Project Risk Assessment Research Articles

Financial Calculation Problems and Countermeasure Analysis of Large-Scale Engineering Construction Projects

The financial aspects of large-scale engineering construction projects profoundly influence their success. Strengthening cost control and establishing a scientific financial evaluation system can enhance the project’s economic benefits, minimize unnecessary costs, and provide decision-makers with a robust financial foundation. Additionally, implementing an effective cash flow control mechanism and conducting a comprehensive assessment of potential project risks can ensure financial stability and mitigate the risk of fund shortages. Developing a practical and feasible fundraising plan, along with stringent fund management practices, can prevent fund wastage and optimize fund utilization efficiency. These measures not only facilitate smooth project progression and improve project management efficiency but also enhance the project’s economic and social outcomes.

Shear Wave Velocity Applications in Geomechanics with Focus on Risk Assessment in Carbon Capture and Storage Projects

Shear wave velocity (Vs) has significant applications in geoengineering investigations. With the ongoing rise in carbon capture and storage (CCS) initiatives, the role of Vs in monitoring the CO2 sequestration sites is escalating. Although many studies have been conducted to assess CCS-induced risks, no inclusive research has been conducted integrating those investigations. This study strives to collate and integrate the applications of Vs in geoscience with an emphasis on CCS risk assessment. Based on this research, major CCS-induced risks were detected: induced seismicity, caprock failure, groundwater contamination, fault reactivation, and reservoir deformation. These risks were inclusively described, and the mathematical formulations incorporating the Vs parameter in risk analysis were elaborated. It was concluded that Vs applications can be further extended in monitoring CO2 plume migration, optimizing CO2 injection pressures, preventing shallow water contamination, and predicting CCS-induced seismic events. All these applications require fully coupled hydromechanical analysis based on poroelasticity theory. Hence, various factors including pore pressure, in situ stresses, faults distribution, and poroelastic parameters must be carefully determined before the CO2 injection phase. The mathematical formulations presented in the present study are quite applicable for granting the safety and long-term success of subsurface carbon sequestration.

Analysis of seepage failure probability for high core rockfill dams during rapid drawdown of reservoir water level

Rapid drawdown (RDD) is an abnormal reservoir flood control operation that may cause seepage failure of high core rockfill dams. In this paper, a novel framework combining Bayesian inference and transient unsaturated seepage simulation is proposed to evaluate the seepage failure probability of the core during RDD. The dynamic hydraulic boundaries utilized in the simulation are first determined by the performance of water release structures. The extreme learning machine (ELM) is then employed as the surrogate model to establish the mapping relationship between material parameters with simulated seepage pressure and hydraulic gradient. The Bayesian inference method is applied to characterize the uncertainty of material parameters based on seepage monitoring data. Finally, the posterior distribution samples of random parameters are utilized to calculate the seepage failure probability of the core. The seepage safety of the core under different drawdown rates is studied with a real 186 m high core rockfill dam. The results reveal that a faster RDD rate can lead to an increased probability of seepage failure. The maximum failure probability is 35.56 % for the four release structures working together (about 0.316 m/h), higher than 23.16 % for the three release structures (about 0.165 m/h). It is also found that the peak of seepage failure probability often occurs in the middle stage of RDD. Therefore, to ensure the safety of the project, it is important to control the RDD rate and carefully monitor the seepage pressure. From the point of view of seepage failure, the proposed method for evaluating the safety of core rockfill dams under RDD is of high guiding significance to the risk assessment of similar projects under extreme conditions.

Prospect Theory-Based q-Rung Orthopair Fuzzy TODIM Method for Risk Assessment of Renewable Energy Projects

Prospect Theory-Based q-Rung Orthopair Fuzzy TODIM Method for Risk Assessment of Renewable Energy Projects

Risk assessment and classification prediction for water environment treatment PPP projects.

