Failure Probability Assessment Research Articles

Task reliability index for operator performance and failure probability assessment in control room simulators

Main control room simulators are an important information source for improving the empirical basis of human reliability analysis (HRA). Both quantitative and qualitative observations provide valuable insight on the operators’ ability to successfully perform their tasks. However, relevant portions of this evidence are not used in typical approaches for estimation of human error probabilities (HEP), which are based on failure counts. With small sample sizes and the operator performance levels representative for nuclear power plants, very little information enters the probability estimates. This paper presents a novel approach to formalize the collection and use of reliability-relevant evidence from simulators. The methodology treats task reliability with the plant-centred orientation characteristic of the systems or Probabilistic Safety Assessment (PSA) perspective as well as a human-centred view of task performance based in human factors engineering. These are combined to form a Task reliability index (TRI) able to distinguish finer performance variations and consider indications of potential failures. Bayesian updating of HEP distributions is suggested by translating the TRI data into pseudo-failures and trials, preserving the convenient coupling of Beta-Binomial distributions in the Bayesian analysis. The approach is demonstrated using data from the International HRA Empirical Study and is shown to produce results that are comparable to those obtained in the empirical study, which were based on comprehensive expert analyses.

An active learning approach for reliability assessment of passive systems combining polynomial chaos expansion and adaptive sampling

Passive safety system is extensively employed in newly designed reactors to increase their inherent safety. However, the driving force is smaller, and the uncertainty of the thermal–hydraulic (T-H) process may potentially result in the inability of system to perform its intended function, which is often known as functional failure. Therefore, the passive systems reliability has received increasing attention. Unfortunately, the assessment of system failure probability utilizing Monte Carlo simulation (MCS) methods necessitates a significant quantity of repeated calculations employing the thermal–hydraulic code, which may be impractical in terms of computational cost. To enhance the assessment calculation efficiency, an adaptive surrogate model is proposed. The bootstrap method was employed to introduce an error estimate into the polynomial chaos expansion (PCE) model, which solves the obstacle of the PCE model in reliability evaluation applications due to the lack of error estimation. The method constructs an active learning approach through error estimation, strategically identifies and selects the best candidate samples, and iteratively updates the initial experimental design. The effectiveness of the method was verified through three benchmark cases, and it was employed for assessing the passive residual heat removal system (PRHRS) reliability within integral-type pressurized water reactor (IPWR200). The results demonstrated that the proposed method significantly diminishes the frequency of invocations to the T-H code and improves computational efficiency to a large extent. Furthermore, it can serve as a valuable guide for the design, operation, and reliability evaluation of the passive system.

Fracture mechanics approach to TRISO fuel particle failure analysis

Weibull stress-based methods for failure probability assessment have been developed and analyzed to assess the integrity of tristructural isotropic (TRISO) fuel particles during fuel life cycles and accident operating conditions. While simple, these methods entail a number of drawbacks when stress concentrates near crack tips, including finite element mesh size dependency when the Weibull stress is averaged over the finite element domain. Fracture mechanics approaches involving the use of interaction integrals eliminate this lack of mesh convergence and produce consistent fracture predictions. In this work, we use an interaction integral approach to computing stress intensity factors for a crack in the inner pyrolytic carbon layer perpendicular to the silicon carbide layer, which is simplified representation of a failure mode in TRISO particles. The interface between these two TRISO layers has been shown to become porous, which we simulate by considering a transition of mechanical properties over such porous length, i.e. the layers are modeled as a functionally graded material. Aspects such as porosity and thermal and irradiation eigenstrains are considered in computing the stress intensity factor from a fracture mechanics approach and compared with the known Weibull stress failure approach. The methodology introduced in this paper enables a more general fracture probability assessment in TRISO particles and eliminates the need to identify best suited parameters when using local or averaged stress-based failure criterion. Finally, the numerical sensitivity studies show how parameters such as the porous transition zone length, the material stiffness, and creep affect the probability of TRISO fuel particle failure.

