrelationship among risks, network nodes, and network edges, and the comprehensive degree indicators for improving the model’s accuracy and resolution. The results show that enterprise collaboration level is the most important factor in the industry chain of industrialized construction. The industrialized system is the most transmittable factor of risk. This study investigated a list of risks in the industrialization of construction, optimized a complex network of risk association, and provided theoretical support for risk management of the industry chain of industrialized construction and understanding of risk response strategies for decision makers.
... •A risk matrix (consequence/probability matrix): is a qualitative or semi-quantitative risk assessment technique, based on the degree of consequence (severity) and probability associated with the risk under analysis [16,49,[90][91][92][93][94][95][96][97]. Depending on the scenarios chosen and the elements identified in the situation under study, the aim is to estimate the degree of severity associated with the occurrence of each scenario considered in the scope of the risk characterization [89]. ...
Article
The growing increase in frequency and intensity of extreme weather events (EWEs) has a wide impact on energy systems and consumers, as energy transmission infrastructures - overhead power lines (OPL). The main objective of this work is to present the methodology of risk analysis of the EWEs on OPL in Portugal. The level of risk associated with each of the identified events is classified according to the probability of occurrence and consequences, in a risk matrix, and through the cause-and-effect analysis. It is concluded that, in Portugal, the extreme wind – corresponding to level 11 of the Beaufort Wind Force Scale, that is, values equal to or higher than 105.1 km h⁻¹ (29.22 m s⁻¹) – is the main factor that provoked the OPL disruption, between 28% and 40% of analyzed events associated with windstorms. Considering the occurrence of compound events - wind and rain - the probability of damage to OPL is between 21% and 30%; for wind and ice, it is 3%–5%. EWEs represent a serious risk for electrical systems, and it is necessary to develop effective solutions to minimize the associated impacts, such as the modification and upgrade of the current design and engineering standards, and electrical network monitoring.
... 21 Risk management is an important process for achieving project objectives. 22,23 Identifying, assessing, and managing construction project risks is indispensable for risk management. A successful project is timeand cost-effective, and has good construction quality. ...
Article
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Background: Construction development in Indonesia is growing rapidly, especially in Central Aceh District. Construction projects often experience risks, so that risk events can have a serious impact on the viability of the project. Project delays can result in cost overruns and project losses. Therefore, it is necessary to identify the factors causing project delays.The purpose of this study was to (1) identify the risk factors that cause delays in construction projects and (2) determine those particular risk factors that have a greater influence on construction projects. The location of this research was Central Aceh District. Methods: The data in this study were primary data in the form of a questionnaire and secondary data obtained from the literature related to this particular type of research. Questionnaires were distributed to respondents, namely contractor companies located in the Central Aceh District. The questionnaires were distributed to determine respondents' opinions about the level of influence of risk factors causing project delays. We used a validity test, reliability test, and descriptive analysis for data processing. Results: Based on the results of the study from 47 respondents, the “very high influence” category (Mode=5) for the tool malfunction factor, cost estimation inaccuracy, increased work costs, implementation of new technologies, details, accuracy and conformity to specifications that are not appropriate, worker quarrels, poor project planning and management, poor condition at locations and accessibility difficulty. Conclusions: Of the 80 risk factors that caused project delays, eight risk factors were found to have a very high influence on the implementation of construction projects in Central Aceh District. Practical implications: The results of this study provide knowledge to contractor companies about the delay factors that have the most influence on project implementation so that they are expected to be able to manage risks to avoid losses.
... 21 Risk management is an important process for achieving project objectives. 22,23 Identifying, assessing, and managing construction project risks is indispensable for risk management. A successful project is timeand cost-effective, and has good construction quality. ...
Article
Background: Construction development in Indonesia is growing rapidly, especially in Central Aceh District. Construction projects have distinctive characteristics and are very complex, so that risk events can have a serious impact on the viability of the project. Project delays can result in cost overruns and project losses. Therefore, it is necessary to identify the factors causing project delays.The purpose of this study was to (1) identify the risk factors that cause delays in construction projects and (2) determine those particular risk factors that have a greater influence on construction projects. The location of this research was Central Aceh District. Methods: The data in this study were primary data in the form of a questionnaire and secondary data obtained from the literature related to this particular type of research. Questionnaires were distributed to respondents, namely contractor companies located in the Central Aceh District. The questionnaires were distributed to determine respondents' opinions about the level of influence of risk factors causing project delays. We used a validity test, reliability test, and descriptive analysis for data processing. Results: Based on the results of the study from 47 respondents, the “very high influence” category (Mode=5) for the tool malfunction factor, cost estimation inaccuracy, increased work costs, implementation of new technologies, details, accuracy and conformity to specifications that are not appropriate, worker quarrels, poor project planning and management, poor condition at locations and accessibility difficulty. Conclusions: Of the 80 risk factors that caused project delays, eight risk factors were found to have a very high influence on the implementation of construction projects in Central Aceh District. Practical implications: The results of this study provide knowledge to contractor companies about the delay factors that have the most influence on project implementation so that they are expected to be able to manage risks to avoid losses.
... This is particularly important to devise a risk response strategy efficiently. Although numerous studies have been done in the space for risk assessment, it is still heavily dominated by the adoption of a probability (P) and impact (I) matrix for their evaluation (Qazi and Dikmen, 2019). However, using the P-I matrix for risk assessment has its fair share of limitations. ...