Water treatment public-private partnership (PPP) projects are pivotal for sustainable water management but are often challenged by complex risk factors. Efficient risk management in these projects is crucial, yet traditional methodologies often fall short of addressing the dynamic and intricate nature of these risks. Addressing this gap, this comprehensive study introduces an advanced risk classification prediction model tailored for water treatment PPP projects, aimed at enhancing risk management capabilities. The proposed model encompasses an intricate evaluation of crucial risk areas: the natural and ecological environments, socio-economic factors, and engineering entities. It delves into the complex relationships between these risk elements and the overall risk profile of projects. Grounded in a sophisticated ensemble learning framework employing stacking, our model is further refined through a weighted voting mechanism, significantly elevating its predictive accuracy. Rigorous validation using data from the Jiujiang City water environment system project Phase I confirms the model's superiority over standard machine learning models. The development of this model marks a significant stride in risk classification for water treatment PPP projects, offering a powerful tool for enhancing risk management practices. Beyond accurately predicting project risks, this model also aids in developing effective government risk management strategies.

TRENDS AND FUTURE DIRECTIONS OF RISK MANAGEMENT APPROACHES APPLIED IN MODULAR CONSTRUCTION PROJECTS: SYSTEMATIC REVIEW AND META-ANALYSIS

In modular construction, given the different undertakings compared to conventional construction, the latter deems less risky. However, the current industry is faced with malpractices which are loopholes disabling full capacity performance. There is a noticeable lack of studies specifically analyzing the risk management approaches. This study aims to vigorously analyze all risk management approaches applied in previous projects by compiling qualitative and quantitative content for the eased risk assessment of modular projects. By performing a systematic literature review and meta-analysis, 175 documents were finalized and manually analyzed. Synthesis of the literature was carried out to generate graphs, illustrations, and tabularized content, followed by critical explanation for relevant risk management approaches identified according to risk category and project criteria. Risk management approaches for modular construction were showcased in terms of yearly trends, geographic involvement, keywords mostly encountered, and universities and institutions involvement. Classified risk management approaches were tabularized alongside a research domain targeting technical risk management approaches. Future work scopes were suggested with percentage initiations from analyzed studies. This study is a fundamental steppingstone in broadening knowledge on risk management approaches of modular construction and will aid both academicians and practitioners to get direct insights on current trends with project-oriented results showcased.

Injection Temperature Impacts on Reservoir Response during CO2 Storage

Summary Sustained injection of industrial-scale volumes of cold CO2 into warmer subsurface rock will result in extensive cooling which can alter rock mass mechanical behavior and fluid migration characteristics. Advanced simulation tools are available to assess and characterize such phenomena; however, the effective use of these tools requires appropriate injection temperatures and rock thermophysical parameters (in addition to geomechanical and hydraulic properties). The primary objective of this study was to demonstrate the sensitivity of injection-induced tensile fracturing and fault reactivation to injection temperature and reservoir thermophysical properties during CO2 injection operations. This was achieved by (1) compiling and reviewing thermophysical parameter data available for formations in the province of Alberta, Canada, and CO2 injection temperature records for CO2 injection projects in western Canada and (2) using a 3D, physics-based, fully integrated hydraulic fracturing and reservoir simulation numerical model to examine the geomechanical response of several potential CO2 reservoirs in the Alberta Basin as a function of injection temperature, thermal conductivity (TC), and coefficient of linear thermal expansion (CLTE) values. The simulation results indicate that reducing the fluid injection temperature from 15°C (assumed in previous work) to 2°C (conservative value selected based on temperature data reviewed in this work) could trigger extensive vertical (20–130 m high, 100–600 m long) tensile fractures with rapid fracture initiation and full vertical growth within short periods (weeks to months) and continued horizontal length increase. When low values for thermophysical properties are used, the results show that thermally-induced tensile fracturing is unlikely, whereas the use of high values results in extensive tensile fracturing in all simulations. A similar conclusion was reached for the thermally-induced reactivation (unclamping) of proximal, critically-stressed faults. Notably, slip is predicted for all simulations where high thermophysical property values are used. This confirms that accurate determination of minimum fluid injection temperature and thermophysical parameters is important for containment risk assessment for commercial-scale CO2 storage projects. Another significant outcome of this work is the observation that most thermophysical parameters in the available data were measured using experimental conditions and/or temperature paths that are not representative of CO2 injection projects. As such, the development and validation of best practice approaches for accurate assessment of these parameters seem necessary.

