Dynamic Risk Evaluation Research Articles

Advanced Risk Assessment for Deep Excavation in Karst Regions Using Improved Dempster–Shafer and Dynamic Bayesian Networks

This study presents a novel risk-assessment methodology for deep foundation pit projects in karst regions, aimed at enhancing project safety and decision-making processes. This approach amalgamates fuzzy dynamic Bayesian networks with a refined Dempster–Shafer (DS) evidence theory to tackle the intricate uncertainties present in such contexts. A comprehensive risk index system, derived from historical accident cases, relevant standards, and the literature, encompasses environmental, design, construction, and management factors. Initial probabilities for each risk factor are determined through the integration of expert knowledge and fuzzy theory. The enhanced Dempster–Shafer theory is utilized to fuse diverse information sources, culminating in a robust and dynamic risk evaluation model. This model leverages real-time monitoring data to dynamically assess and adjust risk levels throughout the construction process. The validation of the proposed method is demonstrated through a detailed case study of the Guangzhou Tangxi Section 1 deep foundation pit project, which effectively identified critical risk factors and facilitated proactive construction strategy adjustments. To further evaluate the reliability of the methodology, comparisons were made with three alternative methods, and applications were conducted on three additional deep foundation pit projects. These comparative analyses confirm the superior reliability and applicability of the proposed methodology across varied scenarios.

Dynamic evaluation of road and bridge engineering construction safety risk based on Fuzzy Dynamic Bayesian Network research

To comprehensively and objectively evaluate the actual safety condition in road and bridge engineering construction, the road and bridge engineering construction safety risk evaluation index system is constructed combined with the factors induced by emergencies in the road and bridge engineering construction process. Aiming at the dynamic uncertainty of road and bridge construction safety risk, using Fuzzy Set Theory and an improved similar aggregation method to determine the prior probabilities and conditional probabilities of network nodes, and then selecting the transition probabilities of nodes through expert opinions and incident reports, leading to the development of a dynamic evaluation model for safety risks in road and bridge engineering construction based on Fuzzy Dynamic Bayesian Network, this model can make the construction safety risk prediction result accurately. Taking the Hebi City Provincial Highway 304 reconstruction project as an example for analysis, the results indicate that the model can accurately predict the probability of changes in safety risks in road and bridge engineering construction. Additionally, it can identify critical risk factors and provide crucial supporting information for decision-makers to optimize risk management strategies.

Dynamic risk evaluation of supersaturated dissolved gas at a confluence with unsteady flow conditions

The confluence of two rivers gives rise to a distinctive hydrodynamic structure where numerous nutrients can be accumulated, making the confluence area a pivotal biological habitat. The threat of supersaturated dissolved gas in upstream water caused by dam discharging is indispensable for the confluence areas. Therefore, it is imperative to study and assess the risks faced by fish in these areas, particularly when the merging of main and tributary streams leads to varying degrees of supersaturation and non-uniform distribution of dissolved gases. The spatial and temporal distribution of supersaturated dissolved gas at the natural confluence in the Yangtze River utilizing continuous prototype monitoring and unsteady flow numerical simulation was investigated, and a dynamic evaluation method that classifies risk into three levels (high, medium, low) was proposed. The confluence area exhibited evident non-uniform distribution characteristics, wherein the primary factors influencing this distribution were identified as the dissolved gas saturation of inlet flow, and flow ratio. The presence of non-continuous discharge intensity resulted in an increase in medium-risk areas within the river, thereby mitigating the risk to fish from the supersaturation of the discharge. A velocity barrier was formed at the confluence, with the velocity range being 1.8 ∼ 3.4 m/s, exceeding the fish’s burst speed of 1.7 m/s. Because of the barrier, there was no direct access to the low-saturation areas, and potential avoidance routes are primarily located in the medium-risk zones downstream. The paper offers a useful method to assess risk distribution in critical ecological regions characterized by non-constant characteristics. Additionally, it recommends dam discharging strategies to expand the scope of fish risk avoidance.

