Risk Assessment Method Research Articles

Novel multivariate design concept for floating wind turbines by Gaidai multivariate reliability method and deconvolution scheme

Development of novel risk and reliability assessment methods is intended to support safer construction of offshore structures, subjected to environmental wave loads. Current study investigated 10-MW FWT (i.e., Floating Wind Turbine), operating under realistic environmental conditions. While increasing operating safety, enhanced risk and reliability assessment methods may eventually help reduce manufacturing and maintenance costs. Excessive structural dynamics being usually caused by environmental stressors, acting on structural system. Environmental loads resulting from ambient wind and wave motions are typical for offshore structures. Current work advocates a novel risk and reliability assessment methodology that allows for reliable forecasting of failure/damage risks, arising from excessive FWT structural dynamics. Recently developed Gaidai multivariate reliability methodology along with state-of-the-art deconvolution method had been employed. Unlike existing reliability approaches such as Weibull-type, GP (i.e., Generalized Pareto), POT (i.e., Peaks Over the Threshold), etc., the recommended methodology does not rely on any pre-assumed functional class, when extrapolating failure probability functional tail. Practical advantages of the suggested multivariate reliability methodology combined with deconvolution scheme over, that is, 4-parameter Weibull’s extrapolation method had been demonstrated. Suggested methodology makes effective use of even limited underlying datasets, enabling robust and accurate projections of multidimensional structural system failure/damage risks. Overall methodological performance suggests that numerically stable and accurate extreme dynamics forecasts for FWT structural bending moments might be obtained, utilizing suggested multivariate reliability methodology. Deconvolution extrapolation approach being more numerically stable than parametric extrapolation techniques, due to its non-parametric nature.

Fracture risk assessment in the presence of competing risk of death.

To identify the optimal statistical approach for predicting the risk of fragility fractures in the presence of competing event of death. We used real-world data from the Dubbo Osteoporosis Epidemiology Study that has monitored 3035 elderly participants for bone health and mortality. Fragility fractures were ascertained radiologically. Mortality was confirmed by the State Registry. We considered four statistical models for predicting fracture risk: (i) conventional Cox's proportional hazard model, (ii) cause-specific model, (iii) Fine-Gray sub-distribution model, and (iv) multistate model. These models were fitted and validated in the development (60% of the original sample) and validation (40%) subsets, respectively. The model performance was assessed by discrimination and calibration analyses. During a median follow-up of 11.3 years (IQR: 7.2, 16.2), 628 individuals (34.5%) in the development cohort fractured, and 630 (34.6%) died without a fracture. Neither the discrimination nor the 5-year prediction performance was significantly different among the models, though the conventional model tended to overestimate fracture risk (calibration-in-the-large index = - 0.24; 95% CI: - 0.43, - 0.06). For 10-year risk prediction, the multistate model (calibration-in-the-large index = - 0.05; 95% CI: - 0.20, 0.10) outperformed the cause-specific (- 0.23; - 0.30, - 0.08), Fine-Gray (- 0.31; - 0.46, - 0.16), and conventional model (- 0.54; - 0.70, - 0.39) which significantly overestimated fracture risk. Adjustment for competing risk of death has minimum impact on the short-term prediction of fracture. However, the multistate model yields the most accurate prediction of long-term fracture risk and should be considered for predictive research in the elderly, who are also at high mortality risk. Fracture risk assessment might be compromised by the competing event of death. This study, using real-world data found a multistate model was superior to the current competing risk methods in fracture risk assessment. A multistate model is considered an optimal statistical method for predictive research in the elderly.

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.

Penetration Study of Key Technologies for Cybersecurity Risk Assessment

In recent years, with the frequent occurrence of cyber security incidents, people have paid more attention to it. Information security risk assessment is a very important research topic. This paper gives a brief overview of the theory of cybersecurity risk assessment, focuses on the description of the current mainstream cybersecurity risk assessment methods, classifies and compares the existing methods according to the nature of the methods, and analyses the advantages, disadvantages, and application scope of each method. Finally, the main factors affecting the evaluation results are summarized and refined, and future research hotspots in this field are proposed. Through the empirical analysis of the three factors, the influence of the correlation of the three factors, the uncertainty of the evaluation indexes, and the timeliness of the evaluation on the evaluation results are concluded, which provides a reference for future research on evaluation methods.

