Probabilistic Assessment Research Articles

Towards resilient pipeline infrastructure: lessons learned from failure analysis

Understanding the mechanisms of pipeline failures is crucial for identifying vulnerabilities in gas transmission pipelines and planning strategies to enhance the reliability and resilience of energy supply chains. Existing studies and the American Society of Mechanical Engineers’ (ASME) Code for Pressure Piping primarily focus on corrosion, recommending inspections every 10 years to prevent incidents due to this time-dependent threat. However, these guidelines do not provide comprehensive regulation on the likelihood of incidents due to other causes, especially non-time-dependent events (i.e. do not provide any indication of the inspection frequency or the most likely time for an incident to occur). This study adopts an innovative approach adopting machine learning, particularly artificial neural networks (ANNs), to analyse historical pipeline failure data from 1970 to 2023. By analysing records from the US Pipeline & Hazardous Materials Safety Administration, the model captures the complexity of various degradation phenomena, predicting failure years and hazard frequencies beyond corrosion. This innovative approach allows adopting more informed preventive measures and response strategies, offering deep insights into incident causes, consequences, and patterns. The results provide practical insights for maintenance planning, offering an estimation of periods when a pipeline may be more susceptible to incidents based on various factors. However, since all models inherently present uncertainties, both in the data and the modelling process, these estimates should be interpreted as probabilistic assessments. This study provides operators with a strategic framework to prescriptively address potential vulnerabilities, thereby promoting sustained operational integrity and minimising the occurrence of unexpected events throughout the service life of pipelines. By expanding the scope of risk assessment beyond corrosion, this study significantly advances the field of pipeline safety and reliability, setting a new standard for comprehensive incident prevention.

Seismic fragility analysis of nuclear containment structure with Bayesian cloud analysis framework

Nuclear containment structure plays a critical role in safe operation of nuclear power plants. Seismic safety of nuclear power plant structures has become a hot issue in nuclear engineering community following Fukushima nuclear accident. Seismic fragility analysis, as a key element in probabilistic safety assessment of nuclear power plants, is widely used in nuclear power plant structures. This study presents an innovative method for seismic fragility analysis of engineering structures. The proposed methodology combines conventional Cloud analysis method and Bayesian framework. The accuracy and efficiency of the proposed methodology were demonstrated by a single-degree-of-freedom system, heavy computational efforts but high accuracy seismic fragility analysis method was adopted for comparison. Seismic fragility analysis of a typical prestressed concrete nuclear containment structure was analyzed using the proposed method. Results indicated that method proposed in this study can improve the accuracy of seismic fragility analysis. Furthermore, with the enhance of performance level, the increasing ratios for median seismic capacity of the nuclear containment structure was decreasing.

A probabilistic assessment of ship blackout incident with Fault Tree Analysis into (FTA) Bayesian Network (BN)

Blackouts in maritime activities can cause propulsion loss and dangerous maritime conditions. Bayesian risk analysis is applied to ship blackout incidents in this study to improve understanding and reduce risks. Using Fault Tree Analysis (FTA), a Bayesian Network (BN) model incorporates fuel quality, lubricating oil quality, sensor error, injector error, and mechanical defects to estimate blackout probability. The model analyses how hazards and their interactions affect this situation using probabilistic inference. Sensitivity analysis identifies variables that affect blackout probabilities and prioritises risk mitigation solutions. Based on prior and posterior probabilities, ‘Automatic Voltage Regulator Failure’ (0.03 prior, 0.17 posterior), ‘Rotor Mechanical Fault’ (0.03 prior, 0.15 posterior), and ‘High Cooling Water Temperature’ (0.03 prior, 0.13 posterior) are the top three blackout causes. Other significant variables include ‘Switchboard Line Failure,’ ‘Faulty Fuel Pump,’ ‘Rotor Open Circuit,’ and ‘Temperature Sensor Failure’ in relative amounts. Bayesian risk analysis can identify and minimise marine blackout concerns, giving decision-makers a comprehensive framework for informed decision-making and proactive risk management. This research emphasises blackout accidents’ importance, improving maritime transportation safety and reliability.

