Project Risk Assessment Research Articles

Risk assessment for onshore wind projects in Canada

The construction phase of onshore wind farm projects involves different types of risks that hinder their development. This study aims to identify and assess risk factors that affect the construction of onshore wind farm projects and rank them based on their severity as assessed from the Canadian perspective. Thirty risk factors were collected through an extensive literature review. Then, experts were asked to subjectively evaluate the probability and impact of each factor on project cost, time, quality, and safety. The fuzzy technique for order of preference by similarity to ideal solution (fuzzy TOPSIS) and the fuzzy analytic hierarchy process (fuzzy AHP) were used to rank these risk factors based on their severity. The closeness coefficient (CC) indicated that a lack of management expertise, a shortage of resources required for project delivery, adverse weather, material damage during construction, and failure to keep up with recent innovative technology are the most severe risks affecting onshore wind farm projects in Canada with CC values of 0.9710, 0.9642, 0.9635, 0.9575, and 0.9450, respectively. The findings of this study support the decision making of investors and contractors who work in the Canadian wind energy industry by highlighting the critical risk factors. Moreover, this study is beneficial for decision makers in other jurisdictions since a review and a list of risk factors that affect the construction of onshore wind projects are provided herein.

Editorial: Risk assessment and management of water conservancy projects

Editorial: Risk assessment and management of water conservancy projects

Analysis of Human Factors that Influence Contractors' Risk Management Attitudes in Sumatra Island in Construction Projects

Project risk assessment serves as an effective tool for overseeing cost, and time, and achieving technical performance in construction projects. The construction industry frequently contends with various uncertainties, which can lead to potential risks such as delays, cost overruns, and compromised quality in building and road construction ventures. Consequently, adept project risk management becomes crucial in the decision-making process of construction companies, ultimately determining project success or failure. This paper aims to assess the prioritize the specific factors influencing contractors' risk attitudes in these companies. The study employed simple random sampling under probability techniques. Data collection involved a thorough review of pertinent literature and the use of a questionnaire employing a five-point Likert scale ranging from (1) very low/very disagree to (5) very high/very agree. The sample size used for analysis was 47 companies, with a particular focus on the Sumatra construction companies. This study was analyzed using Statistical Package for Social Science (SPSS) version 23.0 for Microsoft Windows and a reliability test with Cronbach's alpha. The finding of this study is primary factors influencing contractors' risk attitudes are as follows: educational background, physical health, emotional intelligence, work experience, and professional competence, The results of this research can provide decision-makers with valuable insights into strategies for effectively managing contractors' risk attitudes, thus furnishing stakeholders with the requisite knowledge to enhance risk management performance within the Sumatera construction companies.

Risk Assessment and Management in Water Resources Projects: Insights and Future Directions

Risk Assessment and Management in Water Resources Projects: Insights and Future Directions

Multi-status Bayesian network for analyzing collapse risk of excavation construction

In response to the need for effective collapse risk analysis in engineering projects, a decision support approach is presented. It is rooted in multi-status Bayesian network (MSBN) and fuzzy set theory, encompassing MSBN construction, risk analysis, and management. This research addresses the causative correlation between influential factors and excavation collapse, considering the inherent uncertainty and fuzziness of the project. To enhance the reliability of evaluation results, an expert confidence index, integrating judgment ability, subjective reliability, and risk preference, is introduced. The approach is applied to an excavation case study, demonstrating its viability and capacity. Furthermore, it offers actionable insights for decision-makers to proactively mitigate accident probabilities. Notably, sensitivity analysis identifies critical risk factors. This research contributes to the field of engineering project risk assessment and management. It serves as a foundation for future research and development, guiding the way for improved strategies and decision support systems.

