Traditional Risk Assessment Methods Research Articles

Dynamic Evaluation of the Safety Risk During Shield Construction near Existing Tunnels via a Pair[sbnd]Copula Bayesian Network

Shield tunnel construction near existing tunnels can lead to the deformation and settlement of adjacent tunnels, posing safety risks. To analyze and evaluate the key factors influencing such safety risks, a risk assessment method based on the pair-copula Bayesian network (PCBN) model is proposed. The PCBN model, which integrates a Bayesian network (BN) with a pair copula (PC), is applied to simulate a complex risk factor system and related risk factors. Using the constructed PCBN model, a case study was conducted focusing on excavation between Wuhan Metro Lines 8 and 3, and it was determined that the deformation and settlement risk during shield tunnel construction falls into the safe risk category (Level 3). The key risk factors that significantly impact deformation and settlement risk were identified through correlation analysis and were ranked by importance as follows: advance rate, total thrust, and grouting pressure. Compared with traditional risk assessment methods and BNs, the PCBN model can flexibly handle complex relationships among risk factors, especially nonlinear and tail dependencies, due to the integration of PCs. This approach not only enhances the accuracy of risk assessment but also improves the interpretability of the model, with transparent internal mechanisms, largely due to the use of the BN. On the basis of these findings, decision-making recommendations are proposed to reduce the risk of shield tunnel construction near existing tunnels. The implementation of these recommendations in the Wuhan Metro project has yielded positive outcomes.

Harnessing Risks with Data: A Leakage Assessment Framework for WDN Using Multi-Attention Mechanisms and Conditional GAN-Based Data Balancing

Assessing leakage risks in water distribution networks (WDNs) and implementing preventive monitoring for high-risk pipelines has become a widely accepted approach for leakage control. However, existing methods face significant data barriers between Geographic Information System (GIS) and leakage prediction systems. These barriers hinder traditional pipeline risk assessment methods, particularly when addressing challenges such as data imbalance, poor model interpretability, and lack of intuitive prediction results. To overcome these limitations, this study proposes a leakage assessment framework for water distribution networks based on multiple attention mechanisms and a generative model-based data balancing method. Extensive comparative experiments were conducted using water distribution network data from B2 and B3 District Metered Areas in Zhengzhou. The results show that the proposed model, optimized with a balanced data method, achieved a 40.76% improvement in the recall rate for leakage segment assessments, outperforming the second-best model using the same strategy by 1.7%. Furthermore, the strategy effectively enhanced the performance of all models, further proving that incorporating more valid data contributes to improved assessment results. This study comprehensively demonstrates the application of data-driven models in the field of “smart water management”, providing practical guidance and reference cases for advancing the development of intelligent water infrastructure.

Design and Visual Implementation of a Regional Energy Risk Superposition Model for Oil Tank Farms

Ensuring the safety of oil tank farms is essential to maintaining energy security and minimizing the impact of potential accidents. This paper develops a quantitative regional risk model designed to assess both individual and societal risks in oil tank farms, with particular attention to energy-related risks such as leaks, fires, and explosions. The model integrates factors like day–night operational variations, weather conditions, and risk superposition to provide a comprehensive and accurate evaluation of regional risks. By considering the cumulative effects of multiple hazards, including those tied to energy dynamics, and the stability and validity of the model are researched through Monte Carlo simulations and case application. The results show that the model enhances the reliability of traditional risk assessment methods, making it more applicable to oil tank farm safety concerns. Furthermore, this study introduces a practical tool that simplifies the risk assessment process, allowing operators and decision-makers to evaluate risks without requiring in-depth technical expertise. The methodology improves the ability to safeguard oil tank farms, ensuring the stability of energy supply chains and contributing to broader energy security efforts. This study provides a valuable method for researchers and engineers seeking to enhance regional risk calculation efficiency, with a specific focus on energy risks.

