Time-dependent Failure Research Articles

An efficient sequential Kriging model for structure safety lifetime analysis considering uncertain degradation

Safety lifetime analysis performs a crucial role in ensuring structural safety in service and developing effective maintenance strategies, which also places higher demands on calculation. However, existing safety lifetime analysis methods generally suffer from inefficiency, which is more prominent for complex engineering structures. In this paper, a novel sequential single-loop Kriging (SSK) surrogate modeling approach is proposed to calculate the safety lifetime in an efficient and accurate manner. To reduce the computational cost, a single-loop safety lifetime analysis framework is proposed. In this framework, there is no need to accurately calculate the time-dependent failure probability (TDFP) in any sub-time interval. By searching the safety lifetime in the process of time-dependent reliability analysis (TRA) and dynamically adjusting the interest time interval, the safety lifetime can be quickly determined by constructing only one Kriging model. To maximize the utilization of sample information, SSK employs a modified learning function that allows most of the training points to be added before the safety lifetime. For accuracy, a convergence criterion that includes two Kriging models is proposed. Mathematical engineering examples are used to illustrate the accuracy and efficiency of SSK. The proposed method offers a promising approach for efficient safety lifetime analysis of engineering problems.

Time-dependent reliability analysis of flood defenses under cumulative internal erosion

Internal erosion is a significant cause of failure in dams, levees and other hydraulic structures. This article studies the time-dependent reliability of such structures under Backward Erosion Piping (BEP), a form of internal erosion in the foundation. First, a physics-based time-dependent piping failure model is presented. Second, a time-variant reliability analysis method is presented which allows to quantify how the reliability evolves over the years due to cumulative pipe growth over multiple flood events. Finally, these models are used to study the importance of time-dependence for reliability estimates of flood defenses in The Netherlands. The findings show that, particularly in coastal areas, incorporating time-dependence significantly reduces the computed failure probability. Reductions vary widely, ranging from a factor of 5 to more than 10 6 , depending on flood duration and levee properties. Therefore, reliability estimates for levees can be improved by incorporating time-dependent pipe development in the BEP failure model, and thereby contribute to avoiding unnecessary reinforcements.

Time-Dependent Failure Assessment of Ceramic Receivers

The outlet temperature targets for Gen 3 Concentrating Solar Power (CSP) systems pose a significant challenge to the structural reliability of high temperature metallic components, including those manufactured from nickel-based superalloys. Advanced ceramics present a potential solution due to their excellent high-temperature strength. However, accurate assessment of ceramic components requires an entirely different approach compared to metallic components. This paper describes the implementation of time-dependent reliability analysis of ceramic components in srlife – an open-source software package for estimating the life of high temperature CSP components. This new capability will allow high temperature CSP designers to make fair comparisons between competing metallic and ceramic designs and accurately assess the performance of different ceramic materials for CSP receivers and other components. The current version of the tool is available at https://github.com/Argonne-National-Laboratory/srlife.

A novel cross-entropy-based importance sampling method for cumulative time-dependent failure probability function

Cumulative time-dependent failure probability function (C-T-FPF) can reflect the effects of distribution parameters of random inputs and the upper bound of service time interval, which vary in their design domains, on time-dependent failure probability. However, solving C-T-FPF involves a time-consuming triple-layer framework. Thus, we propose an efficient method by combining cross-entropy-based importance sampling (IS) with adaptive Kriging model (CE-IS-AK). The innovations of CE-IS-AK include two aspects. Firstly, we construct a space augmented by both distribution parameters and the upper bound of service time interval, on which the triple-layer framework is decoupled to a single-layer one. And in the augmented space, an optimal IS density is proposed to reduce the required candidate sample size for estimating C-T-FPF. Secondly, we employ Gaussian mixture model (GMM) to approximate the optimal IS density, and the parameters in GMM are determined by minimizing the cross entropy of GMM and the optimal IS density. Moreover, to reduce the model evaluations in the proposed method, Kriging model is adaptively embedded to replace the actual model, and a first failure instant based learning function is proposed to train Kriging model adaptively. Due to the proposed single-layer framework and IS strategy assisted by the Kriging model, the efficiency is greatly improved for estimating C-T-FPF, which is validated by several examples.