Seismic Microzonation of the Himalayan Region Considering Site Characterization: Application toward Seismic Risk Assessment for Sustainable Tunneling Projects

Seismic Microzonation of the Himalayan Region Considering Site Characterization: Application toward Seismic Risk Assessment for Sustainable Tunneling Projects

Risk Assessment to improve the performance of Projects

The research, focal point on a wide-ranging investigation of the processes associated with identifying, assessing, and managing threats associated with various projects. The main objective is to enhance the performance by efficiently identifying and mitigating the potential hazards that may arise during the lifecycle of the project. While using a quantitative approach for this, the study includes surveying and interviewing a total of 50 project managers and team members to collect primary data. The collected data is subjected to statistical analysis and thematic investigation to discover patterns, trends, and correlations between risk assessment practices and project performance indicators. The research results offer valuable insights into the existing utilization of risk assessment methodologies in project management and their effect on project success. The analysis evaluates the effectiveness of various risk assessment strategies and techniques, encompassing both qualitative and quantitative methods. Additionally, the study explores organizational factors influencing the implementation and outcomes of risk assessment processes.

Implementation of a Combined Fuzzy Controller Model to Enhance Risk Assessment in Oil and Gas Construction Projects

Implementation of a Combined Fuzzy Controller Model to Enhance Risk Assessment in Oil and Gas Construction Projects

Digital Application of Construction Project Cost Risk Control and Management Based on BIM Technology

Abstract The core objective of this study is to explore the application of BIM technology in the whole process of cost management of construction projects and to assess the project risk. The study covers the key stages of project planning, design, transaction and construction, and focuses on analyzing the cost management process of investment decision, design estimate, transaction and construction. Through questionnaire surveys and expert interviews with 72 valid samples, this study collects data on green building and analyzes the quality of the risk assessment data. Through questionnaire survey and expert interviews with 72 good samples, this study collects data on green building and analyzes the quality of the risk assessment data. In establishing the BIM-based project cost risk model, the study analyzes the management elements of BIM and IPD in detail, including the quantification of technical and management components. three case projects show that the application of BIM and IPD technologies in different projects has different focuses, and produces other management effects. for example, the number of engineering changes (e.g., the number of changes to the project) exhibited by the project B For example, the number of project changes (NCO) is 35, the cost change rate (COP) is 0.93%, the duration change rate (DCP) is −10.25%, and the number of contract negotiations (NRFI) is 6. The risk evaluation model is applied in a mixed-use project in Xiamen, China, and the results of the simulation experiments show that the method is effective in realizing the risk assessment of the construction project, and the clustered nature of the output features is good. The average value of the risk fuzzy variable is 0.576, and the minimum value is 0.192.

Rating Risks in Robotic Process Automation (RPA) Projects: An Expert Assessment Using an Impact-Uncontrollability Matrix

This research paper explores the risks associated with Robotic Process Automation (RPA) projects, with a focus on the impact of disregarding key factors that affect project success. While previous studies have identified challenges and success factors for RPA projects, there is a lack of research that systematically and quantitatively evaluates these factors. To address this gap, the study uses a two-dimensional matrix to rate the impact and controllability of such risks associated with key factors in RPA projects, with data collected from 20 subject matter experts. The findings suggest that project managers should pay close attention to RPA development and process management. The high controllability ratings imply that proper planning and execution can effectively manage the identified risks. The study offers valuable insights into risk assessment for RPA projects and can assist project managers in increasing project success rates.