Quantifying the relation between aging-related trabecular bone microstructure and mechanical properties with digital volume correlation approach

Trabecular bone, a highly porous 3D network of interconnected rods and plates known as trabeculae, plays a critical role in bone strength and integrity. Osteoporosis, a condition commonly associated with aging, leads to deteriorations in bone mass and microarchitecture, manifesting as reduced bone mineral density, thinner trabecular struts, increased trabecular spacing, and a shift from trabecular plates to rods. These microstructural alterations contribute to age-related fragility fractures. To quantitatively explore the link between microstructural changes and the mechanical properties of trabecular bone, we combined mechanical testing with micro-computed tomography (micro-CT) and digital volume correlation (DVC) to measure 3D full-field deformation in trabecular bone samples from healthy and osteoporotic human cadaver vertebrae. Our multi-step mechanical testing characterized strain concentration propagation under axial compressive loading, providing new insights into how microstructural parameters influence trabecular bone’s mechanical strength. This study demonstrates that imaging-based approaches for evaluating bone mechanical strength could serve as an alternative to traditional fracture risk assessment methods, which primarily rely on bone mineral density.

Machine-learning-based prediction of cardiovascular events for hyperlipidemia population with lipid variability and remnant cholesterol as biomarkers.

Dyslipidemia poses a significant risk for the progression to cardiovascular diseases. Despite the identification of numerous risk factors and the proposal of various risk scales, there is still an urgent need for effective predictive models for the onset of cardiovascular diseases in the hyperlipidemic population, which are essential for the prevention of CVD. We carried out a retrospective cohort study with 23,548 hyperlipidemia patients in Shenzhen Health Information Big Data Platform, including 11,723 CVD onset cases in a 3-year follow-up. The population was randomly divided into 70% as an independent training dataset and remaining 30% as test set. Four distinct machine-learning algorithms were implemented on the training dataset with the aim of developing highly accurate predictive models, and their performance was subsequently benchmarked against conventional risk assessment scales. An ablation study was also carried out to analyze the impact of individual risk factors to model performance. The non-linear algorithm, LightGBM, excelled in forecasting the incidence of cardiovascular disease within 3years, achieving an area under the 'receiver operating characteristic curve' (AUROC) of 0.883. This performance surpassed that of the conventional logistic regression model, which had an AUROC of 0.725, on identical datasets. Concurrently, in direct comparative analyses, machine-learning approaches have notably outperformed the three traditional risk assessment methods within their respective applicable populations. These include the Framingham cardiovascular disease risk score, 2019 ESC/EAS guidelines for the management of dyslipidemia and the 2016 Chinese recommendations for the management of dyslipidemia in adults. Further analysis of risk factors showed that the variability of blood lipid levels and remnant cholesterol played an important role in indicating an increased risk of CVD. We have shown that the application of machine-learning techniques significantly enhances the precision of cardiovascular risk forecasting among hyperlipidemic patients, addressing the critical issue of disease prediction's heterogeneity and non-linearity. Furthermore, some recently-suggested biomarkers, including blood lipid variability and remnant cholesterol are also important predictors of cardiovascular events, suggesting the importance of continuous lipid monitoring and healthcare profiling through big data platforms.

Artificial Intelligence-Powered Risk Assessment in Supply Chain Safety

The increasing complexity and globalization of supply chains necessitate robust risk management strategies to ensure safety and resilience. Traditional risk assessment methods often fall short in dynamically adapting to the rapidly changing conditions and voluminous data inherent in modern supply chains. This study explores the potential of Artificial Intelligence (AI)-powered risk assessment to address these limitations in the context of Malaysia's supply chain industry. By employing AI technologies such as machine learning, IoT, and predictive analytics, organizations can significantly enhance their risk management capabilities, improving predictive accuracy, real-time monitoring, and overall operational efficiency. Through a qualitative analysis involving in-depth interviews with supply chain managers, AI experts, and technology vendors, the study identifies the strategies employed for AI integration, the perceived effectiveness of these technologies, and the challenges faced in implementation. The findings highlight the importance of robust data governance, the development of explainable AI models, and continuous skill development to overcome barriers related to data quality, model interpretability, and high implementation costs. The study concludes with recommendations for fostering a safer and more resilient logistics environment in Malaysia, emphasizing the need for comprehensive AI adoption frameworks and scalable solutions for small and medium-sized enterprises.