Data-driven probabilistic failure assessment curve based on similitude principle

In the study, based on the similarity in crack-tip fields between pipeline structure and standardized single-edge notched tension (SE(T)) test specimen, a methodology using a data-driven machine learning technique is proposed to determine the specific failure assessment curves for full-scale pipeline girth welds. By considering constraint similitude and ductile tearing, the probabilistic failure assessment line obtained from SE(T) resistance curves with a 50% survival rate can provide the most accurate failure assessment, as validated using the experimental full-scale pipeline data in the literature, particularly for the zone dominated by ductile fracture. Moreover, Option 1 (in R6 terminology) fracture assessment curve of British Energy R6 approach, which corresponds to a 15% survival rate, is proven to be overly-conservative.

A joint multi-model machine learning prediction approach based on confidence for ship stability

Since the traditional ship stability failure probability assessment method has many input parameters and cumbersome intermediate calculation process, this paper proposes a joint multi-model machine learning prediction method based on confidence. The method calculates the confidence of each machine learning model for the current prediction result, selects the top n models among them, and takes the average of their prediction results as the output. The confidence is calculated by a method inspired by semi-supervised learning. To reduce the number of ship features that need to be input for assessment, a sensitivity analysis is used to reject irrelevant features. Eight machine learning models that have good performance and are widely used in other fields, including Radial Basis Function Neural Network, Random Forest, eXtreme Gradient Boosting, and so on, are used as component learners in this study. The hyperparameters of each model are obtained by cross-validation and grid search. The advancedness of the proposed method is verified by comparing it with other models on a small self-built ship stability failure probability dataset. By conducting experiments that simply average the results of the component learners, it is confirmed that simple superposition different models does not necessarily improve the accuracy. At the same time, after pre-processing the input features in different ways, the comparison of the prediction performance was conducted, and the experimental results showed that the proposed method is not affected by the way the input features are preprocessed and therefore has some robustness.

Bayesian update of fragility curves for equipment failure probability in seismic probabilistic safety assessment in nuclear power plant

The assessment of equipment failure probability during seismic probabilistic safety assessment (PSA) is a crucial and fundamental component in nuclear power plant seismic PSA. A Monte Carlo program must be formulated to compute the equipment failure probability and uncertainty analysis in seismic PSA. A numerical calculation method is employed for the integration calculation to obtain the conditional failure probability and the uncertainty distribution for each equipment. The fragility parameters of the equipment and the hazard data of the plant site serve as the input parameters for the failure probability of equipment in seismic PSA. In this study, the probability of occurrence of the minimal cut set in equipment failure under earthquake conditions is calculated using the Monte Carlo sampling method, and the uncertainty distribution for each peak ground acceleration interval is presented. Furthermore, we have updated the fragility parameters of the equipment through the utilization of the Bayesian approach. Based on the updated fragility curves, we have recalculated the failure probability of the equipment and compared it with the results before the update.

The application of OREDA (offshore reliability data) in developing valve criticality analysis criteria

In oil and gas processing plants, extensive networks of hundreds to thousands of valves are vital in controlling the entire process. However, not all valves have the same importance in process control. Therefore, identifying and prioritizing critical valves is essential for efficient and safe operation. Assessing the importance of valves requires a risk-based approach. However, quantifying the associated risks becomes challenging, especially when limited field data makes it difficult to model the probability of valve failure. One possible solution is to use Offshore Reliability Data (OREDA) as a valuable failure probability data source. OREDA provides valuable insight into historical failure and performance data related to oil and gas equipment such as valves. To go one step further, the new proposal presented in this paper includes implementing a modified OREDA approach that considers the cumulative operating time of the valve. By considering cumulative operating hours, a more comprehensive and accurate assessment of failure probability can be achieved, enabling more informed decision-making regarding valve criticality and prioritization of maintenance.