Probabilistic Prediction and Assessment of Train-Induced Vibrations Based on Mixture Density Model

This study presents a probabilistic prediction method for train-induced vibrations by combining a deep neural network (DNN) with the mixture density model in a cascade fashion, referred to as the DNN-RMDN model in this paper. A benchmark example is conducted to demonstrate and evaluate the prediction performance of the DNN-RMDN model. Subsequently, the model is applied to a case study to investigate and compare the uncertainties of train-induced vibrations in the throat area and testing line area of a metro depot. After training, the model is capable of accurately predicting the probability density function (PDF) of train-induced vibrations at different distances from the track and at different frequencies. Utilizing the predicted PDF, probabilistic assessments can be performed to ascertain the likelihood of surpassing predefined limits. By employing a mixture density model instead of a single Gaussian distribution, the DNN-RMDN model achieves more accurate prediction of the PDF for train-induced vibrations. The proposed probabilistic assessment framework can effectively assist in vibration screening during the planning phase and in selecting and designing vibration mitigation measures of appropriate levels.

Methodology for assessing the technical condition of electrical networks based on determining the probability of their failure

RELEVANCE. The current technical condition of electrical networks at the moment is generally unsatisfactory with an average level of physical wear of about 60-70 %, therefore,the development of fundamentally new methods for assessing their technical condition is currently an urgent task. The methods of assessing the technical condition of electrical networks analyzed in the article are based to a greater extent on the subjective and insufficiently accurate method of expert assessments, which actually does not take into account the data of its technical diagnostics, as well as statistics of defects and failures. OBJECT. To develop a methodology for assessing the technical condition of electrical networks, which would be based on probabilistic models characterizing the physical processes occurring in electrical networks, as well as data from their technical diagnostics, statistics of defects and failures. METHODS. To assess the technical condition of electrical networks, a probabilistic assessment method was used. In this case, the methodology involves sequential determination of the probabilities of occurrence of selected groups of emergency modes, the probability of failure of a network element as a whole, as well as the probability of failure of an object and a group of objects of the power grid. RESULTS. Based on the research results, probabilistic models of failure of electrical networks were obtained, which, unlike the assessment methods discussed in the article, are based on data from technical diagnostics and inspections of electrical equipment. An example of the implementation of the developed methodology on real electrical network objects is given. CONCLUSION. The developed methodology allows for a more reasonable prioritization of technical impacts on electrical grid equipment, which will make it possible to replace or repair equipment that has actually exhausted its physical resource, which will reduce the frequency of emergency conditions and reduce damage from power supply interruptions.

Influence of Spatially Varied Soil Property on Train-Induced Soil-to-Column Vibration Transmission

The proximity of residential areas to traffic corridors has raised concerns among researchers regarding train-induced vibrations. Experimental studies have consistently shown variations in train-induced vibrations, which can be attributed to uncertainties and randomness in both the vibration source and propagation path. This study aims to investigate the influence factors and quantify their contributions to train-induced vibration variations through a comprehensive field measurement campaign and numerical simulations using random finite element models. During the field measurements, vibrations were recorded at the ground surface and the column base for a total of 53 passbys of the same type of metro train on parallel rail lines. Variations in train-induced vibrations caused by the source-to-receiver distance and spatially varied soil properties were analyzed and compared. Specifically, the investigation focused on the spatial variation of soil properties including elastic modulus, density and damping ratio in the topsoil and the second layer of clay, while simultaneously considering the influence of spatial variations of all three soil properties on the transmission of train-induced vibrations from the surface soil to the structural columns. Understanding these variations is crucial for the probabilistic assessment of building serviceability during train passbys. The findings of this research provide valuable insights for probabilistic prediction and evaluation of building vibration comfort.