Unraveling time-dependent roof stability dynamics in Iran's coal mines through laboratory-based rock displacement testing

Roof stability is a critical concern in coal mines, as the potential for roof collapse poses a significant risk to miners' safety and productivity. Roof stability is heavily influenced by the time-dependent properties of the rock mass above the workings. This study uses rock displacement testing to examine the effect of time-dependent properties on roof stability and resistance reduction in coal mines in Iran. The study employed laboratory-based rock displacement tests on samples collected from coal mines in Iran, subjected to varying stress levels over time, to simulate the gradual deterioration of rock mass strength in underground mining conditions. The samples were monitored for displacement under these conditions to quantify the reduction in roof resistance over time and assess its effect on roof stability. The study found that areas with high stress at equilibrium gradually fail with time, and the stress transfers from the failure zone into deeper solid rock. The results demonstrate that varying viscous parameters can lead to different relaxation behaviors and stress distribution. Furthermore, incorporating strength degradation into numerical simulation can capture the failure under creep conditions and improve the accuracy of predicting time-dependent roof failure. This research aims to enhance safety measures and reduce the risk of collapses by investigating the time-dependent properties of roof stability through rock displacement testing in Iran's coal mines. The study's innovative approach uses numerical simulation based on the viscoelastic-plastic model to simulate the time-dependent behavior of the rock and incorporate strength degradation into the simulation. The results provide valuable insights into the time-dependent behavior of rock mass in coal mines in Iran and contribute to developing strategies for improving roof stability and lessening the chance of roof collapses. The instantaneous elastic strain was 4.35 × 10–4, and creep simulation was activated to run for a time equivalent to 2 × 106 s.

Impact of Process on Gate Leakage Current and Time-Dependent Dielectric Breakdown Failure Mechanisms of 4H-SiC MOS Capacitors

Impact of Process on Gate Leakage Current and Time-Dependent Dielectric Breakdown Failure Mechanisms of 4H-SiC MOS Capacitors

Efficient reliability analysis of stochastic dynamic first-passage problems by probability density evolution analysis with subset supported point selection

This study introduces a novel point selection procedure for the Probability Density Evolution Method (PDEM) to estimate time-dependent reliability and failure probabilities in dynamic systems under first-passage failure conditions. The method integrates and modifies features of the Subset simulation procedure to adaptively generate dependent sample sets suitable for a reliability analysis by a direct probability integration approach levering PDEM. Performance function assessments are used as weighting factors in the Subset supported Point Selection (S-PS), enhancing the ultimate failure probability estimation accuracy. The presented approach effectively identifies samples in the failure region, particularly benefiting for dynamic systems under stochastic excitation, tested with random dimensions up to 60. It also offers a computationally efficient structural reliability estimation procedure by analyzing full time–history responses. The proposed method provides deeper insights into rare failure events and mechanisms through the visualization of intermediate results. This research presents an advanced framework for estimating structural reliability and understanding critical events in dynamic systems.

Sequentially Quadratic Surrogate Algorithm for Time-dependent Reliability and Reliability Sensitivity Analysis

Time-dependent reliability and reliability sensitivity analysis in presence of random uncertainty is widespread in equipment structures. To this end, this paper establishes a sequentially quadratic surrogate method. Firstly, the global reliability sensitivity analysis (GRS) is transformed into the classification problem of the time-dependent performance function outputs by means of conditional probability formula. Secondly, referring to the strategy of the Meta-IS method, the Kriging model of time-dependent performance function is employed to construct the importance sampling function to generate the importance sampling (IS) samples of failure domain efficiently. Furthermore, the Kriging model is updated in the IS samples set through the single-loop adaptive Kriging method to realize the accurate identification of the failure indicator function of IS samples, as well as simulation of time-dependent failure probability. Finally, utilize the information of the failure samples obtained by the estimation of time-dependent reliability to evaluate GRS. The proposed algorithm has excellent computational efficiency and applicability due to the conversion of the conditional probability formula, which enables the computational consumption of the time-dependent reliability and GRS analysis independent of the dimensions of the inputs, as well as the Meta-IS method, which improves the sampling efficiency and is applicable to the case of complex implicit performance function. The given examples fully verify the conclusions.