Shaping of the risk of a series-parallel manufacturing structure maintenance according to quasi-coherence and the [formula omitted]-th survival value

The paper presents the authors’ model for determining the probability value of the survival function of the difference of two random variables: TP – the amount of time of correct operation and TB – the amount of time of failure. For this purpose, the concept of K-th survival value is introduced. The K-th survival value defines the probability that the total time of correct operation of the production system will be longer by at least k time units (for k≥0) than the total amount of time of failures. The model was exemplified by a real production system with a series–parallel structure. In the nesting manufacturing structure, the availability of machines determines the number and the type of flow streams of the processed material. To formalise the chains of relations at a given Δt, this paper utilises the quasi-coherence property. The paper identifies four possible cases of system operation: 1) a fully operational system in which every machine is capable of meeting the assumed production schedule; 2) a system which has lost the property of quasi-coherence (certain system objects are in an inoperable state) but it is possible to meet the assumed production schedule; 3) a system which has lost the property of quasi-coherence but the assumed task schedule is possible to meet after redefining the material flow streams; 4) a system which has lost the property of quasi-coherence and it is impossible to meet the assumed production schedule. Based on the definition of the survival function [1] S of the random variable T: ST:=PT>τ=1-FTτ,τ∈R, the concept of K-th survival value was introduced. For possible cases, K-th survival value was defined as: KT1,Ti=PT1-Ti≥k=1-FT1-Tik (for i=2,3,4), where T1 and Ti are random variables with given probability distributions. In turn, k is a deterministic value that determines the total number of time units of correct system operation in relation to the total failure time. The application of the developed algorithm enables the system to be analysed in terms of three aspects: (1) to determine the probability of on-time execution of tasks, taking into account historical values of machine failure and correct operation durations; (2) to determine the stability of the system based on empirical data; (3) to monitor the level of risk in real time. The paper validates the model by determining the stability of the analysed system for real machine failure data. The introduced criterion of stochastic stability of the production system has been verified for data in which distributions of correct operation times and failure times are random variables from the family of Gamma distributions. The presented model can be included in the group of multifactorial risk assessment methods.

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.

Review of Risk Assessment Methods for Cybersecurity Attacks on Road Network and Intelligent Transportation System Applications

One of the most discussed infrastructure issues of our time is cybersecurity. A transportation system that connects not only people but also logistics to make the community around the world closer is one of the critical infrastructures requiring cybersecurity to perform its functions. Transportation systems include aviation, maritime, pipeline, railroad, and road networks. This study focuses only on the roadway network system. Cutting-edge technologies related to highway networks, such as variable message signs, vehicular ad hoc networks, and in-vehicle networks, have been developed to improve safety and efficiency. Those technologies make transportation systems more complex and integrated, bringing many potential vulnerabilities and cyber risks. This can attract an adversary to attack and exploit the system. The number of cybersecurity attacks on transportation systems has been growing for many years. However, it is not feasible to protect against all cybersecurity attacks in the system. The risk assessment concept is proposed to prioritize risk resulting from attacks to support decision-makers in formulating appropriate policies or countermeasures. This study reviews risk assessment methods for cybersecurity attacks on road networks and intelligent transportation system applications. Three potential risk assessment methods are examined for road network systems: the National Institute of Standards and Technology Special Publication 800-30, Attack Potential and Damage Potential, and the fuzzy analytic hierarchy process.

CardioRiskNet: A Hybrid AI-Based Model for Explainable Risk Prediction and Prognosis in Cardiovascular Disease.