Global Sensitivity Analysis and Surrogate Models for Evaluation of Limit States in Steel Truss Structures

This article presents the global sensitivity analysis of the serviceability limit state of a steel truss using Monte Carlo simulations. The focus is on the probabilistic assessment of deflection, with failure probability defined as the likelihood of exceeding the deflection limit. Deflection is computed using the beam finite element method. A surrogate model is introduced to reduce computational costs. By integrating the surrogate and original models, significant CPU cost reductions are achieved. Furthermore, classical Sobol sensitivity analysis is used to examine the model outputs and analyze the significance of member loading and stiffness on the deflection. This study advances the use of surrogate models in global sensitivity analysis, enhancing computational efficiency and the understanding of interactions between input variables in the reliability assessment of steel truss structures.

ELSMOR - towards European Licensing of Small Modular Reactors:Methodology recommendations for light-water small modular reactors safety assessment.

Decarbonization of energy production is key in today's societies and nuclear energy holds an essential place in this prospect. Besides heavy-duty electricity production, other industrial and communal needs could be served by integrating novel nuclear energy production systems, among which are low-power nuclear devices, like small modular reactors (SMRs). The ELSMOR (towards European Licensing of Small Modular Reactors) European project addresses this topic as an answer to the Horizon 2020 Euratom NFRP-2018-3 call. The consortium includes 15 partners from eight European countries, involving research institutes, major European nuclear companies and technical support organizations. The 3.5-year project, launched in September 2019, investigates selected safety features of light-water (LW) SMRs with focus on licensing aspects. Providing a comprehensive compliance framework that regulators can adopt and operate, the licensing process of such SMRs could be optimized, helping their deployment. In this prospect, as a result of ELSMOR's work, this article gives an overview of the specific issues that LW-SMRs may bring about in the different domains of nuclear safety, in terms of: •Methodological standpoints: safety goals, safety requirements, safety principles (defence-in-depth implementation);•Main safety functions of reactivity control, decay heat removal and confinement management;•Severe accident management;•Other safety issues particular to SMRs: use of shared systems; performing of multi-unit probabilistic safety assessment (PSA); spent fuel management, transport and disposal management. In this article, adequate methodologies are developed to deal with these issues and to help assess the safety of LW-SMRs. This work gives a precious synthesis of the safety assessment issues of LW-SMRs and of the associated methodologies developed in the context of the ELSMOR European project.

Crack Growth Analytical Model Considering the Crack Growth Resistance Parameter Due to the Unloading Process

Crack growth analysis is essential for probabilistic damage tolerance assessment of aeroengine life-limited parts. Traditional crack growth models directly establish the stress ratio–crack growth rate or crack opening stress relationship and focus less on changes in the crack tip stress field and its influence, so the resolution and accuracy of maneuvering flight load spectral analysis are limited. To improve the accuracy and convenience of analysis, a parameter considering the effect of unloading amount on crack propagation resistance is proposed, and the corresponding analytical model is established. The corresponding process for acquiring the model parameters through the constant amplitude test data of a Ti-6AL-4V compact tension specimen is presented. Six kinds of flight load spectra with inserted load pairs with different stress ratios and repetition times are tested to verify the accuracy of the proposed model. All the deviations between the proposed model and test life results are less than 10%, which demonstrates the superiority of the proposed model over the crack closure and Walker-based models in addressing relevant loading spectra. The proposed analytical model provides new insights for the safety of aeroengine life-limited parts.

Appraisal of contamination, hydrogeochemistry, and Monte Carlo simulation of health risks of groundwater in a lithium-rich ore area.

