Evaluation Of Failure Probability Research Articles

Reappraisal of reliability of a slope through hybridisation of regional and site-specific soil shear strength information

Uncertainty in soil parameters is usually characterised by probability density functions (PDFs), with the influence on system performance represented through the probability of failure. Difficulties in selecting representative PDFs for a project often arise from scarcity of site-specific information, even with ample previous knowledge and test data of similar soil types in the region. This paper proposes an approach to rationally assimilate regional and site-specific information. A newly-compiled regional database of shear strength information for saprolitic soils in Hong Kong is presented, based on results of multi-stage consolidated-undrained triaxial tests. A hierarchical Bayesian model is fitted to the regional database, followed by a Bayesian updating model that produces posterior predictive distributions of shear strength parameters. The posterior estimates incorporate site-specific features into regional information, leading to profound impacts on the evaluation of failure probability for a slope case. To further illustrate the significance of data hybridisation, four semi-hypothetical scenarios are created using the same slope geometry, by assuming that distributions of shear strength parameters are completely known at the site. With the proposed approach, the estimated failure probability approaches the true value with increasing amount of site-specific data, and is more robust than adopting regional data or site data alone.

Vibration test and fatigue test for failure probability evaluation method with integrated energy

The seismic evaluation of key components such as reactor vessel is important for the Seismic Probabilistic Risk Assessment (S-PRA) in a Sodium-Cooled Fast Reactor (SFR). Many components were fractured by integrated damage like fatigue damage during seismic ground motion. In this paper, failure probability evaluation method with integrated energy was developed by comparing the energy with vibration tests and fatigue tests. Vibration tests were performed to evaluate integrated vibration energy at failure by energy balance equation, and fatigue tests were performed to evaluate integrated vibration energy at failure based on experimental results. As results, it is shown that integrated energy at failure time by vibration tests were estimated and its values were in range the energy based on results of fatigue tests.

Dynamic probability modeling-based inspection intervals optimization for civil aircraft composite structures using Bayesian updating

The evaluation of damage tolerance in composite materials is essential for ensuring the safety of aircraft structures. One of the most challenging aspects of applying probability modeling-based methods to evaluate damage tolerance is determining the actual damage size distributions for in-service aircraft structures. Although existing nondeterministic approaches have been used to optimize inspection intervals of composite structures, few studies have investigated the effects of updates on the actual damage size distribution and its impact on both the probability of structural failure and inspection intervals. This paper proposes a dynamic optimization method for inspection intervals of composite structures based on Bayesian updating. The damage size distribution of the composite structure is characterized by a general stochastic distribution. A Bayesian updating methodology is presented to iteratively update the actual damage size distribution whenever new data becomes available. Based on the constructed probability model, the inspection intervals of composite structures are determined under the objectives of optimal safety and economy for civil aircraft using a Monte Carlo approach. Compared to prior distribution models, the proposed method achieves higher safety for structures during a single inspection, reduces the failure probability of structures throughout their entire service life, and incurs lower maintenance costs. It also enables maintenance personnel to flexibly adjust inspection intervals while facilitating quantitative evaluation of both failure probabilities and maintenance costs associated with these intervals. These findings suggest that the proposed method holds great potential in enabling maintenance personnel to make informed decisions regarding inspection intervals for improved safety and economic performance.

Fragility Analysis of a Transmission Tower-Line System Subjected to Wind and Ice Loads Considering Fatigue Damage

Disasters such as ice and wind can pose a serious threat to the normal operation of power transmission lines. Wind-induced fatigue damage can further reduce the load-carrying capacity of transmission towers, increasing their fragility to ice and wind disasters. To assess the comprehensive capability of transmission towers to resist extreme ice and wind disasters, this paper proposes a failure probability evaluation framework for transmission towers considering wind-induced fatigue damage under the coupled effect of ice and wind. Taking a certain transmission line in Hunan as an example, the failure probability caused by ice and wind disasters is calculated for different years of service. First, based on the historical meteorological data in three cities in Hunan, a joint probability model of wind speed and wind direction considering their correlation is established using the copula function. Then, based on this probability model, the wind-induced fatigue damage of transmission towers is calculated using the Miner linear damage theory and the S-N curve. Subsequently, the fragility of the tower under the coupled load of ice and wind is calculated for different years of service. Finally, by combining the structural fragility function with the joint probability distribution model of ice thickness and wind speed, the collapse probability of the transmission tower under the action of ice and wind disasters is calculated. The results indicate that the influence of wind-induced fatigue damage cannot be ignored when transmission towers encounter ice and wind disasters. With increasing service time, the ability of transmission towers to resist ice and wind disasters gradually decreases, and the failure probability also increases under extreme ice and wind conditions.