Bayesian state-space synthetic control method for deforestation baseline estimation for forest carbon credits

Abstract Carbon credits from the reducing emissions from deforestation and degradation (REDD+) projects have been criticized for issuing junk carbon credits due to invalid ex-ante baselines. Recently, the concept of ex-post baseline has been discussed to overcome the criticism, while ex-ante baseline is still necessary for project financing and risk assessment. To address this issue, we propose a Bayesian state-space model that integrates ex-ante baseline projection and ex-post dynamic baseline updating in a unified manner. Our approach provides a tool for appropriate risk assessment and performance evaluation of REDD+ projects. We apply the proposed model to a REDD+ project in Brazil and show that it may have had a small, positive effect but has been overcredited. We also demonstrate that the 90% predictive interval of the ex-ante baseline includes the ex-post baseline, implying that our ex-ante estimation can work effectively.

МЕТОДИКА ОЦЕНКИ РИСКА ПРОЕКТОВ ПО РАЗРАБОТКЕ СИСТЕМ ОБЕСПЕЧЕНИЯ УСТОЙЧИВОСТИ ФУНКЦИОНИРОВАНИЯ ОБЪЕКТОВ С ИСПОЛЬЗОВАНИЕМ НЕЙРОННЫХ СЕТЕЙ

Problem Statement (Relevance).Instrument-making industries need to improve the risk assessment of projects for the design of an automated information system for ensuring the stability of the functioning of a production facility. Objectives.Development of a risk assessment method for the development of an automated information system to ensure the sustainability of the operation of a production facility in order to improve quality.Methods Applied. Methods of operations research.Originality. The method, unlike the existing ones, allows you to assess the risk of a project taking into account the main risk factors inherent in this subject area Result. The method of risk assessment of projects for the creation of AISOU at the stage of the life cycle of the "Design" system is proposed. Practical Relevance.It consists in the ability of users of the EIS OU to more objectively and with less labor to assess the risk of the project of the developed system for assessing the stability of the functioning of the instrument-making facility.

Integrated Approach for Project Risk Assessment and Evaluation Under Risk Interactions

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.

Seismic analysis of buildings with basements

This research work analyzes how seismic waves affect buildings with basements that are usually studied independently: In surface structures the inertial forces originated by the movement of their base and in subway structures the deformations due to ground motion are considered. The building located in the city of Quito with twelve elevated floors and five subfloors is studied using nonlinear finite element modeling, considering that the subsoil depends on seismic excitations in a dynamic soil-basement-structure set, for which it is modeled together with its geotechnical components. The superstructure is integrated to the infrastructure and a time-history analysis is included with the most representative earthquakes can affect the building. The present investigation has three basic components: seismic, geotechnical, and structural, which are merged by means of specialized 3D structural engineering programs. In the seismic component, local and regional information about tectonic faults within Ecuador is collected; the building is then located on a seismic risk map and the magnitudes, types, and other parameters of the site where the building is located are determined. Using the results of the Earthquake Risk Assessment Project for the city of Quito (TREQ) and the Pacific Earthquake Engineering Research Center (PEER) database, three relevant seismic events are chosen for the time-history analysis that meet the seismic risk data for the building. For the geotechnical soil component, the geotechnical parameters of the original design of the building foundation, stratigraphy, and characteristics of soil solids, among others, are used. The structural component considers the type of material, geometry, dimensions, joints between elements and others of the original design for both basements and superstructure. The connection of these three components is carried out using Midas Gen and Midas GTS NX. The results of deformations as a function of time are compared with similar standards, studies, and investigations.