Determinants Of Investment Portfolio Performance: A Systematics Literature Review

A thorough grasp of the variables influencing portfolio performance is necessary given the significance of investment portfolio management in the face of shifting financial market dynamics. This study uses a method called the Systematic Literature Review (SLR) approach with an emphasis on Prefered Reporting Item for Systematic Review and Meta-Analysis (PRISMA) 2020, which has not been discovered in prior research on the same issue. The study highlights the importance of ratios like Sharpe, Treynor, Jensen, and Sortino in assessing investment portfolio success. It also highlights the impact of non-financial variables like household preferences on performance. Return is crucial, and diversification can lower risk and boost returns. The study emphasizes the use of Value-at-Risk (VaR) for dynamic risk evaluation.

An overview of risk assessment and monitoring of malignant transformation in cirrhotic nodules.

Cirrhotic liver nodules can progress to hepatocellular carcinoma (HCC) through a multi-step carcinogenesis model, with dysplastic nodules being particularly high risk. Currently, monitoring the progression of non-HCC cirrhotic nodules is primarily through dynamic observation, but there is a lack of sensitive, efficient, and convenient methods. Dynamic monitoring and risk evaluation of malignant transformation are essential for timely treatment and improved patient survival rates. Routine liver biopsies are impractical for monitoring, and imaging techniques like ultrasound, computed tomography, and magnetic resonance imaging are not suitable for all patients or for accurately assessing subcentimeter nodules. Identifying serum biomarkers with high sensitivity, specificity, and stability, and developing a multi-index evaluation model, may provide a more convenient and efficient approach to monitoring pathological changes in cirrhotic nodules.

Evaluation and treatment of casing pressure in injection/production wells of SQ underground gas storage

The evaluation and management of sustaining casing pressure (SCP) in injection/production wells of underground gas storage(UGS) is an important part of the wellbore integrity. SQ underground gas storage has experienced ten complete injection/production cycles, which puts forward high requirements on management of well integrity. At present, there are different degrees of SCP of injection/production wells of SQ UGS, which seriously threatens the safety of the UGS. This paper analyzes casing pressure in different injection/ production stages and working conditions of injection/production wells, combined with API RP 90-2, NORSOK, ISO/TS 16530-2, SY/T5724-2008 standards. We established an SCP evaluation process and a static and dynamic SCP risk evaluation system, and then proposed a four-level risk control mode for UGS injection/production wells. We formulated a production management method for wells with SCP, constantly improved the operating environment of UGS wells. We managed the injection /production wells with SCP higher than the MAWOP, especially the annular leak problem of well S7. The SCP treatment achieves effective plugging, guarantees the integrity of the injection /production wells, and achieves good repair effect, which has certain guiding significance for the evaluation of the integrity of the injection/production wells of the UGS and the management of the SCP.

An Integrated Transportation Distance between Kernels and Approximate Dynamic Risk Evaluation in Markov Systems

An Integrated Transportation Distance between Kernels and Approximate Dynamic Risk Evaluation in Markov Systems

Depicting Risk Profile over Time: A Novel Multiperiod Loan Default Prediction Approach

With the rapid development of fintech, the need for dynamic credit risk evaluation is becoming increasingly important. While previous studies on credit scoring have mostly focused on single-period loan default prediction, we call for a new avenue—multiperiod default prediction (MPDP)—to depict risk profiles over time. To address the challenges raised by MPDP, such as monotonic default probability prediction and complex relationship accommodation, we propose a novel approach, hybrid and collective scoring (HACS). We design a hybrid modeling strategy to predict whether and when a borrower will default separately through a default discrimination model and a default time estimation model, respectively, and synthesize them through a probabilistic framework. To accommodate various possible patterns of default time and measure the distribution of default probability over successive time intervals, we propose a joint default modeling method to train the default time estimation model. Empirical evaluations at the model (time-to-default prediction performance and discrimination performance) and mechanism (identifiability and discriminability) levels, as well as impact analyses at the application (granting performance and profitability performance) level, show that HACS outperforms the benchmarked survival analysis and multilabel learning methods on all fronts. It can more accurately predict time-to-default and provide financial institutions and investors better decision-support in granting loans and selecting loan portfolios.