Conference Paper
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Risk management is an essential process for the successful execution of the project, and it is pertinent in achieving the project objectives and leading to its successful outcome. The nature of the construction industry, which is full of uncertainty and high capital investment, makes it notably more critical to address and manage the risks promptly. The most important part of the risk management process is identifying and assessing risks. However, the traditional Probability (P)-Impact (I) matrix used in their evaluation fails to account for the uncertainties witnessed in the determination of both P and I. This paper, therefore, uses a fuzzy approach to develop a risk assessment model. Further, the results of the generated model are compared with the conventional P-I matrix to show the effectiveness of the adopted fuzzy system. The data for the model development was collected from one of the metro-rail projects through a questionnaire. Subsequently, semi-structured interviews were conducted to identify the advantages of BIM in the project. The recognized BIM advantages were then correlated with the critical project risks to present it as the process for mitigating these risks. The study findings present the use of FIS to overcome the uncertainty in the risk management process, followed by the applicability of BIM as a risk mitigation tool. Establishing the role of BIM in the risk mitigation process can help in its wider acceptance in the construction industry.
... In many construction contracts, the concept of a potential risk is unclear and not precisely defined, which leads to many problems in the project and affects its timely completion within budget. Risk management is considered as a vital process contributing to the successful outcome of a complex construction project in terms of achieving the associated project objectives (Qazi and Dikmen, 2021). According to Baker et al. (2019), potential risks could be detrimental to the achievement of the project's objectives. ...
Article
Purpose The aim of this paper is to study the effect of strategic and project related potential risks on project delivery in Qatar. Two objectives have been defined. The first is to identify potential risk indicators (manifest variables) and categorize them (constructs/latent variables) based on a literature review, while the second is to examine and rank the relationships between the indicators and constructs by developing a structural equation model. Design/methodology/approach Twenty-five indicators were identified from the literature review and categorized into five groups. To collect the data, an online questionnaire was distributed in Qatar, and 116 responses were obtained. Structural equation modeling (SEM) was used to examine the model. The model that was developed based on the research hypothesis met goodness-of-fit, reliability and validity requirements. Findings The results showed that all constructs contributed well to the model and that the project parties (PPs) have the highest contribution with an effect weight of 0.209 followed by economic and legal (EL) conditions with an effect weight of 0.205. Site and safety (SS) conditions were third with an effect weight of 0.200 while environmental, natural and technological (ENT) conditions were fourth with an effect weight of 0.1989. The last ranked construct is political and social (PS) conditions with an effect weight of 0.186. Based on the outcome of the SEM, recommendations were provided to industry professionals in Qatar about mitigating the impact of potential risks on construction project. Originality/value To the authors' best knowledge, this is the first study to quantify the effects of strategic and project related risks on a construction project using SEM, considering the risk management indicators of SS, EL, ENT, PS in Qatar. The study's practical implications are to enlarge the project's risk management plan by considering the strategic and project related risks to enhance the project performance for the cost overrun and delay. The study is intended for construction projects in Qatar, but it can easily be adapted to other parts of the world given the local circumstances.
... When visualized in risk matrices, these risk scores are used to make sense of the level of risk in a project, and the overall risk level is referenced for the estimation of project contingency. Although extensively used in practice, this traditional qualitative method fails to take into account the interrelations between risk factors clearly, and assumptions behind ratings about mitigation strategies, contract conditions, risk allocation, etc., are not revealed (Qazi and Dikmen, 2021). Furthermore, subjective probabilities based on expert judgment demonstrate a "degree of belief" about an uncertain issue based on personal experience and values (Aven, 2016). ...
Article
Risk assessment in projects requires the integration of various information on project characteristics as well as external and internal sources of uncertainty and is based on assumptions about future and project vulnerability. Complexity is a major source of uncertainty that decreases the predictability of project outputs. In this research, the aim was to develop a decision-support tool that can estimate the level of risk and required contingency in a project by assessment of complexity factors as well as contextual information such as contract conditions and mitigation strategies. A process model and a tool were developed using the data of 11 mega construction projects. The tool was tested on a real project, and promising results were obtained about its usability. The tool has the potential to support decision-making during bidding in construction projects with its visualization and prediction features. On the other hand, as a limited number of cases and experts were involved in this study, findings on its performance cannot be generalized. The identified complexity and risk factors, proposed process model, and visual representations may help the development of similar decision-support tools according to different company needs.
... It was then defined more specifically by PMI (2017b) as "an uncertain event or condition that, once occurring, will positively or negatively affect at least one of the project objectives, such as time, cost, scope, or quality objectives". Risk management is considered a vital process contributing to the successful outcome of a complex construction project in terms of achieving the associated project objectives (Qazi and Dikmen, 2021). This process mainly involves risk identification, risk assessment, risk monitoring and risk response (PMI, 2017b), and risk monitoring throughout the risk assessment process to take timely response measures. ...
Article
The need for enterprises to manage project portfolio risks over the life cycle has become increasingly prominent. It is essential to evaluate and manage them to achieve project portfolios and organizations’ success. Unlike project risk, project portfolio risk is more complex and uncertain due to risk interactions. Risk management is unsatisfactory in project portfolios due to the lack of awareness of risk interactions and the life cycle. The purpose of this paper is to identify the critical risks of project portfolios over the life cycle considering risk interactions. We primarily verified 20 identified risks through a questionnaire survey and an expert interview method and evaluated the interactions among them using the Delphi method. Furthermore, risk interactions were analyzed using the social network analysis (SNA) methodology to determine the important risks. Finally, a comprehensive evaluation of important risks was carried out to identify critical risks according to the evaluation principles. The results identified six critical portfolio risks, two key risk contagion paths and revealed risk characteristics of different life cycle phases. This research considerably contributes to the body of knowledge pertaining to project portfolio management that will enable organizations that implement project portfolios and similar multi projects to emphasize critical risks.
