Methodology For Reliability Analysis Research Articles

Stochastic seepage stability analysis of the Pubugou clay core rockfill dam with potentially high permeability zone by integrating multi-source monitoring information

ABSTRACT One of the most important causes of the failure of a clay core rockfill dam (CCRD) is seepage failure caused by defects inside the clay core (e.g. a high permeability zone, HPZ for short). This study conducted a seepage stability analysis of the Pubugou CCRD with an HPZ, considering the uncertainty of dam parameters, including hydraulic conductivities, water level, and height of the HPZ. The reliability analysis methodology, both with and without the monitoring information, was first introduced. By inputting multi-source monitoring data, the probabilities of both the seepage failure and the dam parameters can be updated. The results show that the large monitoring values on 1 December 2010 have incurred an increase in the failure probability from 3.28 × 10−5 to 8.47 × 10−5, which was mainly caused by the increase of the hydraulic conductivity of the clay core (k 1). The failure probability then decreased from 8.47 × 10−5 to 7.46 × 10−6 when smaller monitoring values on 6 September 2011 were input. The maximum probability interval of the k 1 decreased from greater than 1.48 × 10−7 to between 4.18 × 10−8 and 7.86 × 10−8 due to long-term consolidation. However, the hydraulic conductivity of HPZ (k 2) slightly increased because of possible internal erosion caused by long-time seepage in HPZ.

Methodology for Reliability Analysis of Reinforced Concrete Structures over time Considering Combined Corrosion

One of the main causes of deterioration in reinforced concrete structures (RC) is the corrosion of steel bars. Many factors, including materials, environments, and loading conditions, which often have uncertain characteristics, affect the performance of RC structures. Some uncertainties may depend on time and location, and associated reliability issues become more difficult to address. This study presents a comprehensive method for assessing the temporal reliability of reinforced concrete structures, taking into account combined effects of carbonation (general corrosion) and chloride penetration (pitting corrosion). Probabilistic models are used to capture uncertainties related to the beginning, propagation, and combined effects of corrosion on structural performance over time. The time-dependent reliability of bridge structures is evaluated using sophisticated numerical simulations in conjunction with the FORM method for reliability analysis. Both time and space variables are treated as uniformly distributed random variables within specific intervals when generating sample points. Engineers and decision-makers can assess the reliability of reinforced concrete bridges using a systematic framework that considers the complex interactions of corrosion mechanisms. This aids an informed maintenance planning and effective resource allocation to ensure long-term structural safety and durability. Finally, the suggested method is tested on a reinforced concrete bridge with corroded steel reinforcement bars. The original work is validated, and the results are compared with the crude Monte Carlo simulation (MCS) method.

Modeling nuclear power plant piping reliability by coupling a human reliability analysis-based maintenance model with a physical degradation model

Reliability and availability analysis for repairable components, considering the underlying physical degradation and maintenance, is crucial in support of risk assessment and management. In nuclear power plants (NPPs), reactor coolant piping is a representative example of safety-critical repairable components that are subjected to long-term physical degradation interacting with maintenance activities. The existing methods for piping reliability analysis suffer from a limitation in their capability to analyze the time-dependent physics-maintenance interactions that could occur during the component lifetime and alter the underlying maintenance processes, for instance, an enhancement of maintenance programs based on condition monitoring data or an observed defect. To address this limitation, this paper develops a new piping reliability analysis methodology that couples a physics-of-failure (PoF) model with a maintenance performance analysis model. The contributions of this paper are two-fold: (i) developing a human reliability analysis (HRA)-based maintenance performance analysis model for NPP piping that can quantify maintenance outcomes under multiple types of maintenance programs, including time-based and condition-based preventive maintenance; and (ii) developing a computational methodology to couple the HRA-based maintenance performance analysis model with PoF models. The proposed physics-maintenance coupling methodology is applied to an NPP piping case study.

Fatigue reliability analysis methodology based on fatigue life and failure mechanism for carburized gear