Spatial relationship-aware rapid entire body fuzzy assessment method for prevention of work-related musculoskeletal disorders

In the advent of Industry 5.0, advances in human-centered smart manufacturing (HSM) accentuate the role of humans in human–machine collaboration. This development has catapulted human health in human-machine systems to the forefront of the conversation. Although various tools have emerged to mitigate work-related musculoskeletal disorders (WMSDs), combining biomechanics with human morphology, the extant methods primarily hinge on expert scoring. Such methods display a step-wise change between risk levels, yielding inadequate assessment accuracy and posing challenges to human health assurance in HSM. To address these issues, this study proposes a spatial relationship-aware rapid entire body fuzzy assessment technique. The proposed method enhances the rapid entire body assessment (REBA) by enacting a dynamic evaluation of WMSD-related risk via a deep learning-based 3D pose reconstruction. Contrary to the step-wise transitions between REBA's different risk levels, the proposed method actualizes a fuzzy assessment of WMSD risk by introducing weights between these levels. This innovation allows for a more accurate risk assessment for workers engaged in HSM. Validation through experiments conducted on data from an automobile production line demonstrates that the proposed method can achieve a precision rate of 99.31%. Demo videos and code are available at https://github.com/giim-hf-lab/REBA-PLUS.

Abstract 17498: Association Between 5-Year Change in Cardiovascular Risk and Atherosclerotic Cardiovascular Disease Incidence: A Multicohort Study

Introduction: Early measurement of atherosclerotic cardiovascular disease (ASCVD) risk factors contains valuable information about an individual's risk history. Hypothesis: Individuals with different historical cardiovascular risk statuses have varied risks of future ASCVD incidence. Methods: We analyzed pooled data from 7 community-based prospective cohort studies with up to 20-year follow-ups ( panel A ). White or Black participants aged 40-75 years without prevalent ASCVD were included. Cardiovascular risk was assessed using the pooled cohort equation and was categorized into non-high (<20%) or high risk (≥20%). Changes in CVD risk over a 5-year interval were considered. The main outcome is incident ASCVD. Results: Among 11026 participants (mean [SD] age,60.0 [8.1] years). 4272 (38.7%) were female and 3127 (28.4%) were Black. During a median follow-up of 9.9 years, 2560 (23.2%) ASCVD events occurred. Compared to individuals with consistently high risk, participants with non-high to high risk (HR 0.67; 95%CI 0.59-0.77), high to non-high risk (HR 0.57; 95%CI 0.41-0.80), and consistently non-high risk (HR 0.33; 95%CI 0.29-0.37) had a lower risk of incident ASCVD. Compared to individuals with consistently non-high risk, participants with high to non-high risk (HR 1.74; 95%CI 1.26-2.41), non-high to high risk (HR 2.04; 95%CI 1.84-2.27), and consistently high risk (HR 3.03; 95%CI 2.69-3.42) had an increased risk of incident ASCVD ( panel B ). Conclusions: Changes in the CVD risk status over time inform dissimilar ASCVD risk than a single risk assessment. Dynamic risk evaluation might enable more accurate cardiovascular risk stratification and decision on preventive interventions should take historical risk status into account.

Dynamic risk evaluation of hydrogen station leakage based on fuzzy dynamic Bayesian network

This paper introduces a dynamic risk assessment method for hydrogen leakage at hydrogen stations, employing fuzzy dynamic Bayesian networks. To begin, we utilize the Bow-Tie model for an in-depth analysis and consolidation of the primary risk factors contributing to hydrogen leak explosions. Subsequently, we establish a dynamic Bayesian network model for hydrogen leakage at hydrogen stations, adhering to the mapping rules derived from the Bow-Tie model. In order to reduce the impact of subjectivity, our model derives event prior probabilities through expert scoring and fuzzy set theory. Furthermore, the inclusion of a time factor is followed by the application of the Leaky Noisy-or gate model to enhance and calculate conditional probabilities, leading to the generation of time-series change curves for hydrogen leak probability and the probabilities associated with each accident consequence. The research findings yield valuable insights for risk assessment, accident prevention, and emergency management at hydrogen stations.