The global prevalence of cardiovascular diseases (CVDs) as a leading cause of death highlights the imperative need for refined risk assessment and prognostication methods. The traditional approaches, including the Framingham Risk Score, blood tests, imaging techniques, and clinical assessments, although widely utilized, are hindered by limitations such as a lack of precision, the reliance on static risk variables, and the inability to adapt to new patient data, thereby necessitating the exploration of alternative strategies. In response, this study introduces CardioRiskNet, a hybrid AI-based model designed to transcend these limitations. The proposed CardioRiskNet consists of seven parts: data preprocessing, feature selection and encoding, eXplainable AI (XAI) integration, active learning, attention mechanisms, risk prediction and prognosis, evaluation and validation, and deployment and integration. At first, the patient data are preprocessed by cleaning the data, handling the missing values, applying a normalization process, and extracting the features. Next, the most informative features are selected and the categorical variables are converted into a numerical form. Distinctively, CardioRiskNet employs active learning to iteratively select informative samples, enhancing its learning efficacy, while its attention mechanism dynamically focuses on the relevant features for precise risk prediction. Additionally, the integration of XAI facilitates interpretability and transparency in the decision-making processes. According to the experimental results, CardioRiskNet demonstrates superior performance in terms of accuracy, sensitivity, specificity, and F1-Score, with values of 98.7%, 98.7%, 99%, and 98.7%, respectively. These findings show that CardioRiskNet can accurately assess and prognosticate the CVD risk, demonstrating the power of active learning and AI to surpass the conventional methods. Thus, CardioRiskNet's novel approach and high performance advance the management of CVDs and provide healthcare professionals a powerful tool for patient care.

Assessment and Source Analysis of Heavy Metal Pollution in Farmland Soil Around Tin Mine

Studying the status and source analysis of heavy metal pollution in farmland in typical mining and processing areas is an important prerequisite for promoting farmland soil ecological restoration and farmland protection in concentrated mining areas. In this study, the heavy metal content of farmland soil around a mining area in southwest China was detected, and the pollution status, distribution law, health risks, and sources of heavy metals were studied by using the land accumulation index method, comprehensive pollution index method, kriging interpolation method, health risk assessment method, and PMF receptor model on the sampling data. The results showed that the mean values of eight heavy metals in farmland soil except Ni exceeded the local soil background values, and the results of the ground accumulation index evaluation showed that Cd and Hg were extremely polluted； Pb and As showed medium pollution-heavy pollution； and Cr, Zn, Ni, and Cu were lightly polluted. In the health risk assessment, oral ingestion was the main exposure route posing a health risk to the human body； the main element that constituted non-carcinogenic health risks was As, and the carcinogenic risks were from As and Cd. PMF model analysis showed that the contribution rate of weathering natural sources of iron-bearing ore was 28.02%, and the main factors were Ca and Fe. The contribution rate of agricultural sources was 3.02%, and the main factors were Pb and As. The contribution rate of industrial and atmospheric deposition composite sources was 33.09%, and the main factor was Hg. The contribution rate of the parent material source was 17.27%, and the main factor was Ca. The contribution rate of mining activities such as mining and smelting was 18.60%, and the main factors were Zn and Cd.

Regional ecological risk assessment through the application of production-living-ecology land use function indicators: a case study of the eco-economic zone of Yellow River Delta.

To date, land use structure information has been employed extensively for ecological risk assessment (ERA) purpose in regional/landscape scales; in contrast, land use function (LUF) information-based ERA research is still scarce. Therefore, it is necessary to carry out more ERA case studies in macroscale with the help of pertinent LUF indicators. As an important way to construct production-living-ecology LUF indexes, this study employs the weighted stacking method and related economic statistical data for regional ecological risk assessment (RERA) purpose within Yellow River Delta High-efficiency Eco-economic Zone (YRDHEZ), China. This YRDHEZ-RERA research pointed out that (1) it was rational to use a series of economic statistical data to more comprehensively and precisely characterize regional production and living function grades in YRDHEZ. (2) The Yellow River Delta had lower agriculture and non-agriculture production functions, whereas the rest of the zone had higher production functions. (3) Most people lived in the south part, whereas north coastal zone had very low population density; the east part had higher per capita disposable income of urban/rural households than that of west. (4) The south part of the zone had higher production/living functions and integrated ecological risk source intensity than those of north coastal zone, whereas the coastal zone had higher ecology function, eco-environmental vulnerability, and final integrated ecological risk than those of inland region. As for regional ecological risk management, establishing nature reserve with strict spatial governance for coastal/estuarine wetlands and coordinating production/ecology functions of coastal salterns/breeding ponds are relevant feasible measures.