Article
The interaction and propagation effects among risk-influencing factors (RIFs) often lead to maritime accidents. Therefore, to support maritime risk management in decision-making, it is imperative to conduct a quantitative risk assessment (QRA) of these accidents, which requires a methodology capable of capturing causal relationships in limited cases. To achieve this goal, this study proposes a data-driven Bayesian network (BN) model that integrates physical knowledge with the QRA. Based on the collected data, a combination of domain knowledge, structure learning, and parameter learning is employed to construct the model. In the data-driven phase, three structural learning algorithms including Bayesian search (BS), Greedy thick thinning (GTT), and Peter-Clark (PC) algorithms, were used in combination with the expectation maximization algorithm of parameter learning. Then, using four indices calculated by the confusion matrix, the most fitting algorithm for structure learning was chosen, and the confusion matrix was obtained by five-fold cross-validation. Finally, network propagation impact (NPI) is introduced to prioritize RIFs. Preventive measures and emergency plans are formulated based on the network resilience metric (NRM) and network vulnerability metric (NVM). This approach leverages the objectivity of a data-driven BN and integrates domain expertise to enhance model validity. A case study of collisions was conducted to demonstrate the applicability of the model. The data were sourced from 327 collision accident reports provided by the China Maritime Safety Administration, and the PC algorithm was chosen. Findings indicate that the “Management system”, “Supervision”, and “Crew training” RIFs are prioritized for preventive measures, whereas “Communication” receives more resources for emergency handling. The results suggest the formulation of risk management strategies for practitioners to improve maritime safety.
Article
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The automotive industry serves as a crucial support system for the economies of industrialized nations in their pursuit of international market competitiveness. Despite this industry's importance, most developing countries face the challenge of acquiring a reasonable economic position at the global level in the automotive sector for various reasons. The most salient reasons include inconsistent government policies, multiple taxes, investor insecurity, political instability, and currency devaluation. Identifying risks is crucial for a new entrant in the already-established automotive industry. The researchers have used multiple (qualitative and quantitative) techniques to identify and prioritize risks in setting up manufacturing plants. The efforts to tackle these identified risks are undertaken at the domestic and government levels to smoothen the establishment of industry. The risks are first identified, in the current study, by reviewing the previous literature and conducting interviews of the various stakeholders (automotive dealers, managers, and customers). Then this study uses Monte Carlo simulation (MCS) approach and develops a risk exposure (high, medium, or low) matrix for the automotive industry of Pakistan. The findings reveal that the depreciation of local currency against the foreign exchange, oligopoly nature of competition, and low market acceptability of new entrants due to their products' image are the most critical risks the automobile industry faces. These findings will help automotive research institutes in developing national policies that specifically aim to support new players in the automotive industry, particularly in addressing high-priority hazards. The results may also provide valuable insights for new participants seeking to identify and address the key challenges in the Pakistani automotive industry before entering it.
Article
Purpose In the context of Industry 4.0, intelligent construction technologies (ICT) represented by information technology and networking will undoubtedly provide new impetus to the development of the prefabricated building supply chain (PBSC), but they will also bring various potential risks. So far, there is a large lack of research on the comprehensive consideration of the risks associated with the intelligent transformation of PBSC based on the information sharing perspective, and the critical risks and interactions are still unclear, making it difficult to identify efficient risk mitigation strategies. Therefore, this paper aims to reveal the interactions between stakeholders and clarify the critical risk nodes and interactions in information sharing of PBSC (IS-PBSC), and propose targeted risk mitigation strategies. Design/methodology/approach Firstly, this paper creatively delineates the risks and critical stakeholders of IS-PBSC. Secondly, Data is collected through questionnaires to understand the degree of risks impact. Thirdly, with the help of NetMiner 4 software, social network analysis is conducted and IS-PBSC risk network is established to reveal critical risk nodes and interactions. Finally, further targeted discussion of critical risk nodes, the effectiveness and reasonableness of the risk mitigation strategies are proposed and verified through NetMiner 4 software simulation. Findings The results show that the critical risks cover the entire process of information sharing, with the lack of information management norms and other information assurance-related risks accounting for the largest proportion. In addition, the government dominates in risk control, followed by other stakeholders. The implementation of risk mitigation strategies is effective, with the overall network density reduced by 41.15% and network cohesion reduced by 24%. Research limitations/implications In the context of Industry 4.0, ICT represented by information technology and networking will undoubtedly provide new impetus to the development of the PBSC, but they will also bring various potential risks. So far, there is a large lack of research on the comprehensive consideration of the risks associated with the intelligent transformation of PBSC based on the information sharing perspective, and the critical risks and interactions are still unclear, making it difficult to identify efficient risk mitigation strategies. Originality/value Based on the results of risk network visualization analysis, this paper proposes an ICT-based IS-PBSC mechanism that promotes the development of the integration of ICT and PBSC while safeguarding the benefits of various stakeholders.
Chapter
Project management is a major activity in many domains. They involve many risks due to their being one-time events. Risk management tools are presented and demonstrated.