Association between 5-year change in cardiovascular risk and the incidence of atherosclerotic cardiovascular diseases: a multi-cohort study

BackgroundThe influence of the historical cardiovascular risk status on future risk of atherosclerotic cardiovascular disease (ASCVD) is poorly understood. We aimed to investigate the association between 5-year changes in cardiovascular risk and ASCVD incidence.MethodsWe analyzed pooled data from seven community-based prospective cohort studies with up to 20 years of follow-up data. The study populations included White or Black participants aged 40–75 years without prevalent ASCVD. Cardiovascular risk was assessed using the pooled cohort equation and was categorized into non-high (< 20%) or high risk (≥ 20%). Changes in cardiovascular disease (CVD) risk over a 5-year interval were recorded. The main outcome was incident ASCVD.ResultsAmong 11,026 participants (mean [SD] age, 60.0 [8.1] years), 4272 (38.7%) were female and 3127 (28.4%) were Black. During a median follow-up period of 9.9 years, 2560 (23.2%) ASCVD events occurred. In comparison with individuals showing a consistently high CVD risk, participants whose CVD risk changed from non-high to high (hazard ratio [HR], 0.67; 95% confidence interval [CI] 0.59–0.77) or high to non-high (HR, 0.57; 95% CI 0.41–0.80) and those with a consistently non-high risk (HR, 0.33; 95% CI 0.29–0.37) had a lower risk of incident ASCVD. In comparison with individuals showing a consistently non-high CVD risk, participants whose CVD risk changed from high to non-high (HR, 1.74; 95% CI 1.26–2.41) or from non-high to high risk (HR, 2.04; 95% CI 1.84–2.27) and those with a consistently high risk (HR 3.03; 95% CI 2.69–3.42) also showed an increased risk of incident ASCVD.ConclusionsIndividuals with the same current CVD risk status but different historical CVD risks exhibited varying risks of future ASCVD incidents. Dynamic risk evaluation may enable more accurate cardiovascular risk stratification, and decision-making regarding preventive interventions should take the historical risk status into account.Graphical

Dynamic risk evaluation and control of electrical personal accidents

Dynamic risk evaluation and control of electrical personal accidents

The Safety Risk Assessment of Mine Metro Tunnel Construction Based on Fuzzy Bayesian Network

With the acceleration of urbanization, the construction of urban subway tunnel networks is advancing towards deeper, denser, and larger subterranean forms. Currently, there is a lack of systematic identification and dynamic reasoning analysis of factors throughout the entire process of subway tunnel construction using the mining method. To reduce the probability of accidents and improve safety risk management in the whole process of subway tunnel construction using the mining method, we propose a dynamic safety evaluation method based on Fuzzy Set Theory (FST) and Bayesian Network (BN). Firstly, based on the identification of main stages of the construction process using the Work Breakdown Structure, a safety risk evaluation index system for subway tunnel construction using the mining method was constructed according to the Risk Breakdown Structure. Secondly, by combining Fuzzy Set Theory, the Analytic Hierarchy Process, and the Bayesian Network, we established a dynamic safety risk evaluation model for subway tunnel construction using the mining method, based on FBN. Lastly, taking a large-section tunnel project using the mining method as an example, the effectiveness and accuracy of this model were verified. The results showed: (1) Causal reasoning analysis indicated that, under the condition of known prior probability, if the case reasoning result is greater than 5%, there is a significant possibility of a safety risk incident. The evaluation results of the model are basically consistent with the actual situation. (2) Diagnostic reasoning analysis revealed that factors such as the tunnel excavation method, the quality of the main waterproof construction, the quality of the detailed construction waterproofing, the design of the monitoring and measurement plan, and the results of the monitoring and measurements, are the main influences on the safety of subway tunnel construction using the mining method. (3) Secondary diagnostic reasoning demonstrated that repeated diagnostic reasoning for the main influencing factors, leading to an investigation path dominated by critical risk factors, can effectively reduce the overall project risk. This research is expected to provide useful insights for the scientific management of safety risks in the construction of subway tunnels using the mining method.