Failure Probability and Economic Loss Assessment of a High-Rise Frame Structure under Synthetic Multi-Dimensional Long-Period Ground Motions

Multiple research studies and seismic data analyses have shown that multi-directional long-period ground motion affects crucial and intricate large-scale structures like oil storage containers, long-span bridges, and high-rise buildings. Seismic damage data show a 3–55% chance of long-period ground motion. To clarify, the chance of occurrence is 3% in hard soil and 83% in soft soil. Due of the above characteristics, the aseismic engineering field requires a realistic stochastic model that accounts for long-period multi-directional ground motion. A weighted average seismic amplification coefficient selected NGA database multi-directional long-period ground motion recordings for this study. Due to the significant low-frequency component in the long-period ground motion, this research uses empirical mode decomposition (EMD) to efficiently decompose it into a composite structure with high- and low-frequency components. Given the above, further investigation is needed on the evolutionary power spectrum density (EPSD) functions of high- and low-frequency components. Analyzing the recorded data will reveal these functions and their corresponding parameters. Proper orthogonal decomposition (POD) is needed to simulate samples of high- and low-frequency components in different directions. These samples can be combined to illustrate multi-directional long-period ground motion. Representative samples exhibit the seismic characteristics of long-period multi-directional ground motion, as shown by numerical examples. This proves the method’s engineering accuracy and usefulness. Moreover, this study used incremental dynamic analysis (IDA) to apply seismic vulnerability theory. This study investigated whether long-period ground motions in both x and multi-directional directions could enhance the seismic response of a high-rise frame structure. By using this method, a comprehensive seismic economic loss rate curve was created, making economic loss assessment clearer. This study shows that multi-directional impacts should be included when studying seismic events and calculating structure economic damages.

Dynamic Domino Effect Assessment (D2EA) in tank farms using a machine learning-based approach

The current work presents a Dynamic Domino Effect Assessment (D2EA) methodology for chemical storage tank farms. While the application of the proposed approach is focused on atmospheric tanks, it applies as well to pressurized tanks. It utilizes the temperature and time-dependent material strength property (yield strength) as a structural health indicator. The D2EA methodology uses random forest (RF) and feed-forward neural network (FFNN) to predict yield strength during fire exposure and applies the predicted yield strength to dynamic failure assessment. A lumped parameter model generates datasets to train the dynamic failure prediction model. Two case studies have been used to demonstrate how the method can be used. The results suggest that RF and FFNN can predict gamma distribution-aided dynamic failure probability assessment. The RF is a better tool than FFNN due to its lower computational cost and good performance. The current work will be helpful in the design and operation of tank farms – an essential and critical infrastructure.

Time-dependent reliability of corroded mild steel pipes by different failure modes

This study aims to analytically assess the time-dependent failure of corrosion-induced mild steel pipes by employing two fracture failure criteria: the fracture toughness-based criterion and the stress-based criterion. The investigation intends to identify the influential factors that impinge upon the assessment of failure probability within this context. It is found that there is a linear relationship between the ratio of wall thickness to inner radius and the probability of failure and that between the internal pressure and the probability of failure. Notably, the influence on the evaluation of failure probability by the ratio of wall thickness to inner radius is more prominent than the internal pressure. It is also found that a comprehensive criterion is necessary for evaluating the fracture resistance of corroded mild steel pipes, which considers both initial fracture toughness and ultimate stress. These findings can provide theoretical evidence for pipe engineers to develop maintenance or repair strategies in mild steel pipes. The significance of this paper is the development of an analytical framework for predicting the probability of failure of corroded mild steel pipes, considering the complexities of elastic-plastic fracture mechanics.

Numerical Studies on Seismic Response of Structural Systems Using a Response-Spectra Based Intensity Measure

This paper proposes a novel intensity measure (IM) based on the geometric mean of acceleration response spectral ordinates to assess the probabilistic performance of structures subjected to seismic loading. Instead of relying solely on the fundamental period, the proposed IM is evaluated across a fixed period range for all structural systems, which allows consideration of higher mode effects and period changes due to nonlinearity. The proposed seismic IM is evaluated using two established indices sufficiency and efficiency. Sufficiency quantifies the independence of an engineering demand parameter at a specific intensity level with ground motion characteristics such as seismic magnitude [Formula: see text] and distance from site to fault plane [Formula: see text]. It is calculated by linear regression analysis, or using gradient-based relative sufficiency measures. On the other hand, efficiency is measured as dispersion across ground motions at a given intensity level for any physical response quantity. It helps to reduce computational demand for failure probability assessment by considering a smaller number of records compared to an inefficient IM for similar confidence levels. The effectiveness of the proposed IM along with 10 other IMs is demonstrated on single degree of freedom systems with various fundamental periods by performing nonlinear time history analysis using a far-field ground motion record set. The study is also extended to five degree of freedom lumped mass stick models, 2D models (4-, 8-, and 12-story archetype steel frames), and 3D reinforced concrete shear wall building model. The results indicate that the proposed IM limits dispersion to within 10% for long-time period structures, and demonstrates improved sufficiency across different structural systems. For example, gradient of the proposed IM with respect to magnitude [Formula: see text] and site-to-source distance [Formula: see text] for a 12-story steel frame is reduced by 42.9% and 94%, respectively, compared to spectral acceleration at fundamental time period. Potential application of this research lies in efficiently conducting seismic reliability assessment and design for structural systems.