This study incorporated hydrogeochemical facies, the entropy-weighted water quality index (EWQI), multivariate statistics, and probabilistic human exposure assessment to investigate hydrogeochemistry, analyze groundwater quality, and estimate potential risks to human health in a lithium-rich ore area (Jadar River basin, Serbia). The findings designated the Ca·Mg-HCO3 hydrogeochemical type as the predominant type of groundwater, in which rock weathering and evaporation control the major ion chemistry. Due to the weathering of a lithium-rich mineral (Jadarite), the lithium content in the groundwater was very high, up to 567mg/L, with a median value of 4.3mg/L. According to the calculated EWQI, 86.4% of the samples belong to poor and extremely poor quality water for drinking. Geospatial mapping of the studied area uncovered several hotspots of severely contaminated groundwater. The risk assessment results show that groundwater contaminants pose significant non-carcinogenic and carcinogenic human health risks to residents, with most samples exceeding the allowable limits for the hazard index (HI) and the incremental lifetime cancer risk (ILCR). The ingestion exposure pathway has been identified as a critical contaminant route. Monte Carlo risk simulation made apparent that the likelihood of developing cancerous diseases is very high for both age groups. Sensitivity analysis highlighted ingestion rate and human body weight as the two most influential exposure factors on the variability of health risk assessment outcomes.

Evaluation of the unavailability of the primary circuit of Triga SSR reactor, importance factors and risk criteria for its components

Abstract Probabilistic Safety Assessment is a tool for improving a nuclear installation and its safety in all its function phases. PSA provides a methodical way of identifying sequences resulting from a wide range of initiating events concerning an accident, and includes systematic and realistic determination of accident frequencies and their consequences. This paper analyzes the unavailability of the TRIGA SSR reactor primary circuit, obtaining the system unavailability value, identifying main contributors to system failure, and calculating risk importance factors. Two cases were considered: one without considering Common Cause Failure (CCF) in the developed fault tree, and the second considering CCF on pumps and heat exchangers lines. The aim was to assess their influence on the primary system’s unavailability. Two importance factors, Fussell–Vesely and Risk Achievement Worth, were employed to identify components with a strong impact on the risk (probability of system failure). These factors aid in enhancing maintenance activities and testing. The analysis utilized the EDFT code, part of the PSAMAN code package developed in SCN-Pitesti.

Quantitative Assessments of the Liquefaction Hazard of Soils considering Possible Strong Earthquakes in Seismically Active Regions of Russia

Introduction The seismogenic liquefaction of the soil poses a great hazard to society and the environment. Therefore, it is actively studied in many countries. In Russian engineering and seismological practice, this area is not sufficiently developed. The deterministic approach still prevails in Russian research on this topic. More modern probabilistic estimates are very rare. Methods This paper describes examples of both deterministic and probabilistic assessments of the seismogenic liquefaction hazard performed in certain areas of the Russian territory with different seismogeological conditions. Deterministic estimates were made using the Iwasaki-Seed-Finn methods and their modifications. Probability distribution functions of a random variable, the “seismic potential of liquefaction” (SPL), were developed for probabilistic estimates. These functions are regional in nature and take into account two types of uncertainties. The first is the uncertainty in achieving “critical” values by the SPL value in the event of potentially dangerous earthquake sources for a given location. The second is the uncertainty in the very occurrence of these sources in a given place for a given period of time. The “critical” SPL values are determined by the strength properties of the site soils. All estimates are based on multivariate calculations using various models of strong ground motions and seismicity. In all cases, the probability of liquefaction of water-saturated sandy and sandy-loam deposits was estimated which were found near the seabed and at depths of up to 80 m in the waters of Pogibi cape (the coast of Sakhalin island), in the districts of Sochi and Novorossiysk, as well as in land conditions (Stavropol, Krasnodar). Results The results of the research made it possible to correctly (at the quantitative level) take into account this component of the seismic hazard of the studied territories. Conclusion The variants of practical use of the obtained data are offered. An assessment of the possibilities and limitations of the developed methodology is made, and ways to improve it are outlined.