Driving risk prevention in usage-based insurance services based on interpretable machine learning and telematics data

Usage-based insurance (UBI) adjusts premiums based on an individual policyholder’s dynamic risk evaluation, incentivizing policyholders to maintain safe driving behavior in pursuit of a lower insurance premium. Although post-trip interventions can improve driving behavior significantly, most interventions do not provide risk mitigation strategies tailored to long-term driving behavior. This study proposes a novel approach for driving risk prevention in UBI services, providing risk analysis services and tailoring driving suggestions for policyholders. Advanced prediction models and model interpretation methods were adopted to assess and analyze personal driving risks, respectively. A multi-objective counterfactual model was developed to generate risk mitigation strategies for policyholders. The significance of driving behavior in risk mitigation and its actionability were assessed to provide realistic and actionable suggestions for policyholders. The case study, based on a real dataset from an insurance company in China, showed that the proposed approach can be directly embedded in the existing UBI service framework to provide personalized feedback and services to policyholders.

Dynamic evaluation of cropland degradation risk by combining multi-temporal remote sensing and geographical data in the Black Soil Region of Jilin Province, China

Cropland degradation has attracted increasing attention because the important production functions of cropland and its sensitivity to climate change. Based on multi-temporal remote sensing and geographical data, this study developed a comprehensive index of cropland degradation risk (CDRI) using the improved k-means method and applied it to the Black Soil Region of Jilin Province, China, to analyze the changes in cropland degradation risk from 2005 to 2020. The average cropland degradation risk in 2020 was generally higher than that in 2005. Croplands with changes in degradation risk types accounted for 14.27% of the total cropland area, of which croplands showing an increase and decrease in degradation risk accounted for 8.47% and 5.80% of total, respectively. From 2005 to 2020, areas of croplands with a low degradation risk decreased, but those of croplands with a medium degradation risk increased significantly. The synthetic image of bare soil, temperature, precipitation, and soil nutrients contributed greatly to the CDRI. Degradation risk of croplands distributed in black soil, chernozem, and meadow soil fluctuated significantly. The proposed CDRI method comprehensively considers the degree and spatial pattern of cropland degradation risk and has good prospects for the rapid detection of cropland degradation on a large scale.

Differentiated Thyroid Cancer in a Pediatric Population: Estimating the Risk of Recurrence and Evolution Over Time.

Background Differentiated thyroid cancer (DTC) is the most common endocrine cancer during childhood, and the prognosis is usually good. The 2015 American Thyroid Association (ATA) pediatric guidelines for DTC classify patients into three categories (low, intermediate, and high) that represent the risk for persistent/recurrent disease. The "Dynamic Risk Stratification" (DRS) System showed that, in adults, reassessment of disease status during follow-up was a better predictor of disease status at the end of follow-up when compared to ATA risk stratification. This system is still not validated for the pediatric population with DTC. Our aim was toevaluate the usefulness of the DRS system in predicting DTC disease behaviour in this specific population. We also aimed to evaluate potential clinical-pathological factors associated with persistent disease at the end of follow-up. Methods A retrospective analysis of 39 pediatric patients (≤18 years) with DTC was conducted in our institution between 2007 and 2018, including 33 patients who had follow-up ≥ 12 months; these were classified into ATA risk groupsandre-stratified according to their response to treatment at 12-24 months of follow-up.The associations between the ordinal variables of the baseline ATA risk groupand the disease status re-evaluated 12-24 months after diagnosis (as per the DRS system) and at the end of follow-up were evaluated using a linear-by-linear association test.Gender, age at diagnosis, tumor size, multicentricity, extrathyroid extension, vascular invasion, lymph node metastasis, distant metastasis, and stimulated thyroglobulin (sTg) during the first RAI administration were evaluated as potential factors associated with persistent disease at 27 months after diagnosis using Firth's bias-reduced penalized-likelihood logistic regression. Results In this study, 39 patients were retrospectively analyzed, including 33 patients who had follow-ups ≥ 12 months with a median time of 56 (27-139) months who were classified in ATA risk groups and then re-stratified depending on their response to treatment between 12 and 24 months of follow-up. There was a statistically significant association between ATA risk groups and re-evaluation at 12 and 24 months (p=0.001) and between these two stratifications and the state of disease at final follow-up (p<0.001 for both).Factors with a statistically significant association with persistent disease at 27 months of follow-up were male sex, lymph node metastases at diagnosis, distant metastasis, extrathyroidal extension, and stimulated Tg values. Conclusions The assessment of the response to treatment between 12 and 24 months and at the end of follow-up refines the initial ATA risk stratification, confirming that dynamic risk evaluationis also helpful in the pediatric population.