Multi-Criteria Analysis in Circular Economy Principles: Using AHP Model for Risk Assessment in Sustainable Whisky Production

As the whisky industry applies circular economy principles to maximize resource utilization and minimize environmental impact, companies become exposed to several risks, which require complex assessments to ensure reliable outcomes. This study provides an organized framework to identify, prioritize, and rank various risk factors commonly observed in the whisky industry through the development of an analytical hierarchy process (AHP) multi-criteria analysis model. Experts from 18 small European distilleries identified five main risk criteria and nineteen sub-criteria from brainstorming workplace observations and categorized them as: environmental (5), operational (4), technological innovation (3), food safety (3), and economical (4) risks. The analytical hierarchy process (AHP) approach was used to determine the weights and ranks of the main criteria and sub-criteria based on the survey responses received from experts from each distillery. The final judgements are consistent, as indicated by consistency values (CR) of less than 0.1 for all risk criteria. Unlike traditional risk assessment methods, the AHP model effectively integrates qualitative and quantitative data, aiding strategic decision making in the whisky industry by breaking down complex problems into manageable sub-problems. Future research directions may expand the criteria and explore additional sustainable practices.

Real-time risk assessment of distribution systems based on Unscented Kalman Filter

The continuous growth of renewable energy and the load level has posed increasingly severe operational risks to distribution systems. In view of this, this paper combines state estimation with risk assessment, and uses the results of distribution system state estimation based on Unscented Kalman Filter as the input of risk assessment. With the combination, the sampling based on probability distributions in traditional risk assessment methods is no longer needed, thus avoiding the difficulty of updating probability distributions timely according to the latest information in real-time operation. By applying the proposed risk assessment method, the real-time assessment of operational risks in perspectives of bus voltage, branch power, and renewable energy utilization is achieved. Meanwhile, the weight of each risk index is properly determined according to both subjective and objective knowledge by using Analytic Hierarchy Process method and entropy weight method. Case studies show that the proposed method achieves effective assessment of comprehensive risks in the operation of distribution system.

International Journal of Futuristic Innovation in Engineering, Science and Technology

With the rapid expansion of cloud computing, the need for robust cybersecurity measures has become paramount. As organizations increasingly migrate their data and applications to the cloud, they encounter numerous cybersecurity risks that threaten the integrity, confidentiality, and availability of their information. Traditional risk assessment methods often fall short in addressing the dynamic and complex nature of cloud environments. This paper explores a novel approach to cybersecurity risk assessment in cloud computing using machine learning techniques. We propose a comprehensive framework that leverages machine learning algorithms to detect, predict, and mitigate potential cybersecurity threats. The framework incorporates various supervised and unsupervised learning models, including decision trees, support vector machines, and neural networks, to analyze large datasets and identify patterns indicative of security breaches. Our approach also includes feature selection methods to optimize the performance of these models by focusing on the most relevant risk factors. We conducted extensive experiments on publicly available cloud security datasets, which demonstrated the efficacy of our machine learning-based risk assessment framework in identifying threats with high accuracy and minimal false positives. The results indicate that our approach significantly outperforms traditional risk assessment techniques in terms of speed, scalability, and adaptability to evolving threat landscapes. This study contributes to the field by providing a scalable and efficient solution for enhancing cybersecurity in cloud environments. It highlights the potential of machine learning to revolutionize how we assess and manage cybersecurity risks, offering a proactive stance against emerging threats. Future work will focus on refining the model by incorporating real-time data and exploring advanced machine learning techniques such as deep learning and reinforcement learning to further enhance its predictive capabilities.