Efficient slope reliability analysis under soil spatial variability using maximum entropy distribution with fractional moments

Spatial variability of soil properties imposes a challenge for practical analysis and design in geotechnical engineering. The latter is particularly true for slope stability assessment, where the effects of uncertainty are synthesized in the so-called probability of failure. This probability quantifies the reliability of a slope and its numerical calculation is usually quite involved from a numerical viewpoint. In view of this issue, this paper proposes an approach for failure probability assessment based on Latinized partially stratified sampling and maximum entropy distribution with fractional moments. The spatial variability of geotechnical properties is represented by means of random fields and the Karhunen-Loève expansion. Then, failure probabilities are estimated employing maximum entropy distribution with fractional moments. The application of the proposed approach is examined with two examples: a case study of an undrained slope and a case study of a slope with cross-correlated random fields of strength parameters under a drained slope. The results show that the proposed approach has excellent accuracy and high efficiency, and it can be applied straightforwardly to similar geotechnical engineering problems.

Risk analysis and Assessment of Wellbore Integrity Failure in Underground Gas Storage Based on Bayesian Network

Well integrity assessment plays vital role in the risk management of gas storage wells, which has emerged as a key topic of political and scientific discussion in the gas industry. However, the current research did not provide quantized assessment of failure probability of well integrity. To quantitatively evaluate the failure risk of gas storage well integrity, the probability model based on Bayesian network is established and calculated incorporating the relevant historical statistical data of gas storage well integrity failure and Leaky Noise or Gate extended model. Besides, the risk priority evaluation criteria of well integrity are set up, combining with the analytic hierarchy process. And the risk judgment matrix is constructed to quantitatively evaluate the risk of wellbore integrity failure in gas storage. Practical guidelines and solutions derived from the results of the current study can be used for the design of the gas storage wellbores.

Leakage failure probability assessment of submarine pipelines using a novel pythagorean fuzzy bayesian network methodology

Obtaining precise and adequate failure data can be challenging in the probabilistic risk assessment of process industries, like submarine pipelines. This study proposes a novel approach, the Pythagorean fuzzy Bayesian network methodology, to tackle the challenge of obtaining prior data in the assessment of leakage risk in submarine pipelines. First, the qualitative evaluation of experts is converted by Pythagorean fuzzy sets. Next, the enhanced Pythagorean trapezoidal fuzzy Einstein hybrid geometric operator is integrated with the subject-objective weighting approach to consolidate the expert opinions in order to obtain prior probabilities. Following this, the leaky Noisy-OR gate in the Bayesian network is utilized to access the conditional probabilities, which depict the uncertain logical connection of events. Ultimately, the Bayesian network is utilized for inference and analysis to anticipate the probability of system failure and detect any vulnerabilities. Furthermore, a case study is performed to demonstrate the practicality of the approach. The reliability of the methodology is verified by results comparison and analysis. The suggested approach and evaluation findings can offer valuable guidance for the safety supervision of the submarine pipeline network.