Dynamic risk assessment for underground gas storage facilities based on Bayesian network

Loss of the underground gas storage process can have significant effects, and risk analysis is critical for maintaining the integrity of the underground gas storage process and reducing potential accidents. This paper focuses on the dynamic risk assessment method for the underground gas storage process. First, the underground gas storage process data is combined to create a database, and the fault tree of the underground gas storage facility is built by identifying the risk factors of the underground gas storage facility and mapping them into a Bayesian network. To eliminate the subjectivity in the process of determining the failure probability level of basic events, fuzzy numbers are introduced to determine the prior probability of the Bayesian network. Then, causal and diagnostic reasoning is performed on the Bayesian network to determine the failure level of the underground gas storage facilities. Based on the rate of change of prior and posterior probabilities, sensitivity and impact analysis are combined to determine the significant risk factors and possible failure paths. In addition, the time factor is introduced to build a dynamic Bayesian network to perform dynamic assessment and analysis of underground gas storage facilities. Finally, the dynamic risk assessment method is applied to underground gas storage facilities in depleted oil and gas reservoirs. A dynamic risk evaluation model for underground gas storage facilities is built to simulate and validate the dynamic risk evaluation method based on the Bayesian network. The results show that the proposed method has practical value for improving underground gas storage process safety.

Risk Assessment and Prediction of Underground Utility Tunnels Based on Bayesian Network: A Case Study in Beijing, China

The underground utility tunnels accommodate various types of urban lifelines, which are of great significance for improving the living standards of the citizens. With the rapid development of underground utility tunnels, the large-scale underground utility tunnel systems are gradually becoming the operational lifeblood of China’s large cities. Currently, most of the underground utility tunnels’ risks are estimated and analyzed from a static perspective, and the analysis results are one-sided. This study proposes a dynamic risk evaluation framework. A risk assessment and sensitivity analysis framework based on Bayesian network is established in this study. Combined with the groundwater and electric tunnel risk accident case study, the operation and maintenance data of Beijing Future Science and Technology City from 2010 to 2018 are collected for learning to obtain the conditional probability of the Bayesian network node by using the K2 algorithm. The overall evolution process from the beginning to the end of groundwater tunnel accidents is clearly described and displayed. Through sensitivity analysis and critical path analysis, the critical points of an accident and the probabilities of risk occurrence are identified and predicted. This proposed framework could facilitate the underground utility tunnel management for controlling risk resources, mitigating risk damage and reducing risk losses.

Dynamic Risk Assessment of High Slope in Open-Pit Coalmines Based on Interval Trapezoidal Fuzzy Soft Set Method: A Case Study

Effective high slope risk assessment plays an important role in the safety management and control of the open-pit coal mining process. Traditional slope stability risk assessment methods rarely consider the time factor or evaluate the dynamic change of high slope in an open-pit mine at a certain time in a sensitivity assessment. This paper develops an interval trapezoidal fuzzy soft set method to achieve the high slope dynamic risk evaluation. The proposed dynamic interval trapezoidal fuzzy soft set method for risk assessment of high slope in an open-pit coal mine is developed by integrating the time points and weights of slope risk factors. The extended interval trapezoidal fuzzy soft set was used to calculate the weights of risk factors at different times, and the Fuzzy Analytical Hierarchy Process (FAHP) method was applied to determine the weights of risk factors. The weight change of different risk factors with time can be easily achieved with the proposed method. As a case study, this approach is implemented into a risk assessment model for the north high slope in Shengli #1 open-pit mine located in Xilinhot, Inner Mongolia. The model complies with three time points and contains 4 primary risk factors (S) and 17 secondary risk factors. The results indicated that the hydrological climate conditions and slope geometry conditions were the high risk factors affecting this open-pit coal mine slope. The reasonability and effectiveness of the evaluation results were verified with in-situ observations and measurements. This dynamic risk assessment method is helpful for improving safety management and control for the high slopes of open-pit mines in the coal mining process.