BIG DATA IN CREDIT RISK MANAGEMENT: A SYSTEMATIC REVIEW OF TRANSFORMATIVE PRACTICES AND FUTURE DIRECTIONS

This systematic review examines the profound impact of big data analytics on credit risk management in financial institutions, highlighting both its transformative benefits and the associated challenges. By integrating a wide range of real-time and diverse data sources—such as customer behavior, market trends, and macroeconomic indicators—financial institutions have significantly enhanced the accuracy, efficiency, and predictive power of their credit risk models. The findings reveal that institutions employing big data analytics have achieved substantial reductions in default rates, with improvements of up to 30% over traditional risk assessment methods. However, the adoption of big data also presents considerable challenges, particularly in ensuring data privacy and security, navigating complex regulatory environments, and overcoming technical hurdles related to data integration, storage, and processing. These issues necessitate robust data governance frameworks and significant investments in IT infrastructure. Despite these challenges, big data is expected to play an increasingly central role in credit risk management, offering early adopters a strategic advantage through enhanced risk assessment and decision-making capabilities. This review provides critical insights for financial institutions, policymakers, and researchers, emphasizing the need for ongoing innovation and adaptation to fully harness the potential of big data in this field.

BIG DATA IN CREDIT RISK MANAGEMENT: A SYSTEMATIC REVIEW OF TRANSFORMATIVE PRACTICES AND FUTURE DIRECTIONS

This systematic review examines the profound impact of big data analytics on credit risk management in financial institutions, highlighting both its transformative benefits and the associated challenges. By integrating a wide range of real-time and diverse data sources—such as customer behavior, market trends, and macroeconomic indicators—financial institutions have significantly enhanced the accuracy, efficiency, and predictive power of their credit risk models. The findings reveal that institutions employing big data analytics have achieved substantial reductions in default rates, with improvements of up to 30% over traditional risk assessment methods. However, the adoption of big data also presents considerable challenges, particularly in ensuring data privacy and security, navigating complex regulatory environments, and overcoming technical hurdles related to data integration, storage, and processing. These issues necessitate robust data governance frameworks and significant investments in IT infrastructure. Despite these challenges, big data is expected to play an increasingly central role in credit risk management, offering early adopters a strategic advantage through enhanced risk assessment and decision-making capabilities. This review provides critical insights for financial institutions, policymakers, and researchers, emphasizing the need for ongoing innovation and adaptation to fully harness the potential of big data in this field.

Data-driven risk analysis of nonlinear factor interactions in road safety using Bayesian networks

This paper aims to demonstrate nonlinear risk factor interactions based on a data-driven approach using a Bayesian network model, providing a road safety use case. Road safety is a critical issue worldwide, with approximately 1.3 million road traffic deaths each year (WHO). Traditional road safety risk assessment methods often analyze individual factors separately; however, these assessments fail to capture the complex dynamics of real-world analysis, in which multiple factors interact through nonlinear relationships. In this study, a novel road safety risk assessment approach that uses a Bayesian network model to explore the nonlinear relationships among road safety risk factors is developed. Through the analysis of extensive crash reports from the state of Maryland, the complex interdependencies among various risk factors and their cumulative impact on road safety are investigated. Our findings show that two combined risk factors have different effects on risk level when considered individually. Two case studies related to human state risk factors and environmental risk factors, such as driving under the influence and snowy roads, as well as fatigue and snowy roads, have an amplified effect on the risk level. The findings highlight the importance of considering nonlinear interactions among risk factors when developing effective and targeted strategies for accident prevention and road safety improvement. This research contributes to the field of road safety by presenting a new methodology for understanding and mitigating road safety hazards.

Vulnerability-oriented risk identification framework for IoT risk assessment

The proliferation of Internet of Things (IoT) systems across diverse applications has led to a notable increase in connected smart devices. Nevertheless, this surge in connectivity has induced a broad spectrum of vulnerabilities and threats, jeopardizing the security and safety of IoT applications. Security risk assessment methods are commonly employed to analyze risks. However, traditional IT and existing IoT-tailored security assessment methods often fail to fully address key IoT aspects: complex assets intercommunication, dynamic system changes, assets’ potential as attack platforms, safety impacts of security breaches, and assets resource constraints. Such oversights lead to significant risks being overlooked in the IoT ecosystem. In this paper, we propose a novel vulnerability-oriented risk identification framework comprising a four-step process as a core element of IoT security risk assessment, applicable to any IoT system. Our process enhances both traditional and IoT-specific security risk assessment methods by providing tailored approaches that address their crucial oversights for comprehensive IoT risk assessment. We validate our process with a case study of an IoT smart healthcare system using a proposed expert-driven approach. The results confirm that our process effectively identifies critical attack scenarios originating from the lack of proper security measures, mobility, and intercommunication processes of IoT devices in the healthcare system. Furthermore, our analysis reveals potential attacks that exploit the IoT devices as platforms to target the backend and user domains. We demonstrate the feasibility of our process for identifying realistic risks by conducting simulations of two derived attack scenarios using the Contiki Cooja network simulator.