Fractional cyclic cohesive zone model for time-dependent fatigue behavior of soft adhesives under mode-II loading

Soft adhesives exhibit nonlinear viscoelastic behavior during cyclic loading, suggesting that their cycle-dependent fatigue behavior can be significantly affected by the time-dependent deformation behavior. Soft adhesives are frequently subject to various cyclic conditions in practical applications, presenting potential safety risks. This paper has proposed a fractional cyclic cohesive zone model (CZM) to characterize the time-dependent fatigue behavior of soft adhesives. To capture the nonlinear evolution of fatigue damage under asymmetric stress-controlled cyclic loading, a fatigue damage model that considers the contribution of in-situ stress/strain was incorporated into fractional-order viscoelastic model. The proposed CZM was validated through single-lap shear creep tests at various stress levels and fatigue tests at various stress amplitudes and loading periods. Furthermore, the impacts of stress ratio and peak stress level on fatigue failure under different loading periods were investigated using the proposed CZM. The results indicate that under a short loading period, the failure of the soft adhesives is primarily influenced by cycle-dependent fatigue damage, while under a long loading period, it is primarily affected by time-dependent creep deformation. Overall, this work offers a numerical strategy to describe the fatigue failure of nonlinear viscoelastic soft adhesives and to understand their time-dependent fatigue failure mechanism, with implications for product design and optimization.

Study on creep characteristics of granite of deep tunnel affected by joint orientation

Joints are an important part of rock masses and the spatial relationship between joint orientation and in situ stress (or tunnel axis) has a significant impact on the deformation, strength, and failure behavior of the surrounding rock. To study the influence of joint orientation on creep characteristics of deep hard rock, the true triaxial creep tests were performed in the laboratory on natural jointed granite under different loading angles ω (defined as the angle between the strike of the joint plane and σ2-direction) conditions. The test results show that ω has a significant effect on the time-dependent deformation and failure characteristics of the jointed rock. As ω increases, there is a decrease in creep deformation, steady creep rate in the σ3-direction, and value of the DI index (used to evaluate the difference in the time-dependent deformation in the σ2- and σ3-directions). Stress-structure-controlled failure (sliding along the joint plane) occurs when ω ≤ 30°. However, when ω > 30°, the failure of the jointed granite is not affected by joint and stress-controlled failure occurs. The stress-structure-controlled time-dependent failure evolution enters a stage that is dominated by shear cracks earlier than stress-controlled failure according to AE results. In addition, the time-dependent failure went through stages of microcracks initiation and interaction, stable crack growth and unstable crack propagation. And when the time-dependent failure evolution enters the unstable crack propagation stage, the nature of the cracks changed from tensile-dominated to shear-dominated.

Two-Phase Adaptive Kriging Model Based Importance Sampling Method for Estimating Time-Dependent Failure Probability

To improve the computational efficiency of the time-dependent failure probability (TDFP) to system with dynamic loads, materials degeneration and other time-varying factors, a two-phase adaptive Kriging model-based importance sampling (IS) method is proposed. The main contribution of this article is that a single Kriging model is established and adaptively refined to gradually approach the optimal IS probability density function (PDF) and estimate TDFP in two sequential phases. In the first phase, a generally approximate IS PDF is constructed by Kriging model based on its prediction characteristic and subsequently updated until convergence. Then, a simple rejection sampling technique is used to robustly generate IS samples for the estimation of TDFP. In the second phase, current Kriging model is subsequently refined for accurately identifying the classification of the IS samples generated in the last phase. Finally, the TDFP can be estimated using the products obtained in the above two phases. As the IS PDF established by Kriging model is used for directly approximating the optimal one, the proposed method has the capacity to deal with the complicated problem containing multiple failure regions. Additionally, since IS procedure can dramatically improve the sampling efficiency, the proposed method is an effective technique to the time-consuming TDFP analysis problems.