Review on the Identification and Assessment of Project Risks in International Engineering Contracting Projects

With the steady promotion of the "the Belt and Road" initiative and the introduction of a number of reform measures, the pace of "going global" of Chinese enterprises has been accelerating. More Chinese construction enterprises are actively engaged in overseas engineering construction and have made remarkable achievements. At the same time, the way of project contracting is also constantly upgrading, and the time span, uncertainty and complexity of international project contracting business are increasing, so it is necessary to carry out risk management. In addition, the introduction of relevant policies and regulations at home and abroad has further promoted the construction of overseas risk management of international project contracting enterprises. Therefore, it is necessary to use scientific methods and tools to identify and assess the risks of the project according to the internal and external conditions of the international engineering contracting project, and determine the measures to deal with the risks of the international engineering contracting project according to the results of the risk analysis, its own resource conditions, the environment and other factors. Finally, the corresponding risk response measures and suggestions are put forward based on the actual development strategy of the enterprise and the market environment of international project contracting business.

Natural Hazard Vulnerability and Risk Assessment: The Case of the China-Pakistan Economic Corridor

The coverage area of the China-Pakistan Economic Corridor (CPEC) in Pakistan, including its critical functional zones within which major projects are located, is prone to different types of natural hazards and disasters. Since 2015, efforts have been made to operationalize CPEC without institutionalizing and establishing a comprehensive natural hazard risk assessment mechanism within the China-Pakistan collaborative framework. It goes against the grain of robust, resilient, and sustainable long-term development of CPEC. This study critically argues for the need to set up a multi-hazard risk assessment and disaster response mechanism as part of the institutional framework of CPEC, responsible for identifying, preparing, and planning against primary, secondary, and tertiary risks in accordance with the varied hazard vulnerability prevalence in varying geospatial and functional zones within the coverage area of CPEC. Overall, the study identifies and discusses the risk-prone nature of the current development of CPEC and proposes a real-time, technologically enabled, natural hazard risk assessment of CPEC projects spread over varying geospatial and functional zones in an institutional framework characterized by the uninterrupted collaboration and concentration of experts and resources from China and Pakistan.

Role of Project Risk Assessment on Performance of Credit Products at Bank of Africa, Rwanda

The study examined the role of project risk assessment on performance of credit products at the Bank of Africa in Rwanda. The study was guided by the following specific objectives: To determine the contribution of technical feasibility on performance of credit product at Bank of Africa, to establish the contribution of project financial viability on performance of credit product at Bank of Africa and to evaluate the contribution of client’s credit rating on performance of credit product at Bank of Africa. This study applied descriptive design to get results related to the study, the target population as well sample size was 72 respondents. Researcher utilized census as sampling technique. The source of data was primary and secondary methods. Questionnaires were adopted to collect primary data and documentary review applied for collecting secondary data. The data was obtained using descriptive statistical analysis with use of frequency, percentage, mean and standard deviation, and inferential statistics by the use of Pearson correlation (r) and multiple linear regression analysis through Social Packages and Statistical Science version 23. The presentation of results was presented using tables, the pilot test was determined to ensure the validity and reliability of instrument used in data collection. The results were presented using Pearson Coefficient Correlation to indicate the relationship between variables, the findings showed that technical feasibility correlated with credit product at Bank of Africa at r=0.76; Furthermore, project financial viability influences performance of credits products at correlation coefficient of .738 while the findings showed that there is positive correlation between client’s credit rating and performance of credit product at Bank of Africa at 72.1%. The study concludes that project risk assessment contributes to the performance of credit products through technical feasibility, project financial viability and client’s credit rating. The study recommends that project implementors need to improve the client credit rating to enhance performance of credit products. Keywords: Project Risk Assessment, Performance, Credit Products, Rwanda

Quantitative tools in microbial and chemical risk assessment.