Nationwide meta-analysis of microplastic distribution and risk assessment in China's aquatic ecosystems, soils, and sediments

Microplastic (MP) accumulation has recently become a pressing global environmental challenge. As a major producer and consumer of plastic products, China’s MP pollution has garnered significant attention from researchers. However, accurate and comprehensive investigations of national-level MP pollution are still lacking. In this study, we systematically collated a national MP pollution dataset consisting of 7766 water, soil, and sediment sampling sites from 544 publicly published studies, revealing the spatiotemporal distribution and potential risks of MP pollution in China. The results indicate that MP distribution is influenced by various regional factors, including economic development level, population distribution, and geographical environment, exhibiting considerable range and complexity. MP concentrations are generally higher in economically prosperous areas, but the degree of pollution varies significantly across different environmental media. Given the uncertainty and lack of standardized data in traditional microplastic risk assessment methods, this article highlights the urgency of developing a comprehensive big data and artificial intelligence (AI)-based regulatory framework. This work provides a substantial amount of accurate MP pollution data and offers a fresh perspective on leveraging AI for microplastic pollution regulation.

Enhanced Condition Monitoring of Gearboxes using Fuzzy Comprehensive Evaluation and Group Decision-making: A Case Study

In this study, we introduce a novel methodology for the condition monitoring of gearboxes by integrating fuzzy comprehensive evaluation and group decision-making. Traditional risk assessment methods often suffer from subjective biases and uncertainties inherent in expert evaluations. Our approach mitigates these limitations by aggregating expert opinions using fuzzy membership functions and assigning weights based on the similarity of individual evaluations to the group consensus. This converts qualitative judgments into quantitative measures, resulting in more precise and objective Risk Priority Numbers (RPNs). We validate the efficacy of our methodology through a case study involving a gearbox. The primary failure modes identified include gear tooth wear, misalignment, bearing failure, lubrication failure, and thermal overload. Our results indicate a significant improvement in condition monitoring accuracy, with calculated fuzzy RPN values closely aligning with historical data and expert feedback. Comparative analysis highlights the advantages of our methodology over conventional RPN calculations, particularly in reducing subjective biases and enhancing the reliability of risk assessments. Our findings demonstrate that this methodology can be effectively applied in various industrial settings, establishing a robust framework for mechanical system condition monitoring. Future research should explore integrating advanced data analytics and machine learning techniques to enhance the methodology's accuracy and efficiency.

Risk Assessment of Highway Infrastructure REITs Projects Based on the DEMATEL—ISM Approach

Highway infrastructure projects financed by real estate investment trusts (REITs) have garnered significant attention due to their potential for generating stable returns. However, assessing the risks associated with such projects remains a critical challenge. This study addresses this gap by proposing a novel approach to risk assessments utilizing the Decision-Making Trial and Evaluation Laboratory (DEMATEL) integrated with Interpretive Structural Modeling (ISM). Unlike traditional risk assessment methods, which often treat risk factors as isolated entities, the DEMATEL—ISM approach provides a comprehensive framework that reveals the direct and indirect relationships between risk factors, thus offering a systemic understanding of the interactions. The results found that falling market prices, insufficient operating revenues, and operational inefficiencies were direct influences, and high financing costs and force majeure were the fundamental influences, while other factors acted as intermediate influences. In addition, the study identifies direct and indirect relationships between different risk factors, providing a hierarchical topology of risk factors to reflect the relationships between the risk factors. The findings not only contribute to enhancing the understanding of risk dynamics in highway infrastructure REITs projects, but also offer a structured framework for risk assessment and mitigation. This research represents a significant innovation in the field of infrastructure investment risk management, offering practical implications for project planning, decision-making, and risk management strategies.