Stability analysis of a loess landslide considering rainfall patterns and spatial variability of soil

Accurate analysis of the stability of rainfall-induced landslides is an important and urgent task. Under complex geological conditions, traditional stability analysis methods cannot meet stability evaluation requirements. To study the impact of the rainfall pattern on the probability of instability of loess slopes, the intensity-frequency-duration curve and breakdown coefficient methods were introduced to analyse the effects of the rainfall extremum distribution and time scale, and bounded random cascade simulation were used to generate random rainfall patterns (RRPs). The obtained RRPs under several classical rainfall patterns were compared in terms of the safety factor, rainfall threshold (IaS/IaF), and annual failure probability (AFP) of loess slopes. Finally, soil preferential flow and spatial variability combined with the RRPs were introduced to analyse the change in the stability of rainfall-induced loess landslides under different theoretical frameworks. It was revealed that the use of a uniform rainfall pattern produced conservative stability evaluation results. Considering the RRPs, the calculation of IaS/IaF and AFP should include the common characteristics of various rainfall patterns. By comprehensively evaluating slope stability considering soil spatial variability and rainfall pattern variability, this study revealed that neglecting certain factors may result in incorrect slope stability estimates. Incorporating multiple assumptions in failure probability evaluation could provide more comprehensive slope safety assessment.

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.

A Subdomain uncertainty-guided Kriging method with optimized feasibility metric for time-dependent reliability analysis

Active learning strategy combined with single-loop Kriging method has attracted much attention for Time-dependent Reliability Analysis (TRA). Large sample pool across all time instants is required to comprehensively cover the failure surface. Identifying training samples with most existing methods requires assessing the response of the entire sample pool, leading to high time costs. Similarly, to assist in identifying critical sampling regions, evaluating failure probability with a large sample pool in each iteration is also time-consuming. Additionally, higher-order uncertainty information is typically ignored, impairing the sampling efficiency. To address these issues, a Subdomain Uncertainty-guided Kriging (SUK) Method is proposed. Stochastic processes are first equivalently converted to random variables. By simultaneously sampling random variables and time parameter, an equivalent sample pool with significantly reduced size is generated for adaptive sampling. With a concise subdomain uncertainty assessment function, critical sampling region, i.e. sensitive subdomain, is distinguished efficiently. By comprehensively considering both the expectation and standard deviation of feasibility function, a novel Optimized Feasibility Metric (OFM) is then proposed for active learning. The proportion of misclassified samples is analytically deduced as stopping criterion. Finally, comparison results on four examples demonstrate the good performances of the proposed subdomain uncertainty-guided Kriging method.

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.

Development of fuzzy classifier for technical condition ranking of power transformer

The work aim is to develop a fuzzy classifier for technical condition ranking of power transformer under condition of vagueness and ambiguity diagnostic information. Methodology. The fuzzy classifier developing for technical condition ranking of power transformer was based on approach of using fuzzy set theory and optimization methods. The proposed approach for power transformer rank assessment by using a classifier was developed on the basis of Takagi-Sugeno fuzzy inference system. The input indicators choice is justified and their efficiency for classifier is evaluated by expert evaluation method. This makes it possible to formalize expert assessments regarding the development of power transformer defects. Results. The formalization of technical condition assessment of power transformer in knowledge base form, which implemented in expert system prototype for technical condition assessment, was carried out. The complex technical condition assessment for each functional unit of power transformer was determined based on expert evaluations with using the test and measurement parameters results. Originality. The considered approach to formalization of uncertainty regarding technical condition of power transformer allows building a deterministic decision-making scheme for further maintenance strategy, in which the ranking and decommissioning procedures for specific objects are implemented on the basis of objective criteria. Practical value. The proposed fuzzy classifier allows determination with a high probability degree of technical condition assessment of power transformer based on the test and measurement parameters results. Thus, an applied aspect of using the obtained scientific result is the possibility to objectively rank of power transformers park based on the identified possible defects and their development degree. This constitutes the prerequisites for determining the failure probability evaluation of power transformer at nearest observation period and emergency risk assessment in integrated electric power systems under power transformer failures.

Estimating Failure Probability with Neural Operator Hybrid Approach

Evaluating failure probability for complex engineering systems is a computationally intensive task. While the Monte Carlo method is easy to implement, it converges slowly and, hence, requires numerous repeated simulations of a complex system to generate sufficient samples. To improve the efficiency, methods based on surrogate models are proposed to approximate the limit state function. In this work, we reframe the approximation of the limit state function as an operator learning problem and utilize the DeepONet framework with a hybrid approach to estimate the failure probability. The numerical results show that our proposed method outperforms the prior neural hybrid method.