A cGAN-based fatigue life prediction of 316 austenitic stainless steel in high-temperature and high-pressure water environments

The thermo-mechanical-chemical coupling effect presents significant challenges in accurately predicting the fatigue life of 316 austenitic stainless steel in high-temperature and high-pressure water environments (referred to hereafter as environmental fatigue). The complexity of environmental fatigue experiments results in limited and dispersed data, further making the life prediction difficult. Traditional fatigue life prediction models are often constrained by specific loading conditions and do not adequately account for the complex environmental influences. To address these issues, this paper proposes a novel environmental fatigue life prediction model of 316 stainless steel utilizing conditional Generative Adversarial Networks. The proposed model incorporates critical environmental factors, loading conditions and stacking fault energy, allowing direct prediction of environmental fatigue life. A comparative analysis on the predicted and experimental results reveals that the cGAN-based model significantly improves the prediction accuracy, reducing the fatigue life prediction error from a factor of 5 to within 3. To quantify the uncertainty in fatigue life prediction, the Monte Carlo Dropout method is employed to enable a probabilistic assessment of fatigue life. Furthermore, four environmental and loading conditions are established to evaluate the model’s extrapolation capability. The results demonstrate that the probabilistic fatigue assessment effectively captures data distribution and achieves high prediction accuracy.

A probabilistic neural network assessment method for insulator pollution level based on discharge noise

The insulators of a transmission line are exposed to air for a long time, a mount of dust particles will be deposited on the surface. When there rain, fog or high humidity, the surface of the insulator will discharge or even flashover to breakdown. For this, in this paper, the characteristic parameters of insulator discharge noise under different pollution levels, ambient temperature and relative humidity are extracted, and the influence of insulator discharge noise are analyzed respectively. Probabilistic neural network (PNN) model training samples are used to propose an insulator pollution level assessment method based on discharge noise and verify it. The results show that the pollution level has obvious influence on the characteristic parameters of insulator discharge noise. The higher the pollution level, the more obvious the discharge phenomenon and the greater the discharge intensity. When the relative humidity ranges from 70 % to 90 %, the greater the relative humidity, the greater the pollution discharge intensity of the insulator. The energy of the noise signal gradually concentrated in the 5.5 kHz to 8.3 kHz frequency band, and the pollution discharge in the 5.5 kHz to 16.5 kHz frequency band reflected in the low-frequency part of the information increased. Ambient temperature has no obvious effect on insulator discharge noise. The precision rate of the assessment method for pollution levels Ⅰ, Ⅱ, Ⅲ and Ⅳ are 91.5 %, 88.2 %, 86.5 % and 94.0 %, respectively.

A Stochastic Model for Induced Seismicity at the Geothermal Systems: A Case of the Geysers

Abstract Induced seismicity has emerged as a source of a significant earthquake hazard associated with recent development of unconventional energy resources. Therefore, it is imperative to develop stochastic models that can accurately describe the observed seismicity rate and forecast its evolution. In this study, a mechanism suggested by linear response theory is incorporated into a stochastic earthquake model to account for changes in the seismicity rate. It is derived that the induced rate can be modeled as a convolution of the forcing, related to fluid injection operations, and a specific response kernel. The model is incorporated into a Bayesian framework to compute the probabilities for the occurrence of the largest expected events during future time intervals. The applicability of the model is illustrated by analyzing the injection and seismicity data at the Geysers geothermal field in California. The suggested approach provides further insight into the probabilistic assessment of earthquake hazard associated with fluid injection operations. It also can be used for probing the rheological properties of the subsurface by analyzing the inherent characteristic timescales associated with the subsurface response to external forcing.