Data-Driven Fatigue Failure Probability Updating of OSD by Bayesian Backward Propagation

This study introduces a data-driven approach for updating the fatigue failure probability of the orthotropic steel deck (OSD) using Bayesian backward propagation. The OSD in steel bridges is considered as a parallel system composed of two critical fatigue-prone components, namely, the rib-to-diaphragm and rib-to-deck joints. A probabilistic model for fatigue reliability is established based on the equivalent structural stress method and limit state function. The system-level fatigue reliability model is then constructed, taking into account the correlations between limit states of individual components through Bayesian network forward propagation. The key advantage of the Bayesian network-based framework is its ability to perform backward propagation, allowing for the updating of failure probabilities for critical components when the system-level failure of the OSD is observed. Consequently, the proposed approach enables the identification of vulnerable components through data-driven fatigue failure probability updating. Finally, the approach is applied to a real instrumented steel bridge to determine the time-dependent fatigue failure probability at both the system and component levels over its service life. The results show that the component-level fatigue failure probability model will underestimate the fatigue life in comparison to the system-level model. Meanwhile, the proposed method could identify vulnerable components by quantifying the fatigue failure probability of in-service steel bridges.

Time-dependent failure characteristics of natural jointed granite of deep tunnel under different dip angles conditions

Time-dependent failure characteristics of natural jointed granite of deep tunnel under different dip angles conditions

Time-dependent reliability analysis of structural systems based on parallel active learning Kriging model

Time-dependent reliability analysis quantifies the failures of structural systems due to time-dependent uncertainties, such as material degradation and dynamic loads. The active learning Kriging model methods are widely used in structural reliability analysis to replace extensive time-consuming finite element simulations. However, they can only update one training sample and one failure mode per iteration, which limits their application to time-dependent, parallel computing, and multiple failure modes problems. In this study, we propose a new parallel active learning Kriging model for time-dependent reliability analysis, which can update multiple training samples and multiple failure modes per iteration. It includes the following strategies: (1) a novel parallel learning function is proposed, which combines the correlation function and U learning function to allow for the selection of multiple training samples per iteration; (2) an adaptive adjustment strategy for the number of parallel samples is proposed, which takes into account the prediction probability of parallel samples; (3) the proposed parallel learning function is integrated into time-dependent reliability analysis with multiple failure modes, enabling simultaneous updates of multiple training samples and failure modes, thus greatly reducing the number of iterations and computational time; and (4) a new stopping criterion is proposed to improve the efficiency of the estimation of failure probability. The proposed method can be applied to series or parallel time-dependent structural systems with multiple failure modes. We demonstrate the effectiveness of the proposed method through three examples, and the proposed method can achieve a balance between the computational time and function calls while maintaining a high level of accuracy in the estimation of time-dependent failure probability.

Time-dependent earthquake-fire coupling fragility analysis under limited prior knowledge: A perspective from type-2 fuzzy probability

Earthquake-triggered fire domino scenarios (E-FDSs) arise frequently from the interaction between earthquakes and chemical installations, resulting in catastrophic multi-hazard coupling events. The complicated mutually amplified phenomena between natural disasters and chemical accidents significantly aggravates the escalation of domino accidents, which has posed great challenges for modeling and preventing E-FDSs. Under this impetus, this work proposes an advanced type-2 fuzzy probabilistic methodology to obtain the time-dependent failure probability of steel cylindrical tanks (SCTs) subjected to the earthquake-fire sequence. To cope with the limited prior knowledge on E-FDSs, a basic universal is established to describe the fire resistance attenuation caused by the seismic damage. The coupling failure criterion of SCTs is formulated by a type-2 fuzzy time-dependent limit state equation. A credibility-based stochastic simulation algorithm is developed for the hybrid uncertainty analysis (combining ambiguity and stochasticity). The proposed methodology is validated by case studies of a 5000 m3 fixed roof tank. Compared to the existing accident probability model, the proposed methodology can not only capture the fire resistance attenuation caused by the seismic damage but also provide a dynamic estimation of tank failure probability with respect to the fire exposure time. The proposed methodology can effectively and dynamically capture the accident evolution process, which in turn helps mitigate and prevent the spatiotemporal propagation of domino effects.