CYBERSECURITY ON IOT SYSTEMS BASED MULTI-LAYERED SECURITY RISK ASSESSMENT

IoT systems contribute to digital transformation through the development of smart concepts. However, the IoT has also generated new security challenges that require security tools to be adapted, such as risk analysis methodologies. Many organizations use a variety of methods and frameworks to assess cybersecurity risks. However, most current risk assessment methods are for generic software systems, so there is no holistic approach to risk assessment for IoT technologies, especially due to the diversity of IoT systems The purpose of our study is based on the following question: What IoT device factors should be considered in cybersecurity risk assessment methods? In this study we first propose a cybersecurity risk assessment model based on vectors such as vulnerabilities and attacks, which evaluates the security risk from different dimensions of physical layer, network layer and application layer. Secondly, we design the mathematical assessment model for computing risk value of IoT system and then establish the mapping relationship table that the risk value is transformed into risk level.

Semi‐partial Quadratic Subtraction Set Pair Potential (SQSSPP) method for regional drought risk assessment: A case study in Suzhou City, China

AbstractDrought risk assessment plays a crucial role in effective drought management. However, it is often challenging due to the intricate relationships among various indicators and the lack of practical guidance. This study presents a drought risk assessment model developed using the Semi‐partial Quadratic Subtraction Set Pair Potential (SQSSPP) method, which is derived from the theory of set pair analysis. The indicator system comprises 21 indicators divided into four subsystems. The SQSSPP method utilizes uncertainty information in the overall development trend of regional drought risk states by extracting connection numbers from the Subtraction Set Pair Potential (SSPP), improving the reliability of evaluation results. The SQSSPP method is validated through a case study of Suzhou City, China, from 2007 to 2017. Three grades are used to evaluate comprehensive drought risk. The result shows an overall decreasing trend over time, with a level III risk in 2010 and consistently at level II from 2011 to 2017. Indicators in the hazard and resilience subsystems are the primary factors influencing drought risk in the Suzhou City. Specific indicators requiring emphasis for improvement are identified, including arable land rate, agricultural population ratio, reservoir regulation rate, current water supply capacity, and irrigation index. The SQSSPP method not only provides targeted drought risk assessment but also provides valuable guidance for future water resource management. While the study focuses on Suzhou City, the proposed approach is applicable to broader‐scale risk management evaluations and practices.

Ecological and geological security risk assessment of underground space development in cold and arid canyon cities-Taking Ping'an District, Haidong City, Qinghai Province, as an example.

The ecological and geological problems caused by the rise of groundwater level due to the development of underground space in cold and arid canyon cities are particularly typical. Reasonably assessing the ecological and geological security risks of utilizing underground space is conducive to reducing the occurrence of ecological and geological problems during the construction and operation of underground engineering projects. Taking Ping'an District of Haidong City as an example, the topography and geomorphology of the research area were investigated in the field, and the distribution of topography and geomorphology in the research area was understood; through geological drilling and geotechnical engineering testing, the distribution of different strata in the research area was obtained; through pumping and seepage experiments, the recharge, runoff, and discharge relationship between surface water and groundwater in the research area and the water abundance of different strata are obtained, and the causes and mechanisms of geological safety risks in forest and grassland ecosystems, farmland ecosystems, and human settlements ecosystems were analyzed based on literature. Corresponding ecological geological safety risk assessment index systems and methods were established, and the ecological geological safety risks before and after the development of underground rail transit projects along both banks of the Huangshui River in the study area were evaluated. PRACTITIONER POINTS: The development of underground space in canyon type cities can easily lead to ecological and geological problems. Taking Haidong City, Qinghai Province, as an example, this study investigates the causes of ecological and geological problems caused by the development of underground spaces in canyon type cities. An ecological geological security risk assessment index system and method for canyon-type cities were established. An evaluation was conducted on the ecological and geological safety risks before and after the development of the underground rail transit projects on both sides of the research area.