A failure probability assessment method for train derailments in railway yards based on IFFTA and NGBN

Derailment is one of the main hazards during train passes through railway turnouts (RTs) in classification yards. The complexity of the train-turnout system (TTS) and unfavorable operating conditions frequently cause freight wagons to derail at RTs. Secondary damages such as hazardous material spillage and train collisions can result in loss of life and property. Therefore, the primary goal is to assess the derailment risk and identify the root causes when trains pass through RTs in classification yards. To address this problem, this paper proposes a failure probability assessment approach that integrates intuitionistic fuzzy fault tree analysis (IFFTA) and Noisy or gate Bayesian network (NGBN) for quantifying the derailment risk at RTs. This method can handle the fact that the available information on the components of the TTS is imprecise, incomplete, and vague. The proposed methodology was tested through data analysis at Taiyuan North classification yard in China. The results demonstrate that the method can efficiently evaluate the derailment risk and identify key risk factors. To reduce the derailment risk at RTs and prevent secondary damage and injuries, measures such as optimizing turnout alignment, controlling impact between wagons, lubricating the rails, and regularly inspecting the turnout geometries can be implemented. By developing a risk-based model, this study connects theory with practice and provides insights that can help railway authorities better understand the impact of poor TTS conditions on train safety in classification yards.

Reliability Analysis of the Slope Stability of Homogeneous Earth Dam under Seismic Loading

Earth dams are widespread throughout the world and their safety is the overriding concern of civil engineers, especially in regions with high seismic activity. This contribution presents a deterministic and reliability-based approach of the slope stability analysis of homogeneous earth dam under seismic loading. The hydrostatic loading as well as the saturation line in the dam body is considered for different design situations to investigate the effects of water level fluctuations on earth dam slope stability. Seismic loading is introduced by horizontal and vertical pseudo-static forces, while the considered failure mode is circular slip, analyzed with the classical method of slices. Given the nature of the soils, uncertainties about their properties must be considered in the assessment of the slope stability. These uncertainties can be quantified by a reliability analysis. The proposed reliability method is approached by the failure probability assessment of the slope stability of an earth dam based on Monte-Carlo simulation. The used random variables are the mechanical shear properties of the embankment body as well as the acceleration coefficient of the seismic zone. For the failure probability evaluation, the log-normal distribution law was used to generate the random variables. A computer program was developed using Matlab®. The developed approach was applied, for a practical example, to analyze the slope stability of an earthen dam located in Médéa (Algeria), whose dimensions are realistic. The results obtained with the developed analytical model were compared with those obtained with the Flac2D© software. The confrontation turned out to be satisfying.

Reliability assessment of pipelines crossing strike-slip faults considering modeling uncertainties using ANN models

Buried pipelines are crucial infrastructure that sometimes suffers from the adverse effects of ground movements resulting from the earthquake. A pipe crossing strike-slip fault can experience high tensile or compressive strains due to fault displacement during the earthquake. Various analytical and numerical methods are available in the literature to calculate the pipe wall strain through modeling the pipe-soil interaction. These methods are often too complex and not suitable for performing extensive parametric studies. In this research, Artificial Neural Network (ANN) based methods were developed for calculating the peak tensile and peak compressive strains. Due to the lack of field data, finite element analysis was performed to investigate the complex behavior of pipe under the fault movements and generate a database to develop the ANN models. The study revealed the weakness of ANN models that although a very high coefficient of determination (i.e., R2 ≈ 1) and low error can be obtained, the strains predicted using these models can significantly be different from the actual strains due to model uncertainty. To this end, the random strains were predicted by adding the calculated strains with the randomized model errors. Using the random strains, calculating the probability of exceedance of strains was proposed. Finally, a framework is developed for failure probability assessment of pipelines subjected to fault movements accounting to the uncertainty of ANN models.

Analytical and Computational Modeling for Multi-Degree of Freedom Systems: Estimating the Likelihood of an FOWT Structural Failure

Wind turbines and their associated parts are subjected to cyclical loads, such as bending, torque, longitudinal stresses, and twisting moments. The novel spatiotemporal reliability technique described in this research is especially useful for high-dimensional structural systems that are either measured or numerically simulated during representative observational time span. As this study demonstrates, it is possible to predict risks of dynamic system failure or damage given the in situ environmental load pattern. As an engineering example for this reliability, the authors have chosen 10-MW floating wind turbines and their dynamic responses, under environmental loadings, caused by wind and waves. The aim of this study was to benchmark a state-of-the-art approach suitable for the reliable study of offshore wind turbines. Existing reliability methods do not easily cope with dynamic system high dimensionality. The advocated reliability technique enables accurate and efficient assessment of dynamic system failure probability, accounting for system nonlinearities and high dimensionality as well as cross-correlations between different system components.