Application of state‐space models with frequency‐dependent uncertainty for efficient reliability analysis of complex structures

AbstractIn this work, we introduce a commonly used framework to model uncertain dynamical systems and adapt it to propagate both parameters uncertainty and model mismatches through a dynamically excited railway bridge system. The approach considers the system inherent uncertainties in the frequency domain and leads to a significant reduction of the computational effort when performing an uncertainty quantification tasks. In an application example the proposed approach is utilized for efficient reliability analysis of the uncertain railway bridge system. The utilized technique allows for an efficient generation of structural responses, and thus enables a fast evaluation of failure probabilities by crude Monte Carlo sampling. Accuracy of the uncertain prediction model is ensured by comparing the failure probabilities with failure probability simulations based on a semi‐analytical Euler‐Bernoulli bridge beam model.

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.

Applying Machine Learning and Automatic Speech Recognition for Intelligent Evaluation of Coal Failure Probability under Uniaxial Compression

Acoustic emission (AE) monitoring is an effective tool to quantify the dynamic damage that may cause heavy casualties and huge property losses in rock engineering. Instead of traditional failure evaluation methods, in this paper, the coal failure mechanism is evaluated in a complicated geological environment under uniaxial compression tests by employing machine learning (ML) and automatic speech recognition (ASR). Taking advantage of the ASR technology, the Mel-frequency cepstrum coefficients (MFCC) were extracted as sample features, while ML was used to paradigm the artificial intelligent evaluation of the failure probability of coal (AIEFPC). Additionally, the five-fold cross-validation method was used to assess the AIEFPC predictive effect incorporating cumulative hits number, cumulative ring count, and amplitude as sample features. The influence of category weight on the prediction effect of AIEFPC on a different category of sample sets has been discussed and analyzed. The results show that AIEFPC has the potential to use the MFCC of the 40 ms AE segment at any time to predict the dangerous state of the coal sample with a prediction accuracy of >85%. The probability value of the hazardous samples is computed through AIEFPC that further helped in evaluating the reliability of the prediction results. It is inferred from the obtained results that a larger category weight value of the hazardous samples can improve the prediction accuracy of AIEFPC than the safe sample. This research provides a new way of effectively predicting the coal failure probability before the damage and failure that can be applied to worldwide case-studies.

Efficient time-variant reliability analysis of Bazimen landslide in the Three Gorges Reservoir Area using XGBoost and LightGBM algorithms

The Three Gorges Reservoir Area (TGRA) is one of the most important areas for landslide prevention and mitigation in China. Rational reliability analysis of reservoir slope stability is a significant prerequisite for designing mitigation measures and preventing landslide disasters. Due to the influences of periodic reservoir water level fluctuation and seasonal rainfall, the reservoir slope reliability may be varying with the external environment. Although geotechnical reliability analysis has been widely applied, how to evaluate the time-variant reliability accurately and efficiently is still a challenging task. This study develops an efficient time-variant reliability analysis approach by integrating the advanced machine learning algorithms of extreme gradient boosting (XGBoost) and light gradient boosting machine (LightGBM). The proposed approach is applied to a practical case adapted from Bazimen landslide in the TGRA. The performances of XGBoost and LightGBM in the evaluation of Bazimen landslide time-variant failure probability are systematically explored. Results show that the proposed approach can evaluate the time-variant failure probability of Bazimen landslide accurately and efficiently, addressing the prohibitive computational cost of conducting a large number of deterministic analyses at each time instant repeatedly. This greatly facilitates the acquisition of time-variant failure probability for geotechnical engineers in practical applications.

Informational probabilistic sensitivity analysis and active learning surrogate modelling

In this paper, information theory is applied for probabilistic sensitivity analysis and surrogate modelling with active learning. One of the authors has recently proposed the adoption of the informational coefficient of correlation as a measure of dependence between random variables, in place of the largely adopted linear coefficient of correlation. Here, it is shown that the informational coefficient of correlation can be used for probabilistic sensitivity analysis based on the Value of Information (VoI). Effective Informational sensitivity indices based on the mutual information are presented. Moreover, two novel learning functions for adaptive sampling are proposed. The first, called H-function, gives rise to the method AK-H (Adaptive Kriging-Entropy), which describes the epistemic uncertainty through the entropy metric. The second, called MI-function, gives rise to the method AL-MI (Active Learning-Mutual Information), which describes the model error through the Mutual Information. The peculiarity of AL-MI is that it allows the implementation of active learning in any kind of surrogate modelling, even different from Kriging. The two learning functions are applied for two different categories of problems: (i) regression and (ii) evaluation of failure probability within the framework of structural reliability analysis. Numerical examples show its features and its potential for application of the proposed approach.