The popularity of biological origin food protection substances is driven by demands from consumers for natural and clean label product, increasing various food-related safety and health concerns and sustainability issues. Lactic acid bacteria (LAB) are most promising because they are a large group of beneficial microorganisms commonly used in food protection due to their ability to inhibit the growth of pathogenic bacteria and enhance food safety. Extensive scientific research has been conducted to understand the mechanisms by which LAB exert their protective effects in various food systems. Even though LAB activity against various food pathogens and spoilers is distinguished, use of cell-free supernatant (CFS) is still under investigation. This report is dedicated to present how qualitative measures can elaborate in new bacteria-origin food additive investigation. As part of the EU-FORA programme, the fellow was involved in the risk assessment tasks and projects which include gaining basic knowledge in predicative microbiology fundamentals, including different types of modelling strategies; delivering essential understanding about experimental design, knowledge in three specific software tools (MATLAB, GInaFiT and DMFit) and gained overall understanding what are the main differences while modelling growth or inactivation models. Secondary activities were included as a way to expand competences beyond qualitative measures to overall all activities done regarding risk assessment and build a strong network of food safety experts and professionals to continue engaging in risk assessment beyond fellowship programme.

A novel failure mode and effect analysis model using personalized linguistic evaluations and the rule-based Bayesian network

Failure mode and effect analysis (FMEA) is a useful reliability-management instrument and has provided an effective risk assessment tool for engineering applications. With the advancement of technology, the engineering field has encountered an increasing number of failure modes. However, there is a lack of literature focused on evaluating large-scale failure modes. Additionally, the conversion of expert evaluations into numerical forms is primarily conducted by researchers, resulting in potential misunderstandings and information loss. Moreover, most FMEA methods rely only on the risk rankings, lacking further references to provide management recommendations. To address these research gaps, we propose a novel FMEA model. First, we employ a staged evaluation approach to accurately assess a large number of failure modes, which minimizes decision errors caused by evaluator fatigue. Second, to reduce the misinterpretation of expert evaluations and information loss, we improve the personalized individual semantics method to increase expert involvement, and introduce the inverse distance weighting method to minimize information loss. Third, we utilize the rule-based Bayesian network method to provide additional risk indicators. In addition to risk rankings, the output risk distribution offers valuable insights for engineering management. Finally, we apply the proposed model to a risk assessment project, providing suggestions on the safe operation of the floating production storage and offloading facility and vessels. Sensitivity and comparison analyses are conducted to demonstrate the validity and advantages of the proposed model.

Methodology for risk assessment of engineering procurement construction project based on the probabilistic hesitant fuzzy multiple attributes group decision making

With the continuous development of China’s economic system, the development of the construction industry is becoming more and more rapid, and the number and scale of construction projects are increasing. Due to the characteristics of large projects and long cycles, there are a large number of construction parties involved in construction projects. The increase in the number of participating partners makes it difficult for their projects to be integrated and managed by management departments such as owners, let alone for various parties to collaborate in the construction of projects. In order to effectively solve this problem, the engineering procurement construction (EPC) general contracting model has emerged. The risk assessment of EPC project is classical multiple attributes group decision making (MAGDM). The probabilistic hesitancy fuzzy sets (PHFSs) are used as a tool for characterizing uncertain information during the risk assessment of EPC project. In this paper, the classical grey relational analysis (GRA) method is extended to PHFSs. Firstly, the basic concept, comparative formula and Hamming distance of PHFSs are introduced. Then, the definition of the score values is employed to obtain the attribute weights based on the information entropy. Then, probabilistic hesitancy fuzzy GRA (PHF-GRA) method is built for MAGDM under PHFSs. Finally, a practical case study for risk assessment of EPC project is designed to validate the proposed method and some comparative studies are also designed to verify the applicability.