Article
In the last decade, it has been increasingly recognized that efforts to address project delays without understanding the relationships among risk factors (RFs) could be futile. In light of the limitations of previous research that has addressed this issue, this study seeks to contribute to the literature by developing a novel weighted fuzzy social network analysis (SNA)-based approach, which accounts for the likelihood of occurrence and the impact of RFs on one another. To validate its practicality, the approach is applied to a demonstrative study in which the causes of delays in real-world electrical installation projects were modeled and analyzed. In addition to providing a holistic view of relationships among RFs, the proposed approach enables engineering managers to identify the root causes of delays and their corresponding critical propagation paths. Compared to other approaches in the literature, the value of the approach lies in its simplicity and utility for supporting engineering managers in developing effective risk-mitigation plans to minimize the severity of project delays.
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Workspace demand changes across space and time, stressing the need to consider space as a limited and renewable resource. Traditional scheduling techniques have not fully handled this issue. This paper proposes a workspace management framework using a game engine to address that. The simulator detects spatial interferences by combining geometric computations and physics simulations. The detected conflicts are filtered through Bayesian inference to detect non-critical scenarios and avoid overestimation. The proposed spatial conflict simulator was tested using a real use case and compared to commercial tools. Results showed that the Navisworks approach detected 58 spatial conflicts (of which only 25% were relevant), the Synchro approach detected 1 spatial conflict, and the proposed approach detected 1 “direct” and 4 “indirect” spatial conflicts. Results show its capability to detect more relevant spatial issues than state-of-the-art tools and avoid overestimations. Construction management teams can adjust or confirm the schedule with that information.
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Uncertainties are predominant in hydropower development that lead to risks of time and cost overruns. The overruns’ assessment requires quantitative data that is difficult to obtain and sometimes partially or completely unavailable. Accordingly, this study proposes an expert fuzzy-based system for assessing the impact of uncertainties on time and cost overrun. A Bayesian network analysis is used wherein the experts’ opinions are incorporated through an analytic hierarchy process for the uncertainty’s likelihood and impact. A fuzzy inference system is used to incorporate the decision maker’s optimistic and pessimistic approaches. Such a methodology has not been applied by past studies for hydropower projects, which is the study’s main contribution. The methodology can be easily applied by construction project developers globally in different fields by varying the uncertainties, their likelihood, and impact scales as per different risk perceptions and regional conditions.
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The risks found in project management have a spatio-temporal effect and can interact with each other. Such interactions can be presented as a risk interaction network (RIN), with the nodes as the risks and the edges as the interaction. This article devises a twofold process to assess the RIN, namely, 1) a three-step procedure for the division, weight elicitation, and aggregation to assess the edges, and 2) a two-step procedure comprising the node classification and weight elicitation to qualify the nodes. The RIN evaluation takes the assessed RIN as the input, including an index accommodating the project manager's level of risk-aversion and a simulation-based network model for computing the index. A graphical user interface is developed to highlight the data collection and processing. An international construction project is provided to validate the proposed approach of RIN assessment and evaluation. The results inform that the proposed index provides more accurate information for risk analysis.
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Risk information that is used during project risk identification and assessment should be communicated well to enable risk-informed decision making. This study aimed to use risk descriptors for risk contextualization and explore how visualization can improve the communication of project risk information. Risk descriptors (e.g., assumptions and controllability) were identified, and two workshops were held to verify the selected descriptors and explore the effectiveness of visualizations for risk communication. The first workshop was designed to assess the perceptions of different risk experts, and the second workshop was a case study application to evaluate the usability of risk visualization. Qualitative analysis of the first workshop revealed four themes, specifically standardization, representation, customization, and practicality, to be considered during risk visualization. The second workshop confirmed the value-added through the use of visualizations and the usefulness of risk descriptors. Although this study did not focus on the best way of delivering the most useful data, it contributes to the existing body of knowledge by characterizing risk descriptors and introducing new insights regarding the use of visualization for communicating and describing risks in projects.
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Background: Construction development in Indonesia is growing rapidly, especially in Central Aceh District. Construction projects have distinctive characteristics and are very complex, so that risk events can have a serious impact on the viability of the project. A lack of attention to the risks faced will affect project implementation by creating delays, resulting in losses. The purpose of this study was to (1) identify the risk factors that cause delays in construction projects and (2) determine those particular risk factors that have a greater influence on construction projects. The location of this research was Central Aceh District, Indonesia. Methods: The data in this study were primary data in the form of a questionnaire and secondary data obtained from the literature related to this particular type of research. Questionnaires were distributed to respondents, namely contractor companies located in the Central Aceh District. The questionnaires were distributed to determine respondents' opinions about the level of influence of risk factors causing project delays. We used a validity test, reliability test, and descriptive analysis for data processing. Results: Based on the results of the study from 47 respondents, the “very high influence” category (Mode=5) for the tool malfunction factor was chosen by 21 respondents (44.68%), cost estimation inaccuracy by 20 respondents (42.55%), increased work costs by 22 respondents (46.81%), implementation of new technologies by 25 respondents (53.19%), details, accuracy and conformity to specifications that are not appropriate by 20 respondents (42.55%), worker quarrels by 20 respondents (42.55%), poor project planning and management by 22 respondents (46.81%), poor condition at locations and accessibility difficulty by 20 respondents (42.55%). Conclusions: Of the 80 risk factors that caused project delays, eight risk factors were found to have a very high influence on the implementation of construction projects in Central Aceh District.