Quantitative risk assessment of coal mine gas explosion based on a Bayesian network and computational fluid dynamics

A gas explosion is one of the most serious coal mine accidents. The complexity and dynamics of gas explosion accidents, resulting from both multiple risk factors and time-dependent physical parameters, necessitates the use of risk assessment techniques to analyze the accident. A quantitative risk assessment method is proposed for coal mine gas explosions based on a Bayesian Network (BN) and Computational Fluid Dynamics (CFD). The results show that, in addition to the problems of “low vigilance” and “substandard safety education”, the “irrational design of mine gas extraction” has become another major risk factor at the new stage of development of mine excavation technology. Three typical gas explosion areas are selected to analyze the possible consequences of an accident. Based on the probabilistic risk assessment model, time-dependent physical parameters such as accident conditions, location and time are analyzed. The probability of occurrence of time-dependent risk factors is quantified to calculate the risk values of the consequences of explosion accidents corresponding to different times. The proposed method helps to improve the calculation accuracy of updating the risk probability value over time and provides theoretical support for the risk analysis of coal mine gas explosion accidents.

Experimental investigation of aging effects on thermal behavior of lithium-ion batteries during charging process

Thermal runaway generated by lithium-ion batteries is considered as one of the major hazards that exhibits great thermal risk during operation. The aging behavior, as an inevitable phenomenon after long-term cycling, largely contributes to the deterioration of thermal characteristics. In this paper, comparative experiments were conducted to investigate the aging cell thermal performance during the charging process. The results indicate that the aging cell with a high charging rate presents a higher thermal risk. With the decrease of SOH, the anode structure was destroyed and side reactions reduced the thermal stability. The thermal runaway onset temperature decreased from 274.8 ± 9.1 °C for fresh to 238 ± 1.2 °C for aging cells, and the corresponding critical time shortened by approximately 1 min. In addition, the maximum heat release rate increased by 37.5 %–45.5 % for aging cells under high charging conditions, manifesting that aging cells are more prone to trigger fire accidents. Finally, a modified risk assessment method was established. This study contributes to comprehending the coupling effects of aging behavior and charging rate thermal runaway mechanisms, and provides valuable guidance for thermal runaway detection during the whole life.

Development of modified fuzzy FMEA method in environmental risk assessment of earth dams

The investigations have shown that the construction of the dam and its related facilities have significant physical–chemical and ecological effects on the ecosystem. Failure Mode and Effects Analysis (FMEA) is a technique for ranking risks in projects to construct dams, but it has many deficiencies and ambiguities. Therefore, to prevent the shortcomings of the classical method, the modified fuzzy inference system (MFIS-FMEA) method has been used by creating a two-stage model to more accurately assess the risk of Eyvashan Dam. First, all the considered indicators are weighted using the Shannon entropy method, and the environmental risk is prioritized using the Fuzzy OWA method. In this study, two-stage fuzzy reasoning and a Max–Min combination rule are used. When severity (SEV) and occurrence (OCC) variables are combined, the critical risk index (RCI) values are predicted in the first stage. RCI and detection index (DET) input are then used to predict the MFIS-RPN in the second stage. The results of the risk priority number (RPN) in the MFIS-RPN method are much more accurate and serious than the FIS-RPN method due to the two-stage nature and the use of new language terms. The results of the proposed MFIS-RPN technique show that the highest RPN was obtained with immediate action in the dam construction phase for soil erosion and soil pollution and in the dam operation phase for aquatic and water pollution. Therefore, due to the increase in risk score, it is necessary to take immediate and more accurate monitoring during the construction and operation phases.

Analysis and control of occupational safety risks using the HIRARC method in the Machining Workshop

This qualitative study focuses on occupational safety and health in vocational school machining workshops to identify significant risks related to training practices. Workplace accidents are considered a serious threat to workers and equipment, potentially affecting productivity and environmental safety. Observations at several vocational schools, especially at SMK Negeri 1 Batipuh, showed low student awareness and compliance with OHS regulations, increasing the risk of risky behaviour and accidents. To address this, the research used the Hazard Identification, Risk Assessment, and Risk Control (HIRARC) method, which systematically identifies hazards, evaluates risks based on severity and likelihood, and proposes control measures. The research at SMK Negeri 1 Batipuh involved direct observation, interviews, and analysis of SOPs in the workshop, finding significant risk variations in workshop conditions, lathe operations, welding, material handling, hand tool use, and machine protection. The results highlighted the predominance of intermediate risks in all-hazards analyzed, emphasizing the importance of developing more effective risk management strategies. This research aims to reduce accidents and improve student safety during training by addressing existing OHS implementation gaps and implementing appropriate risk controls. In addition to filling a gap in the literature, this study provides practical insights for improving OHS practices in vocational schools.