Failure probability evaluation for a weld of the heat pipe in the Mega-Power heat pipe cooled reactor

Heat pipe cooled reactor, featuring using heat pipes as passive cooling, has received renewed interest. A Monte-Carlo-based probabilistic fracture mechanics analysis code, including crack initiation and propagation model, is developed to estimate the probability of failure at a weld in the heat pipe due to stress corrosion cracking (SCC), fatigue, and high temperature creep. The application of this code is demonstrated on the heat pipe of the Mega-Power rector. The results show that SCC is more important in accelerating heat pipe failure than fatigue and high temperature creep. Failure probabilities under the condition of one or two adjacent failed heat pipes are abnormally close. Uncertainty analysis for input parameters proves initial crack size is more sensitive than temperature. This also provides a reference for heat pipe design and safety assessment of whole reactor.

Probabilistic fatigue life analysis considering mean stress effects of fiber reinforced polymer (FRP) composites

In general, the S-N curve, which describes the relationship between fatigue life and stress level with a typical guarantee rate, is employed during the design of FRP structures. However, the effect of mean stress on fatigue life is typically ignored. In this paper, a revision term is proposed to consider the mean stress effects on the basis of the common S-N equation. The third material parameter of the revised S-N-R equation increased the difficulty to predict the probabilistic fatigue life with the traditional analysis method during the design of FRP structures. Hence, a failure probability evaluation method is proposed to obtain the probabilistic fatigue life with three material parameters by using Markov chain Monte Carlo (MCMC) and Bayesian inference. The results show the applicability and effectiveness of the method in evaluating failure probabilities.

A Failure Probability Evaluation Method for the Collapse of Drill-Blast Tunnels Based on a Multistate Cloud Bayesian Network

Collapse is one of the main dangers of tunnel construction using the drill-blast method. To assess the risk of collapse and provide a basis for risk control, a failure probability evaluation method for tunnel collapse based on a Bayesian network (BN) and normal cloud theory is proposed in this paper. First, typical tunnel collapse cases are analysed statistically based on the risk breakdown structure method, a Bayesian network model is built for drill-blast tunnel collapse and the causal relationships between the tunnel collapse and influential factors, such as geological factors and construction management factors, are revealed. Second, the multiple fault states of the risk factors are described using fuzzy numbers. A multi-state fuzzy conditional probability table of uncertain logical relationships between nodes is established using normal cloud theory to describe node failure probabilities. Finally, this paper will consider the entire life cycle of risk-prone events, including pre-incident, dynamic evaluation of the construction process and post-incident control. A typical tunnel collapse incident encountered during the construction of the Jinzuba Tunnel in Fujian was used as an example to verify the applicability of the method.

Fundamental study on failure probability assessment method during vibration

The various vibration tests have been conducted, depending on the specification and shape of the structure. However, the data, which was obtained from these vibration tests, are underutilized in the failure evaluation caused by vibration. In this study, the failure probability evaluation method of the similar structure is investigated based on these test data. In previous studies, cabinets and underfloor equipment on railroad vehicles were investigated based on the PRA (Probabilistic Risk Assessment) method used in nuclear facilities. As a result, the failure probability could be evaluated at classifying these sample appropriately before the vibration test. Furthermore, in order to adapt this method to more structures and equipment, an evaluation indicator, which can be evaluated regardless of differences in input waves, is necessary. Accordingly, in this report, the vibration tests with the simple examination specimens were carried out in order to investigate the vibration energy which input to the equipment and accumulated. The natural frequency of the simple examination specimen is 112Hz. The sine wave test was carried out at the resonant frequency. The accumulated energy to failure, was investigated for each vibration test. As a result, the accumulated energy increased with increasing input acceleration, furthermore, the average value of the accumulated energy to failure was approximately 3750 [J] on this test condition.

Application of HMC-SS Method in Pipeline Reliability Analysis and Residual Life Assessment

In this paper, the reliability analysis and residual life assessment model of gas pipelines with multiple corrosion pits are established. Aiming at the simulation evaluation of small failure probability of gas pipelines, a new method for reliability analysis and residual life assessment of gas pipelines with multiple internal corrosion pits is proposed, which is called the Hamiltonian Monte Carlo subset simulation (HMC-SS) method. Compared with the traditional MCS (Monte Carlo simulation) algorithm, the HMC-SS method has the advantages of less sampling, low cost, and high accuracy. And compared with the random walk SS method, the HMC-SS method can analyze the state space more efficiently and achieve faster convergence. In this paper, the HMC-SS method is applied to the reliability analysis and residual life assessment of gas pipeline engineering, and the sensitivity analysis of the random parameters affecting the failure probability of the pipeline is carried out. The results show that the corrosion rate, the depth of corrosion defects, and the wall thickness of the pipeline have great influence on the residual life of the pipeline, while the yield strength, working pressure, and the length of corrosion pits have no obvious influence on the failure probability and residual life of the pipeline. The analysis shows that the proposed HMC-SS method can be used as a reasonable tool for failure assessment of natural gas pipelines affected by corrosion to determine the remaining life of the pipeline system. This method provides a reliable theoretical basis for the integrity management of the gas pipeline.