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The aim of this paper is to examine, categorize and prioritize the critical risk factors that influence manufacturing-oriented projects. Utilizing data obtained from the metallic production industry in United Arab Emirates, we employ multi-criteria decision analysis encompassing the ‘Best-Worst Method’ (BWM) for factor ranking and categorization. The outcome of this exercise being the development of substantial proficiency in risk management that will have a significant impact on the overall success of projects commissioned within the manufacturing space. Findings drawn against an integrated ‘Technology–Organization–Environment’ and ‘Four levels of uncertainty’ framework suggests that ‘Automation’, ‘Cycle time’, and ‘Feed rate’ (technological factors), ‘Manpower utilization’ and ‘Agility’ (organizational factors), and ‘Occupational health and safety’ (environmental factors), ranked highest in terms of critical risk factors likely to impact upon the outcome of manufacturing projects.
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The failure of safety critical petroleum assets (SCPA) is often accompanied by devastating safety consequences. The conceptualization, design, and construction of SCPA need to integrate factors that will maintain the asset's lifecycle integrity. In this paper, a risk-based assessment of a case petroleum pipeline asset in Nigeria was used to examine the project conceptualization phase of an asset. The paper adopts a case study method, semistructured interviews, and field observations, and drew on pipeline failure data. Key managerial issues that need to be considered in project conceptualization for SCPA were identified. These issues include consideration for risk receptors and the need to assess organizational capabilities with respect to owning, operating, and regulating SCPA. This paper contributes theoretically by providing a performance-based learning framework for the conceptualization of new SCPA.
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Extant literature has called for researchers to be more pluralistic in their approaches to researching projects. Responding to this call, this paper offers an exposition of a causal mapping technique. In the project management literature, there already exists a small number of articles reporting effective use of causal mapping. However, these are not dedicated to detailed explanation of the technique itself and so lack consideration of its features beyond those relevant to a particular application. Consequently, an exposition of the technique is needed to enable comprehensive understanding of causal mapping to be gained and its suitability for research designs assessed. Specifically, this paper examines causal mapping's theoretical grounding, explores its strengths and weakness, presents example applications, compares alternative causal mapping approaches, and overall, explains how causal mapping can support a systemic perspective on projects. These issues will be of interest to researchers who wish to incorporate causal mapping into their project management research designs.
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Megaprojects are complex and expensive projects that often involve social, technical, economic, environmental and political (STEEP) challenges to project management. Despite these challenges, project owners and financiers continue to invest large sums of money in megaprojects that run high risks of being over schedule and over budget. While some degree of cost and schedule risks are considered during project planning, the challenge of modelling risks interactions and impacts on project performance still remains. To tackle this technical problem, this research adopted the Analytical Network Process and combined it with a new Risk Priority Index as an innovative approach to model risks analytically based on data collected from the Edinburgh Tram Network project at the construction phase. The approach provides an interactive way for developers to prioritise risks across the project supply network and to initiate timely mitigation strategies against significant cost and time consequences of STEEP risks on megaproject performance. © 2015 Elsevier Ltd and Association for Project Management and the International Project Management Association.
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Risk matrices are widely used in risk management. They are a regular feature in various risk management standards and guidelines and are also used as formal corporate risk acceptance criteria. It is only recently, however, that scientific publications have appeared that discuss the weaknesses of the risk matrix. The objective of this paper is to explore these weaknesses, and provide recommendations for the use and design of risk matrices. The paper reviews the few relevant publications and adds some observations of its own in order to emphasize existing recommendations and add some suggestions. The recommendations cover a range of issues, among them: the relation between coloring the risk matrix and the definition of risk and major hazard aversion; the qualitative, subjective assessment of likelihood and consequence; the scaling of the discrete likelihood and consequence categories; and the use of corporate risk matrix standards. Finally, it proposes a probability consequence diagram with continuous scales; providing, in some instances, an alternative to the risk matrix.
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An accident occurring at sea, though a rare event, has a huge impact both on the economy and the environment. A better and safer shipping practice always demands new ways to improve marine traffic and this essentially requires learning from past experience/faults. In this regard, probabilistic analysis of accidents and associated consequences can play a very important role in making a better and safer maritime transport system. Bayesian networks represent a class of probabilistic models based on statistics, decision theory and graph theory. This paper introduces the use of data-driven Bayesian modelling in risk analysis and makes a comparison with the different data-driven Bayesian methods available. The data for this study are based on the Lloyds database of accidents from 1997 to 2009. Important influential variables from this database are grouped and a Bayesian network that shows the relationship between the corresponding variables is constructed which in turn provides an insight into probabilistic dependencies existing among the variables in the database and the underlying reasons for these accidents.
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Risk assessment based on probability-impact (P-I) ratings is the most widely used approach in project-based industries such as the construction industry. However, there are various criticisms about utilization of the P-I rating approach because there are factors and assumptions that are hidden within the risk ratings that cannot be conveyed to decision makers for a reliable risk assessment. This study aimed to explore biases, particularly how the risk attitude and assumptions on controllability of risk (illusion of control bias) may affect subjective risk ratings assigned by the experts during risk assessment of international construction projects. Results showed that as the level of perceived controllability rose, risk ratings assigned by the experts tended to be lower. There was a moderate correlation between risk attitude and risk ratings. Risk attitude and assumptions on controllability were also moderately related, and their combined effect on risk ratings varied according to different risk scenarios. Risk ratings were affected by the risk attitudes of experts, especially when the country risk level was high, whereas assumptions on controllability tended to affect risk ratings more significantly when the country risk level was low. Although the questionnaire findings about the impact of biases on risk ratings are valid only within the context of this study, findings may have some generic implications for developing new methods that can highlight and control the hidden factors in subjective risk ratings. Assumption-based thinking and knowledge elicitation on risk ratings, together with underlying assumptions by group decision making, may decrease the impact of illusion of control bias during the risk assessment process.