Heart Disease Prediction using Machine Learning

Abstract: Heart disease remains a leading cause of morbidity and mortality worldwide. Early detection and accurate prediction of heart disease risk factors are crucial for effective prevention and timely intervention. In recent years, machine learning techniques have emerged as powerful tools for predictive analytics in healthcare. This paper presents a comprehensive review of recent advances in the application of machine learning algorithms for heart disease prediction. The review begins by discussing the significance of heart disease prediction and the limitations of traditional risk assessment methods.

Unraveling the interplay of human decisions and flood risk: An agent-based modeling approach

Flooding is a frequent and highly destructive natural disaster that requires effective flood management policies. This paper addresses the limitations of traditional flood risk assessment methods, focusing on the interplay between human decisions and flood risk using an integrated approach. Agent-Based Modeling (ABM) is employed to capture the social behavior of government, households, and farmers in three flood management scenarios. The study is conducted in the Fall River watershed, Idaho, USA, over a 30-year period. The results highlight the important influence of crop selection decisions on flood damage outcomes, with alfalfa emerging as the preferred crop due to its profitability and lower damage potential (around 67 % crop loss compared to more than 81 % for crops like corn, barley, and wheat). The levee scenario is identified as the most effective strategy, significantly reducing flood damage by more than 37 % and generating the highest average annual profit for farmers. The study emphasizes the need for comprehensive flood risk management strategies that address both frequent and rare flood occurrences in mitigating flood damage and promoting community resilience. By integrating human-flood interactions into flood risk assessment, this study offers valuable insights for Policymakers and stakeholders can utilize the research implications to develop effective strategies that mitigate damage and enhance community resilience.

Beyond inundation: a comprehensive assessment of sea level rise impact on coastal cultural heritage in China

The rise in sea levels, driven by global climate change, poses a significant threat to cultural heritage in coastal regions. Traditional risk assessment methods, focusing on direct inundation, often fail to consider the crucial impact of socio-economic factors, which are significantly vulnerable to sea level rise. To bridge this gap, this study introduces an innovative Sea Level Rise Cultural Heritage Impact Assessment Model (SLR-CHIA Model), a novel approach that integrates both land inundation and socio-economic aspects. This comprehensive model evaluates potential risks to various types of cultural heritage in coastal China, including intangible cultural heritage, relics, and traditional villages. The study’s findings are striking: (1) About 7.79% of coastal villages, 53.94% of relics, and 2.53% of intangible cultural heritage are potentially at high risk in a 100-year sea level rise event; (2) Relics in the Eastern coast and villages in the Southern coast are most vulnerable; (3) Different types of cultural heritage rely on diverse principal factors for protection, resulting in varied risk levels under sea level rise conditions. The SLR-CHIA Model provides a vital methodological framework for evaluating cultural heritage risks in other global regions.

Resilience in Focus: Rethinking the Risk Matrix

This research presents a three-dimensional risk matrix model designed for the analysis and prioritisation of critical risks in the context of resilience. Traditional risk assessment methods prevalent in information security, which typically juxtapose the likelihood and consequences of risks, are inadequate for fully capturing the intricacies of critical risks. The proposed three-dimensional model addresses these shortcomings by cohesively integrating the dimensions of likelihood, impact and cost of risk management. This integration provides a holistic tool for resilient risk analysis that goes beyond the capabilities of traditional models. A key feature of this model is its ability to address the complexities associated with critical risks, which are often not adequately addressed by traditional risk matrices due to their stochastic nature and significant potential impact on organisational resilience. By incorporating budgetary constraints into the risk assessment process, the model enables a more objective and quantifiable approach to managing critical risks. It shifts the evaluative focus from a purely probabilistic perspective to a cost-value based assessment, emphasising the balance between potential benefits and mitigation expenditure. This approach not only refines the accuracy of critical risk assessment, but also enhances existing risk management practices, providing a more robust and strategic tool for managing organisational risk.