Simulation of fracture in vascular tissue: coupling a continuum damage formulation with an embedded representation of fracture

The fracture of vascular tissue, and load-bearing soft tissue in general, is relevant to various biomechanical and clinical applications, from the study of traumatic injury and disease to the design of medical devices and the optimisation of patient treatment outcomes. The fundamental mechanisms associated with the inception and development of damage, leading to tissue failure, have yet to be wholly understood. We present the novel coupling of a microstructurally motivated continuum damage model that incorporates the time-dependent interfibrillar failure of the collagenous matrix with an embedded phenomenological representation of the fracture surface. Tissue separation is therefore accounted for through the integration of the cohesive crack concept within the partition of unity finite element method. A transversely isotropic cohesive potential per unit undeformed area is introduced that comprises a rate-dependent evolution of damage and accounts for mixed-mode failure. Importantly, a novel crack initialisation procedure is detailed that identifies the occurrence of localised deformation in the continuum material and the orientation of the inserted discontinuity. Proof of principle is demonstrated by the application of the computational framework to two representative numerical simulations, illustrating the robustness and versatility of the formulation.

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.

Comprehensive Study of Failure Mechanisms of Field-Aged Automotive Lead Batteries

Modern vehicles have increasing safety requirements and a need for reliable low-voltage power supply in their on-board power supply systems. Understanding the causes and probabilities of failures in a 12 V power supply is crucial. Field analyses of aged and failed 12 V lead batteries can provide valuable insights regarding this topic. In a previous study, non-invasive electrical testing was used to objectively determine the reasons for failure and the lifetime of individual batteries. By identifying all of the potential failure mechanisms, the Latin hypercube sampling method was found to effectively reduce the required sample size. To ensure sufficient confidence in validating diagnostic algorithms and calculating time-dependent failure rates, all identified aging phenomena must be considered. This study presents a probability distribution of the failure mechanisms that occur in the field, as well as provides insights into potential opportunities, but it also challenges diagnostic approaches for current and future vehicles.

A novel quantile-based sequential optimization and reliability assessment method for safety life analysis

Safety life analysis under random uncertainty is an effective tool for guiding the maintenance and design of structures. However, existing methods for safety life analysis employ a nested solution strategy by dichotomy, with inner time-dependent reliability analysis, resulting in high computational costs. This paper proposes a quantile-based sequential optimization and reliability assessment (QSORA) method to overcome the shortcoming of nested solution strategy in safety life analysis. In QSORA, the probability constraint is first equivalently transformed into a quantile constraint corresponding to the target time-dependent failure probability (TDFP). Then, the coupling relationship between the search for safety life and the quantile estimation is then eliminated, and the safety life is solved iteratively through a series of cycles. Each cycle contains two independent executions: an equivalent deterministic optimization and quantile estimation of extreme performance function corresponding to the target TDFP. Finally, a sampling-based method combined with a double-loop Kriging model is proposed to efficiently estimate the quantile, and it is embedded into QSORA to improve the efficiency of safety life analysis. Two learning functions are adopted to ensure the accuracy of double-loop Kriging model. Numerical and engineering examples verify the efficiency and accuracy of the proposed method for safety life analysis.

Potential change of failure modes and time-dependent behavior of RC beams under a marine atmospheric environment

Stirrups always corrode earlier than the longitudinal steel bars in RC beams, and the corrosion degree is higher. Stirrups will lose their bearing capacity due to corrosion, leading to a decrease in shear strength. Thus, the failure mode may change from bending to shear failure. Previous studies have proposed models to predict corroded RC beams' flexural and shear behavior, respectively. The missing link in these models was the absence of the change of failure modes on the structural behavior. This paper covers the authors' study of corroded RC beams, investigating the effects of reinforcement corrosion on the transformation of failure modes. A numerical simulation was performed to analyze RC beams' time-dependent behavior and failure modes. The effects of the concrete cover thickness, the corrosion initiation time, the concrete strength, and the reinforcement configuration were also investigated. The analysis showed that the stirrups corroded earlier than longitudinal steel bars, leading to potential shear failure rather than bending failure. The larger diameter of stirrups could be adopted in the design to delay the deterioration of structural behavior and the transformation of failure mode. Prolonging the corrosion initiation time using thicker concrete cover thickness and higher strength of concrete is also effective.