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captures interdependencies between risks, multiple (potentially conflicting) performance measures and risk mitigation strategies within a (risk) network setting. The process helps in prioritising risks and strategies specific to the decision maker's risk appetite. The process is demonstrated through a case study conducted in a global manufacturing supply chain involving semi-structured interviews and focus group sessions with experts in risk management. Theoretically grounded in the framework of Bayesian Belief Networks (BBNs) and Expected Utility Theory (EUT), the modelling approach has a number of distinctive characteristics. It utilises a top-down approach of Fault Tree Analysis (FTA). Performance measures are identified first and subsequently connected to risks. A ‘probability-conditional expected utility’ matrix is introduced to reflect the propagation impact of interdependent risks on all performance measures identified. A ‘weighted net evaluation of risk mitigation’ method is proposed and the method of ‘swing weights’ is used to capture the trade-off between the efficacy of strategies and the associated cost keeping in view the decision maker's risk appetite. The approach adapts and integrates techniques from safety and reliability engineering (FTA), decision making under uncertainty (EUT), and multi-criteria decision analysis (swing weights). The merits and challenges associated with the implementation of interdependency based frameworks are discussed. Propositions are presented to elucidate the significance of modelling interdependency between risks and strategies.
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In this paper, we introduce an integrated supply chain risk management process that is grounded in the theoretical framework of Bayesian Belief Networks capturing interdependency between risks and risk mitigation strategies, and integrating all stages of the risk management process. The proposed process is unique in four different ways: instead of mapping the supply network, it makes use of Failure Modes and Effects Analysis to model the risk network which is feasible for modelling global supply chains; it is driven by new dependency based risk measures that can effectively capture the network wide impact of risks for prioritisation; it utilises the concept of Shapley value from the field of cooperative game theory to determine a fair allocation of resources to the critical risks identified; and the process helps in prioritising potential risk mitigation strategies (both preventive and reactive) subject to budget and resource constraints. We demonstrate its application through a simulation study.
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Project complexity has been extensively explored in the literature because of its contribution towards the failure of major projects in terms of cost and time overruns. Focusing on the interface of Project Complexity and Interdependency Modelling of Project Risks, we propose a new process that aids capturing interdependency between project complexity, complexity induced risks and project objectives. The proposed modelling approach is grounded in the theoretical framework of Expected Utility Theory and Bayesian Belief Networks. We consider the decision problem of identifying critical risks and selecting optimal risk mitigation strategies at the commencement stage of a project, taking into account the utility function of the decision maker with regard to the importance of project objectives and holistic interaction between project complexity and risk. The proposed process is supported by empirical research that was conducted in the construction industry and its application is illustrated through a simulation study.
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In risk response analysis, risks are often assumed independently. In fact, however, risks in a project mutually affect and the independent risk seldom exists in reality. This paper provides an approach to quantitatively measure the risk interdependence. Based on the analysis of the risk interdependence, we construct an optimization model for selecting risk response strategies considering the expected risk loss, risk interdependence and its two directions. Further, the effects of the risk interdependence on risk response can be investigated. There are two major findings by the analysis of the case project. First, the expected utility would be more sensitive to the risk interdependence itself than to the directions of it. Second, the insufficient attention paid to or neglect of the risk interdependence would lower the expected utility and increase the implementation cost. © 2016 Elsevier Ltd and Association for Project Management and the International Project Management Association.
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Although complexity, uncertainty, risk, and resilience are concepts of growing interest, there is a lack of structured synthesis of these concepts and their relationships in supply chain management (SCM) and project management (PM) literatures. This paper addresses this gap through novel tertiary and bibliometric analyses. The tertiary research embraces 22 literature reviews and guides the development of the synthesis framework. The bibliometric analysis includes 1,275 papers and complements the tertiary research with study descriptors, a co-citation, and a static and dynamic/longitudinal co-word network analysis. Authors cite each other within the confines of their research area with no cross-fertilization of studies in PM and SCM, despite several commonalities among the areas. Both areas use similar conceptual definitions and there are close resemblances in risk management in SCM and temporary multi-organization (TMOs) projects. Resilience appears as a new topic in SCM but is absent in TMO. A research agenda closes the paper. © 2015 Elsevier Ltd and Association for Project Management and the International Project Management Association.
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Risk management is about identifying risks, assessing their impacts, and developing mitigation strategies to ensure project success. The difference between the expected and actual project outcomes is usually attributed to risk events and how they are managed throughout the project. Although there are several reference frameworks that explain how risks can be managed in construction projects, a major bottleneck is the lack of a common vocabulary for risk-related concepts. Poor definition of risk and patterns of risk propagation in a project decrease the reliability of risk models that are constructed to simulate project outcomes under different risk occurrence scenarios. This study aims to extend previous studies in risk management by presenting an ontology for relating risk-related concepts to cost overrun. The major idea is that cost overrun depends on causal relations between various risk sources (namely, risk paths) and sources of vulnerability that interfere with these paths. Ontology is used to develop a database system that represents risk event histories of international construction projects and to construct a model for estimation of cost overrun. It will form the basis of a multiagent system that can be used to simulate the negotiation process among project participants about sharing of costs considering the risk allocation clauses in the contract, sources of vulnerability, and causal relations between risk events and their impacts. The ontology is constructed by interaction with Turkish contractors working in international markets and extensive literature review on risk-related concepts. The validation test results provide evidence that the ontology is fairly effective to help Turkish contractors to assess cost overrun by considering sources of vulnerability and risk in international construction projects. DOI:10.1061/(ASCE)CP.1943-5487.0000090. (C) 2011 American Society of Civil Engineers.