Risk assessment and optimal scheduling of serial projects

The valuation and planning of complex projects are becoming increasingly challenging with rising market uncertainty and the deregulation of many industries, which have also raised the need for efficient risk management. We take the perspective of a private firm interested in sequential capacity expansion of a project and develop a framework for measuring the downside risk of the serial project and optimising the sequence of the stages. Under general distributional assumptions for the duration of each stage, we present an accurate representation of the project’s net present value (NPV) distribution based on a Pearson curve fit, leading to closed-form expressions for the associated risk measures. We then assess the impact of duration variability on the value at risk and demonstrate its role in stochastic project scheduling. We also account for the trade-off between maximising the expected NPV and minimising the risk exposure, and obtain the optimal schedule for risk-averse decision-makers. It becomes obvious that both the duration variability of each stage and the decision-makers’ risk preferences can significantly affect the optimal sequence of the stages and that high duration variability is not always undesirable, even for risk-averse decision-makers.

Risk Assessment in Sustainable Infrastructure Development Projects: A Tool for Mitigating Cost Overruns

The persistent decline in infrastructure spending, notably within the transportation sector, raises concerns about governments’ capacity to meet the demands of a sustainable growing economy. The incorporation of risk assessment in the analysis and computation of potential cost overruns emerges as an effective and efficient tool, underpinning the economic and financial sustainability of infrastructure expansions. Focusing on the “State Road No. 51 of Alemagna Vittorio Veneto” (SSv-51) variant, this study analyzes and proposes a model to forecast the possible cost overruns of an infrastructure project. The application of the risk assessment tool proposed by the National Anti-Corruption Authority (ANAC) offers valuable insights into potential risks associated with project costs and their valuation. The matrix developed in the current study draws from the ANAC Matrix, which comprises four categories of risk divided into 21 risk types. The selection is based on the project’s characteristics, and a matrix is compiled that forecasts the combination of the probability of risk occurrence and the cost impacts on the project. The proposed risk matrix serves as a practical tool for managing uncertainties and estimating potential cost overruns, estimating ex ante a possible cost increase of 7.53%. This projected increase differs only by 1.34% from the final execution costs, mitigating the unforeseen cost overruns not estimated by the initial project. The proposed risk assessment tool emphasizes the importance of integrating risk management into project planning and execution. The research investigated an applied case utilizing an easily adaptable tool, suitable for potential future implementation, further advancement, and broader testing across various project samples in the future. The study provides a framework to assess and mitigate risks linked to cost overruns. As nations navigate infrastructure development complexities, proactive risk management practices are indispensable for efficient resource management, ensuring the economic and financial sustainability of these complex projects.

A Data-Driven Artificial Neural Network Approach to Software Project Risk Assessment

In the realm of software project management, predicting and mitigating risks are pivotal for successful project execution. Traditional risk assessment methods have limitations in handling complex and dynamic software projects. This study presents a novel approach that leverages artificial neural networks (ANNs) to enhance risk prediction accuracy. We utilize historical project data, encompassing project complexity, financial factors, performance metrics, schedule adherence, and user-related variables, to train the ANN model. Our approach involves optimizing the ANN architecture, with various configurations tested to identify the most effective setup. We compare the performance of mean squared error (MSE) and mean absolute error (MAE) as error functions and find that MAE yields superior results. Furthermore, we demonstrate the effectiveness of our model through comprehensive risk assessment. We predict both the overall project risk and individual risk factors, providing project managers with a valuable tool for risk mitigation. Validation results confirm the robustness of our approach when applied to previously unseen data. The achieved accuracy of 97.12% (or 99.12% with uncertainty consideration) underscores the potential of ANNs in risk management. This research contributes to the software project management field by offering an innovative and highly accurate risk assessment model. It empowers project managers to make informed decisions and proactively address potential risks, ultimately enhancing project success.

Impact of Machine Learning on IT Project Management

This paper comprehensively examines the effects of Artificial Intelligence (AI), specifically machine learning on IT project management in the context of technologically advanced surroundings. The study emphasizes the importance and advantages of incorporating machine learning techniques to simplify project management procedures and effectively implement IT projects. In providing a thorough review of machine learning applications in project management, the paper emphasizes how these technologies can completely transform decision-making, resource allocation, risk assessment, and overall project performance. This paper provides insightful information on the transformative potential of machine learning in enhancing technology project management methods through a thorough literature review and detailed analysis.