Optimization the reliability of emergency safety barriers based on the subjective safety analysis and evidential reasoning theory. Case study

PurposeThis study aims to improve the reliability of emergency safety barriers by using the subjective safety analysis based on evidential reasoning theory in order to develop on a framework for optimizing the reliability of emergency safety barriers.Design/methodology/approachThe emergency event tree analysis is combined with an interval type-2 fuzzy-set and analytic hierarchy process (AHP) method. In order to the quantitative data is not available, this study based on interval type2 fuzzy set theory, trapezoidal fuzzy numbers describe the expert's imprecise uncertainty about the fuzzy failure probability of emergency safety barriers related to the liquefied petroleum gas storage prevent. Fuzzy fault tree analysis and fuzzy ordered weighted average aggregation are used to address uncertainties in emergency safety barrier reliability assessment. In addition, a critical analysis and some corrective actions are suggested to identify weak points in emergency safety barriers. Therefore, a framework decisions are proposed to optimize and improve safety barrier reliability. Decision-making in this framework uses evidential reasoning theory to identify corrective actions that can optimize reliability based on subjective safety analysis.FindingsA real case study of a liquefied petroleum gas storage in Algeria is presented to demonstrate the effectiveness of the proposed methodology. The results show that the proposed methodology provides the possibility to evaluate the values of the fuzzy failure probability of emergency safety barriers. In addition, the fuzzy failure probabilities using the fuzzy type-2 AHP method are the most reliable and accurate. As a result, the improved fault tree analysis can estimate uncertain expert opinion weights, identify and evaluate failure probability values for critical basic event. Therefore, suggestions for corrective measures to reduce the failure probability of the fire-fighting system are provided. The obtained results show that of the ten proposed corrective actions, the corrective action “use of periodic maintenance tests” prioritizes reliability, optimization and improvement of safety procedures.Research limitations/implicationsThis study helps to determine the safest and most reliable corrective measures to improve the reliability of safety barriers. In addition, it also helps to protect people inside and outside the company from all kinds of major industrial accidents. Among the limitations of this study is that the cost of corrective actions is not taken into account.Originality/valueOur contribution is to propose an integrated approach that uses interval type-2 fuzzy sets and AHP method and emergency event tree analysis to handle uncertainty in the failure probability assessment of emergency safety barriers. In addition, the integration of fault tree analysis and fuzzy ordered averaging aggregation helps to improve the reliability of the fire-fighting system and optimize the corrective actions that can improve the safety practices in liquefied petroleum gas storage tanks.

Failure probability assessment of emergency safety barriers integrating an extension of event tree analysis and Fuzzy type‐2 analytic hierarchy process

AbstractLiquefied petroleum gas (LPG) storage fires and explosions occur due to uncontrolled gas leaks and the gradual breakdown of associated safety barriers. By installing an effective safety barrier, these accidents can be greatly reduced. However, this study assesses the probability of failure of emergency safety barriers (ESBs) to help decision makers understand how they can support decisions to reduce the risks associated with LPG storage. In this context, an extension of the event tree analysis is proposed named emergency event tree analysis (EETA). The aim of this paper is to develop an integrated approach that uses interval type‐2 fuzzy sets and Analytic Hierarchy Process (AHP) method and emergency event tree analysis to handle uncertainty in the failure probability assessment of emergency safety barriers (ESBs). In addition, a case study on the failure probability assessment of the emergency safety barriers of the LPG plant in Algeria based on the proposed methodology is provided and carried out to illustrate its effectiveness and feasibility. The results demonstrated the ability of interval type‐2 fuzzy sets and the AHP method to provide highly reliable results and to evaluate the failure probability of emergency safety barriers in emergencies situations. However, the classical event tree analysis (CETA) does not take into account the possibility of assessing the emergency consequences of different accident scenarios. Consequently, it only allows you to estimate the occurrence probability of accident scenarios. The results of this study show that the value of the probability of failure of the emergency safety barriers can be used to estimate the probability of occurrence of emergency consequences under different accident scenarios, improved the reliability and help prioritize emergency improvement measures. The study provides scientific and operational references for analyzing emergency consequences of the various accident scenarios in all fields such as petrochemical, maritime industry, and health occupational.