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The major aim of this research is to demonstrate that causal relationships exist among various risk factors that necessitate identification of risk paths, rather than individual risk factors, during risk assessment of construction projects. International construction projects have more complex risk-emergence patterns because they are affected by global and foreign country conditions and project-related factors. Identification of a network of interactive risk paths, each of which initiated from diverse vulnerabilities of the project system, is considered to be a better reflection of the real conditions of construction projects than the use of generic risk checklists. In this study, using the data from 166 projects carried out by Turkish contractors in international markets and utilizing structural equation modeling (SEM) techniques, 36 interrelated risk paths were identified and the total effects of each vulnerability factor and risk path on cost overrun were assessed. SEM findings demonstrate that every risk path is initiated from a specific vulnerability factor related to project environment, and that contractor-specific vulnerabilities have the most effect on project cost overrun. Risk identification using SEM helps decision makers in answering what-if questions in the early stages of a project, tracing the effects of interdependent risks throughout the life cycle of the project and evaluating the influence of alternative mitigation strategies, not only on specific risks but also on the whole network of interrelated risk factors.
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The complexity of public private partnership (PPP) projects ensures that risks can arise and spread in unpredictable and sometimes catastrophic ways. Systems thinking is often proposed as a potential solution to this problem but has not been widely adopted in practice. To explore the reasons for this, interviews were conducted with sixteen senior construction professionals with experience of PPPs. The results show that the main barriers to the adoption of systems thinking are: conflicts of interest within PPP projects; confrontational contracts; resistance to change; lack of time and resources; perceptions of complexity; unknown legal implications of sharing risk; and external validation of existing risk management practices. It is concluded that in moving to a systems thinking approach, deeply imbedded ontologies, path dependencies, confrontational practices, and traditional linear and reductionist risk management practices will need to be challenged. Five key questions are also proposed for future research in this area.
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As evidenced through both a historical and contemporary number of reported over-runs, managing projects can be a risky business. Managers are faced with the need to effectively work with a multitude of parties and deal with a wealth of interlocking uncertainties. This paper describes a modelling process developed to assist managers facing such situations. The process helps managers to develop a comprehensive appreciation of risks and gain an understanding of the impact of the interactions between these risks through explicitly engaging a wide stakeholder base using a group support system and causal mapping process. Using a real case the paper describes the modelling process and outcomes along with its implications, before reflecting on the insights, limitations and future research.
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Effective risk assessment and management is critical for success in international construction projects. This paper proposes a knowledge-based risk mapping tool for systematically assessing risk-related variables that may lead to cost overrun in international markets. The tool uses an ontology that relates risk and vulnerability to cost overrun [1] and a novel risk-vulnerability assessment methodology [2] to estimate potential risk paths that may emerge in international construction projects. The tool has been developed in collaboration with an industrial partner, a construction management company that gives risk management consultancy services to Turkish contractors working in international markets. The tool has been designed by using the previous projects of the partner firm as test cases and preferences of company professionals are taken into account while determining the functions of the tool. As the reliability and usability of the tool significantly depend on the subjective evaluations of users about level of vulnerability and magnitude of potential risk events, a lessons learned database has been incorporated into the tool so that decision-makers may refer to risk event histories of previous projects to make estimations about forthcoming projects. In order to evaluate the usability of the tool, a usability test has been conducted by eight construction experts. Usability test results demonstrated that the tool can be utilized for prediction of probable risk paths and their impact on cost. Results of a case study, a building project in Serbia, have also been reported in this paper to demonstrate the functions and the performance of the tool. Although the usability and reliability test results are satisfactory, the tool should be seen as an initial platform which should be improved by increasing its “intelligence” by high quality and enough number of risk event histories, and also customized according to user preferences as well as company policies.
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This paper reviews the literature of construction risk modelling and assessment. It also reviews the real practice of risk assessment. The review resulted in significant results, summarised as follows. There has been a major shift in risk perception from an estimation variance into a project attribute. Although the Probability–Impact risk model is prevailing, substantial efforts are being put to improving it reflecting the increasing complexity of construction projects. The literature lacks a comprehensive assessment approach capable of capturing risk impact on different project objectives. Obtaining a realistic project risk level demands an effective mechanism for aggregating individual risk assessments. The various assessment tools suffer from low take-up; professionals typically rely on their experience. It is concluded that a simple analytical tool that uses risk cost as a common scale and utilises professional experience could be a viable option to facilitate closing the gap between theory and practice of risk assessment.
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The popularity of Bayesian Network modelling of complex domains using expert elicitation has raised questions of how one might validate such a model given that no objective dataset exists for the model. Past attempts at delineating a set of tests for establishing confidence in an entirely expert-elicited model have focused on single types of validity stemming from individual sources of uncertainty within the model. This paper seeks to extend the frameworks proposed by earlier researchers by drawing upon other disciplines where measuring latent variables is also an issue. We demonstrate that even in cases where no data exist at all there is a broad range of validity tests that can be used to establish confidence in the validity of a Bayesian Belief Network.