Gears are one of the important components in mechanical products, and their fatigue reliability determines the safety performance of mechanical products. In this paper, the fatigue characteristics of carburized gears under different torque and constant rotational speed conditions are investigated by using a gear contact fatigue testing machine, and combined with the dynamic maximum contact stress distribution on the subsurface of the meshing gears, the very-high cycle fatigue P-S-N curves of carburized gears under a stress ratio of −1 is established. Local stress concentration in the surface or subsurface of carburized gears causes grain dislocation movement under the combined influence of maximum contact and residual stresses, and then causes dislocation pileup and transcrystalline rupture after being hindered by grain boundaries, ultimately leading to pitting and fatigue failure of gears. Based on the dislocation energy method and combined with the fatigue failure mechanism of gears, a life prediction model of gears with good prediction results is established by considering the interaction of factors such as dynamic load, grain size, initial crack length, residual stress, slip band length and width. A fatigue reliability analysis method of gears is established based on the life state equation of gears considering the life prediction model. Further analyses of the influence of rotational speed, grain size, residual stress, slip band width, slip band length and initial crack size on the fatigue life reliability index of the gears resulted in the conclusion that the fatigue reliability of the gears not only decreases with the increase of the above-mentioned parameters, but also shows decreasing trend with the increase of time. This is of significance for evaluating the fatigue reliability of gears under very-high cycle fatigue conditions.

Quantified active learning Kriging model for structural reliability analysis

A quantified active learning Kriging-based (qAK) methodology for structural reliability analysis is presented. The proposed approach is based on an updated probability density function (PDF), which is dominant in the vicinity of the limit-state surface. This PDF is created using weights based on an improved learning function called the most probable misclassification function. This function is used as a metric for efficiently updating the Kriging model, as it symmetrically quantifies the uncertainty of candidate points in terms of the model’s accuracy. The proposed approach accurately approximates the points that lie on the limit-state surface. Moreover, a probabilistic-based stopping criterion is proposed. The new support points are selected using the weighted K-means algorithm and the sample from the updated PDF. Thus, the method does not require solving an optimization problem or using a sampling algorithm. The proposed qAK methods are more reliable and robust than previous implementations of the Kriging method for structural reliability assessment. The proposed approach is presented within the framework of standard reliability methods, i.e., the Monte Carlo and the Subset Simulation methods. The efficiency of the proposed qAK methods is demonstrated with the aid of six case studies.

Time-domain fatigue reliability analysis for floating offshore wind turbine substructures using coupled nonlinear aero-hydro-servo-elastic simulations

The present study aims to develop a novel methodology for the fatigue reliability analysis and design for a floating offshore wind turbine substructure using its fully coupled nonlinear dynamic responses in the time domain. The developed methodology offers a computationally efficient yet robust assessment for designing fatigue-critical welded joints on floating substructures. The methodology starts with the sea state selection based on the fatigue damage contribution of all the sea states in the scatter diagram. To this end, the dynamic response is analysed by fully coupled aero-hydro-servo-elastic simulations using OpenFAST. The resulting short-term fatigue loading is then used to estimate the hotspot stress history using the analytical solution for global structural analysis and the empirical solution for the stress concentration factor. The long-term fatigue damage is calculated as the joint probability-weighted sum of the short-term fatigue damage using Palmgren-Miner's linear damage rule. Afterwards, the selected sea states are used to perform dynamic response simulations with multiple random seeds, ensuring no significant accuracy loss. To obtain the probabilistic fatigue life prediction, a novel time-domain fatigue reliability analysis algorithm is developed based on the bootstrapping method, which creates a pool of short-term fatigue responses with different random seeds and combines them within a Monte Carlo Simulation. Finally, the fatigue reliability analysis considering the S-N and damage-tolerant approaches for design is carried out.

Colorimetric and Photothermal Dual-Modal Switching Lateral Flow Immunoassay Based on a Forced Dispersion Prussian Blue Nanocomposite for the Sensitive Detection of Prostate-Specific Antigen.

Prostate-specific antigen (PSA) is a key marker for a prostate cancer diagnosis. The low sensitivity of traditional lateral flow immunoassay (LFIA) methods makes them unsuitable for point-of-care testing. Herein, we designed a nanozyme by in situ growth of Prussian blue (PB) within the pores of dendritic mesoporous silica (DMSN). The PB was forcibly dispersed into the pores of DMSN, leading to an increase in exposed active sites. Consequently, the atom utilization is enhanced, resulting in superior peroxidase (POD)-like activity compared to that of cubic PB. Antibody-modified DMSN@PB nanozymes serve as immunological probes in an enzymatic-enhanced colorimetric and photothermal dual-signal LFIA for PSA detection. After systematic optimization, the LFIA based on DMSN@PB successfully achieves a 4-fold amplification of the colorimetric signal within 7 min through catalytic oxidation of the chromogenic substrate by POD-like activity. Moreover, DMSN@PB exhibits an excellent photothermal conversion ability under 808 nm laser irradiation. Accordingly, photothermal signals are introduced to improve the anti-interference ability and sensitivity of LFIA, exhibiting a wide linear range (1-40 ng mL-1) and a low PSA detection limit (0.202 ng mL-1), which satisfies the early detection level of prostate cancer. This research provides a more accurate and reliable visualization analysis methodology for the early diagnosis of prostate cancer.