Probabilistic framework for strain-based fatigue life prediction and uncertainty quantification using interpretable machine learning

Establishing a unified fatigue life prediction model and quantifying the uncertainty in the mechanical behavior of materials are critical to ensure the structural integrity and equipment performance. For the commonly-used strain-based fatigue methods, existing estimation methods exhibit inevitable deviations, while data-driven methods have shown poor extrapolation ability and interpretability. Therefore, this paper aims to develop a probabilistic framework for strain-based fatigue life prediction and uncertainty quantification (UQ) to provide an indication for fatigue design/assessment using interpretable machine learning (ML) techniques. Based on Shapley additive explanations (SHAP) and symbolic regression (SR), interpretable prediction models with concise expressions and outstanding prediction performance are established and optimized according to the priori physical knowledge. Moreover, accounting for the material variability, the probabilistic assessment with UQ excellently validates the prediction model, and quantifies the variability of ε-N curves. The proposed framework provides valuable reference and shows promising prospects in fatigue design for engineering components.

COFUN: A New Tool for Evaluating Containment Failure Frequency and Uncertainty

The feature of the COntainment Failure probability and Uncertainty aNalysis program (COFUN) is the quantification of the containment state. The probability factors used in probabilistic safety assessment (PSA) and the resulting outputs contain probability bands, so it is necessary to present the uncertainty bands as results. The uncertainty analysis process of the level 2 PSA is that a level 1 PSA uncertainty propagates to a level 2 PSA uncertainty. The basic event data including probability distribution were used for the level 1 PSA uncertainty analysis and probability distribution and priority of the fractional branch point were used for uncertainty analysis in a level 2 PSA. Once the input is ready, uncertainty analysis is performed via Monte-Carlo sampling. Finally, the uncertainty analysis can be performed for source term category (STC) and large early release frequency (LERF). One of the outstanding characteristics of the COFUN is to perform level 2 PSA for multi-unit accidents. The one of formats of multi-unit level 2 PSA is the combination of STCs but the combinations could be too many to acquire insight by STC combination itself. The COFUN provides a user-defined combination that makes STC combinations classified and enables the derivation of multi-unit accident LERF result.

Probabilistic seismic hazard assessment of Delhi (National Capital Region) and its adjoining region, India

Probabilistic seismic hazard assessment of Delhi (National Capital Region) and its adjoining region, India

Probabilistic Health Risk Assessment of Primary Aromatic Amines in Polyamide Cooking Utensils in China by Monte Carlo simulation

The migration of primary aromatic amines (PAAs) from food contact materials raises significant public health concerns. In this study, the migration levels of 26 PAAs were analyzed in 242 nylon cooking utensils using ultra-performance liquid chromatography-tandem mass spectrometry. A total of 18 PAAs were detected, of which 14 were quantified, with 4,4′-diaminodiphenylmethane (4,4′-MDA) and aniline being the most prevalent ones. The compliance rates for nylon kitchenware were similar under both legislation of European Commission (76.9%) and Chinese legislation (77.9%). Probabilistic non-carcinogenic and carcinogenic risk assessment were conducted using Monte Carlo simulation, with read-across approach applied to fill the gap of toxicity data. The hazard quotients for 18 PAAs were calculated, revealing that 17 PAAs (excluding 4,4′-MDA) had acceptable hazard quotients (<1). Lifetime cancer risks for 17 PAAs were determined, with 15 PAAs (excluding benzidine and 4,4′-MDA) showing acceptable cancer risks (<10−4). The study suggests that the non-carcinogenic and carcinogenic health risks posed by PAAs migrating from FCMs can be effectively mitigated by promptly identifying non-compliant products and reducing exposure to high-risk PAAs such as 4,4′-MDA and benzidine. Enhancing the understanding of PAA hazard characterization and implementing measures to minimize health risks associated with PAA migration from FCMs is hence recommended.

Probabilistic landslide hazard assessments: adaptation of spatial models to large slow-moving earth flows and preliminary evaluation in Loja (Ecuador)

Probabilistic landslide hazard assessments: adaptation of spatial models to large slow-moving earth flows and preliminary evaluation in Loja (Ecuador)