Hazard Identification Risk Assessment Onsite Survey for External Human-Induced Events Aspect of the Nuclear Power Plant in West Kalimantan, Indonesia

Every stage of the West Kalimantan Nuclear Power Plant (NPP) building process prioritizes safety. The external, human-induced events (HIE) aspect is one part of the NPP site survey, as some risks in the site evaluation procedure could compromise the safety of the activity implementers. As a result, a risk assessment study relating to occupational safety and health (OSH) is necessary. This study aims to determine the quantity of risk from NPP site survey activities in terms of the HIE aspect. The risk assessment method involves a literature review and collecting data from field verification using a quantitative method that refers to the risk assessment based on BATAN Standard 006.1:2019 to support the implementation of the OSH management system. Following that, the analysis begins with a hazard risk identification risk analysis to determine the risk probability scale and consequence scale acquisition that may result from the hazards. The research findings from the conducted assessment of risk related to the nonroutine HIE site survey show ergonomic, mechanical, and biological hazards that were developed during the preparation, field review, and reporting processes. The risk rating is category A, with a risk scale of 2, indicating acceptable risk and effective control measures. These data and information support site evaluation license requirements for Indonesia as it plans for the construction of NPPs.

Heart Disease Prediction Using Machine Learning

Heart disease remains a leading cause of mortality worldwide, necessitating improved methods for early detection and risk assessment. This paper reviews and analyzes the application of machine learning techniques in heart disease prediction, focusing on five primary algorithms: Naïve Bayes, k-Nearest Neighbor (KNN), Decision Tree, Artificial Neural Network (ANN), and Random Forest. By examining existing studies and datasets, we evaluate the effectiveness of these algorithms in predicting heart disease risk. Our analysis demonstrates that machine learning models can significantly enhance the accuracy and efficiency of heart disease prediction compared to traditional diagnostic methods. The Random Forest algorithm exhibited the highest overall performance, with studies reporting accuracy rates up to 95% in identifying potential heart disease cases. This review highlights the potential of machine learning in revolutionizing cardiovascular healthcare by enabling more personalized risk assessments and facilitating early intervention strategies. The integration of these advanced predictive models into clinical practice could substantially improve patient outcomes and reduce the global burden of heart disease. Keywords: Cardiovascular Risk Prediction, Machine Learning Algorithms, Electronic Health Records, Random Forest, Artificial Neural Networks, Feature Importance, Clinical Decision Support, Personalized Medicine, Predictive Analytics in Healthcare, Early Disease Detection.

Optimization-based stacked machine-learning method for seismic probability and risk assessment of reinforced concrete shear walls

Efficient seismic risk assessment aids decision-makers in formulating citywide risk mitigation plans, providing insights into building performance and retrofitting costs. The complexity of modeling, analysis, and post-processing of the results makes it hard to fast-track the seismic probabilities, and there is a need to optimize the computational time. This research addresses seismic probability and risk assessment of reinforced concrete shear walls (RCSWs) by introducing stacked machine learning (Stacked ML) models based on Bayesian optimization (BO), genetic algorithm (GA), particle swarm optimization (PSO), and gradient-based optimization (GBO) algorithms. The study investigates 4-, to 15-Story RCSWs assuming different bay lengths and soil types to build a comprehensive database based on the incremental dynamic analysis (IDA) subjected to 56 near-field pulse-like and no-pulse records. Having 227,200 and 63,384 data points for a median of IDA curve (MIDA) and seismic probability curve, respectively, the proposed Stacked ML models have shown good performance on curve fitting ability by accuracy of 99.1% and 99.4% for MIDA and seismic fragility curves, respectively. In addition, the proposed models can estimate the mean annual frequency, λ, which is a key parameter in seismic risk assessment of buildings. To provide the results of the study for general buildings, a user-friendly GUI is proposed that facilitates result utilization, offering insights into seismic performance levels, providing the estimated MIDA and seismic failure probability curves, and mean annual frequency calculations for specific performance levels and seismic hazard curves.