The Nexus Between Construction Contract Risk Assessment and Big Data in China

The construction market in China is currently showing an increasing trend in terms of project size, complexity, and technical difficulty. With large initial investments and long capital payback periods, construction projects are vulnerable to the external environment during development and risk control is particularly important. Contracts are at the heart of project management and every step of project development revolves around the content of the contract signed by the contractor. Signing contracts to protect the rights of both parties has become the most effective way of controlling project risk. And with big data technology being rapidly developed and widely used, and the amount of data created and accumulated by the internet growing exponentially, big data has the potential to be used for analysis, forecasting and decision making in the construction industry. The aim of this study is therefore to develop a risk identification list for construction project contracts in China, to score the risks within the list according to their incidence and importance, and to find practical applications of big data technology in contract risk assessment. To achieve this goal, this study used qualitative research and in-depth interviews with experts within the Chinese construction engineering industry. The results of this study form a list of risks in Chinese construction contracts, indicating that late payment and government intervention are the most important risks in Chinese construction contracts. This study also identifies TianYanCha, a commonly used big data enterprise risk identification platform in China, which provides big data support in three areas: evaluating owner integrity, enterprise risk identification, and owner relationship knowledge. At the practical level, this study helps contractors to assess project risks and improve contractual contents during the preparation phase of construction projects, thus improving the success rate of projects. At the theoretical level, this study fills the gap in contractual risk assessment for construction projects in China and provides insights for subsequent risk assessment of construction projects based on the contractor's perspective.

Estimation of Owned and Street Dog Population by Quesionnire Survey and Mark-Recapture Method in Three Urban Areas: Bishoftu, Dukem and Modjo Towns

The understanding of the structure of free-roaming and owning dog population’s estimation is of extreme importance for the planning and monitoring of dog population control strategies, animal welfare and health. The methods used to estimate the abundance of this group of dogs are more complex than the ones used with domiciled owned dogs. We point to enhancements necessary for the implementation of the studies and to potential updates and revisions to the recommendations of the World Health Organization with respect to the estimation of free-ranging and owning dog populations. Rabies is a fatal viral disease of animals and people. People usually infected via bites from an infected animal (e.g. dog). Post Exposure Prophylaxis (PEP) has to initiate immediately after bite wounds of suspected rabid animals in order to avoid fatalities. The situation of rabies poorly known in Ethiopia. Questionnaire survey was conducted in selected blocks in Dukem, Bishoftu and Modjo towns in order to capture information on dog population size estimates using mark-resight surveys has provided useful baseline data for understanding the population dynamics of dogs at the study sites. Mark-resight surveys provide useful information for designing and managing the logistics of dog vaccination or assessing vaccination coverage and for evaluating the impact of neutering programs on the size and structure of dogs’ populations over time. As part of a dog born zoonosis disease risk assessment project, surveys conducted to estimate the size of the dog population and to describe dog management practices of households belonging to different ethnic groups. A markedrecapture method employed and the number of unowned dogs estimated. A total of 599 dog owning households and 276 non-dog household interviewed from at selected 19 blocks at study sites. Owned dogs were more likely male, local breed and for the purpose of used to guard. These households kept their dogs either fully restricted, semi and free roaming but full restriction have reported only at the higher percentage of the total dog households. By counted method, study the ownerless dogs higher than owning and semi-restricted dogs.

Artificial Intelligence in Project Management & Its Future

Artificial Intelligence (AI) has emerged as a disruptive force, transforming industries and revolutionizing business processes. Among the domains significantly impacted, project management holds immense potential for transformation. AI’s integration promises intelligent automation, data-driven decision-making, and predictive capabilities, addressing challenges in traditional project management methodologies. Successful AI implementations have revolutionized project management, improving forecasting, resource allocation, and risk assessment. Despite the benefits, challenges hinder AI adoption, including data quality, investment uncertainties, workforce readiness, and change management. However, AI’s potential to address gaps in project management is substantial, enabling data-driven decisions, real-time monitoring, resource optimization, and enhanced agility. This research examines AI’s evolution, successful implementations, challenges, and potential implications, providing insights for effective AI integration in project management. Key Takeaways: • Project management is one domain that stands out as highly impacted by artificial intelligence, offering immense potential for transformation through AI integration. • AI-driven solutions in project management promise intelligent automation, data-driven decision-making, and enhanced predictive capabilities, addressing traditional methodological limitations. • Despite challenges, AI holds substantial potential to address existing gaps in project management practices.