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Bayesian belief nets (BBNs) provide an effective way of reasoning under uncertainty. They have a firm mathematical background in probability theory and have been used in a variety of application areas, including reliability. BBNs can provide alternative representations of fault trees and reliability block diagrams. BBNs can be used to incorporate expert judgement formally into the modelling process. It has been claimed BBNs may overcome some of the limitations of standard reliability techniques. This paper presents an overview of BBNs and illustrates their use through a simple tutorial on system reliability modelling. The use of BBNs in reliability to date is reviewed. The challenge of using BBNs in reliability practice is explored and areas of research are identified. Copyright © 2001 John Wiley & Sons, Ltd.
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Delays on construction projects cause financial losses for project stakeholders in developing countries. This paper describes how Bayesian belief network (BBN) is applied to quantify the probability of construction project delays in a developing country. Sixteen factors were identified through a questionnaire survey of 166 professionals. Eighteen cause-effect relationships among these factors were obtained through expert interview survey to develop a belief network model. The validity of the proposed model is tested using two realistic case studies. The findings of the study revealed that financial difficulties of owners and contractors, contractor’s inadequate experience, and shortage of materials are the main causes of delay on construction projects in Vietnam. The results encourage practitioners to benefit from the BBNs. This approach is general and, as such, it may be applied to other construction projects with minor modifications.
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Risk management (RM) comprises of risk identification, risk analysis, response planning, monitoring and action planning tasks that are carried out throughout the life cycle of a project in order to ensure that project objectives are met. Although the methodological aspects of RM are well-defined, the philosophical background is rather vague. In this paper, a learning-based approach is proposed. In order to implement this approach in practice, a tool has been developed to facilitate construction of a lessons learned database that contains risk-related information and risk assessment throughout the life cycle of a project. The tool is tested on a real construction project. The case study findings demonstrate that it can be used for storing as well as updating risk-related information and finally, carrying out a post-project appraisal. The major weaknesses of the tool are identified as, subjectivity of the risk rating process and unwillingness of people to enter information about reasons of failure.
Article
The aim of this paper is to understand the key risks in construction projects in China and to develop strategies to manage them. Risks were prioritized according to their significance of influences on typical project objectives in terms of cost, time, quality, safety and environmental sustainability, and then scrutinized from a joint perspective of project stakeholders and life cycle. Postal questionnaire surveys were used to collect data, based on which a total of 25 key risks were ascertained. These risks were compared with the findings of a parallel survey in the Australian construction industry context to highlight the unique risks associated with construction projects in China. Strategies to manage the risks were sought from the perspectives of project stakeholders and life cycle and in light of the Chinese construction culture. It is concluded that clients, designers and government bodies should take the responsibility to manage their relevant risks and work cooperatively from the feasibility phase onwards to address potential risks in time; contractors and subcontractors with robust construction and management knowledge should be employed to minimize construction risks and carry out safe, efficient and quality construction activities.
Article
This paper uses a Bayesian Belief Networks (BBN) methodology to model the reliability of Search And Rescue (SAR) operations within UK Coastguard (Maritime Rescue) coordination centres. This is an extension of earlier work, which investigated the rationale of the government's decision to close a number of coordination centres. The previous study made use of secondary data sources and employed a binary logistic regression methodology to support the analysis. This study focused on the collection of primary data through a structured elicitation process, which resulted in the construction of a BBN. The main findings of the study are that statistical analysis of secondary data can be used to complement BBNs. The former provided a more objective assessment of associations between variables, but was restricted in the level of detail that could be explicitly expressed within the model due to a lack of available data. The latter method provided a much more detailed model, but the validity of the numeric assessments was more questionable. Each method can be used to inform and defend the development of the other. The paper describes in detail the elicitation process employed to construct the BBN and reflects on the potential for bias.
Article
Risk matrices-tables mapping "frequency" and "severity" ratings to corresponding risk priority levels-are popular in applications as diverse as terrorism risk analysis, highway construction project management, office building risk analysis, climate change risk management, and enterprise risk management (ERM). National and international standards (e.g., Military Standard 882C and AS/NZS 4360:1999) have stimulated adoption of risk matrices by many organizations and risk consultants. However, little research rigorously validates their performance in actually improving risk management decisions. This article examines some mathematical properties of risk matrices and shows that they have the following limitations. (a) Poor Resolution. Typical risk matrices can correctly and unambiguously compare only a small fraction (e.g., less than 10%) of randomly selected pairs of hazards. They can assign identical ratings to quantitatively very different risks ("range compression"). (b) Errors. Risk matrices can mistakenly assign higher qualitative ratings to quantitatively smaller risks. For risks with negatively correlated frequencies and severities, they can be "worse than useless," leading to worse-than-random decisions. (c) Suboptimal Resource Allocation. Effective allocation of resources to risk-reducing countermeasures cannot be based on the categories provided by risk matrices. (d) Ambiguous Inputs and Outputs. Categorizations of severity cannot be made objectively for uncertain consequences. Inputs to risk matrices (e.g., frequency and severity categorizations) and resulting outputs (i.e., risk ratings) require subjective interpretation, and different users may obtain opposite ratings of the same quantitative risks. These limitations suggest that risk matrices should be used with caution, and only with careful explanations of embedded judgments.