Risk Interdependency Network Model for the Cost and Time of Pile Installation in Saudi Arabia, Using Partial Least Squares Structural Equation Modeling

Pile construction projects cause significant time and expense overruns. The pile installation activity is the primary reason for project underperformance and uncertainties. Additionally, the risks associated with pile installation are mostly considered independent in the overall risk management process, leading to inadequate risk assessment and response. However, few studies have evaluated the risks associated with pile installation. Thus, this study aims to establish the risks of the time and cost of pile installation, using an interdependency network model with a particular emphasis on sand and rocky terrain conditions. In addition, this study introduces a new method for establishing a model that considers the interrelationships among risks via a partial least squares structural equation model (PLS-SEM). The research methodology involves assessing the probability and impact of 53 risk factors of pile installation time and cost. Twelve pile construction experts participated in this assessment. Then, a Monte Carlo Simulation was utilized before the data were integrated into the PLS-SEM. The research findings reveal that the site and economic risks indirectly affect the cost of installing pile in sand through construction risks. Also, the risk group comprising site and equipment risks indirectly affects the cost of installing pile in rock through design risks. This study’s findings will help construction organizations to improve time and cost risk assessments for pile installation projects.

Life Cycle Risk Assessment of Public-Private Partnership Project of Comprehensive Environmental Management Based on Principal Component Analysis

Life Cycle Risk Assessment of Public-Private Partnership Project of Comprehensive Environmental Management Based on Principal Component Analysis

Fine-Kinney method based on fuzzy logic for natural gas pipeline project risk assessment

Fine-Kinney method based on fuzzy logic for natural gas pipeline project risk assessment

Risk Assessment of the main transport corridor in Iraq

The increased rate of road fatalities ad injured has been a vital issue. Risk assessment of road project is essential in the planning and design stages to prevent road crashes occurring in the operational stage. In Iraq, the main transport corridor has been designed to connect major cities and used by significant traffic volume which led to increase in the crash fatalities. Therefore, it is important to identify the risk factors and improve the safety level. This paper aims to assess the risk of part of the main transport corridor in Iraq. The selected section is part of Road 9 which connects Abu Gharib district with Fallujah City. The selected methodology of assessment is based on the road features and supported traffic data developed by the International Road Assessment Program (iRAP) agency. The output of the assessment is in terms of star rating and star rating score. Higher star rating means safer road condition while higher star rating score means more dangerous road condition. The results of assessment show high level of risk for vehicles. A set of road improvements that meet safety standards are proposed such as improving delineation, lighting, Metal barriers for the median, central strips, intersection channelization, Various repairs according to the severity of the damage at pavement and using shoulder rumble strips. The results of assessment after improvements show upgrading the star rating to the recommended minimum (3 and 4 stars) for vehicles.

Application of AI Technology in Program Management

Effective program management is crucial in ensuring the successful execution of complex projects and initiatives in today's continuously changing corporate environment. Using Artificial Intelligence (AI) technology into program management procedures provides a viable path for improving decision-making, resource allocation, risk assessment, and overall project results. This article explores the use of artificial intelligence (AI) technology to program management, outlining its possible advantages, problems, and execution techniques. This study intends to give insights into the transformational effect of AI in improving Program management techniques by evaluating real-world examples and case studies.

Security risk assessment of projects in high-risk areas based on attack-defense game model

Assessing the security risk of projects in high-risk areas is particularly important. This paper develops a security risk assessment model for projects in high-risk areas based on the target loss probability model and Bayesian game model. This model is modeled from the perspective of attack-defense confrontation and addresses the issue that traditional risk assessment focuses on the analysis of the attacker yet neglects to analyze the defender—the defender’s optimum defensive information is not quantitatively determined. The risk level, optimum defensive resource value, and optimum defensive strategy of the project are determined through the analysis of a project in the high-risk area. This enables the project’s risk manager to adjust the defensive resources reasonably and optimally, confirming the objectivity and feasibility of the model and offering a new benchmark for security risk assessment, which has significant practical implications.