Phoenix–A model-based human reliability analysis methodology: Data sources and quantitative analysis procedure

Human Reliability Analysis (HRA) has continuously evolved to adopt more advanced human error probability (HEP) quantification approaches. Despite several efforts and enhancements of HRA methods, many still present limitations. Phoenix HRA Methodology aims to overcome known issues by incorporating strong elements of current HRA good practices, leveraging lessons learned from empirical studies and existing and emerging HRA methods' features. Phoenix models performance influencing factors (PIFs) and their impact on failure modes through Bayesian Networks (BBNs). Despite increased use in HRA applications, the large amount of data BBNs may require is still challenging. Thus, to populate Phoenix BBNs, several data sources were combined, including other HRA methods, expert judgment, and operating experience. The paper discusses Phoenix's quantitative analysis process, the data sources used to estimate the BBN parameters, and the applied data aggregation method. It further provides the quantitative analysis procedure guide with the significant steps and sub-steps an HRA analyst requires to implement this Phoenix methodology phase. Furthermore, the method for mapping the data to Phoenix BBN parameters and for aggregating the data considering non-homogeneous data sources can be further used for other HRA methods that apply BBNs or other applications.

Reliability analysis of fatigue crack growth in shallow shell structures using the Dual Boundary Element Method

This work presents a novel methodology for fatigue life reliability analysis using a newly developed Dual Boundary Element Method-based Implicit Differentiation Method (DBEM-IDM) in shallow shell structures. The proposed DBEM formulations evaluate stress intensity factors and fatigue life sensitivities in shallow shells considering geometrical variables and curvature. The IDM formulations, obtained through direct differentiation of the shallow shell DBEM formulations, uses the First-Order Reliability Method (FORM) to assess the reliability index and probability of failure of shallow shell structures with multiple cracks. Two numerical examples were simulated to illustrate the effectiveness of the proposed methodology. The first example examines a shallow shell with a centre hole and cracks subjected to various loads. The critical crack length required to ensure structural reliability and inspectability is determined. Fatigue reliability assessment is performed for the same example, employing limit state functions expressed in terms of fatigue life. FORM results are compared with the results of Monte Carlo Simulations (MCS), and provided a maximum difference of 3.67%. Parametric analyses are conducted to identify significant variables that affect reliability, and it was found that precise determination of design parameters is necessary to reduce the probability of failure, particularly for the Paris law constants. The second example, featuring more complex geometry, involved assessing fatigue reliability in a cylindrical shallow shell structure with multiple cracks. The results of this example indicate a 3.49% maximum difference between FORM and MCS. The results exhibit a low error compared to the MCS, and the inherent efficiency of the IDM-based FORM underscores its suitability for addressing uncertainty in reliability analysis.

Reliability Analysis and Risk Assessment for Settlement of Cohesive Soil Layer Induced by Undercrossing Tunnel Excavation

Due to the complex urban geological environment and physicochemical interactions, the physical and mechanical parameters of the cohesive soil layer in the adjacent construction area show strong spatial variability and correlation. In addition, the actual exploration and test data are very limited because of limited technical and economic conditions. This severely restricts the ability to evaluate the stability of adjacent structures and to prevent and control instability disasters during subway construction. In this study, a generation method of limited sample data for the cohesive soil layer in the adjacent construction area is proposed. The spatial variability and correlation of uncertain mechanical parameters for the clay layer are quantified using incomplete probability data. A calculation method of uncertain settlement for the cohesive soil layer in the adjacent construction area is developed. The distribution fitting tests of settlement characteristics are conducted with different joint distribution functions and correlation structure. A reliability analysis and risk assessment methodology for the settlement of the cohesive soil layer is presented. The reliability value and failure probability induced by undercrossing tunnel excavation are analyzed and predicted. The results show that the bootstrap simulated sampling and random field method can quantify the cohesive soil layer heterogeneity reasonably under limited investigation data. Different joint distribution and correlation structure functions have different effects on the distribution fitting test. The uncertain settlement of the upper center of the tunnel is the largest, and the failure disaster is most likely to occur. The effects of a copula structure and correlation parameter on the failure probability of the cohesive soil layer are sensitive. This research can provide scientific support for public safety and sustainable development in urban subway construction.

A novel reliability analysis methodology based on IPSO-MCopula model for gears with multiple failure modes

In order to accurately and quickly predict the failure probability of gears with multiple failure modes, a novel reliability analysis methodology based on the mixed Copula (MCopula) function model is proposed to deal with the complex correlation among different failure functions. Firstly, we construct a novel MCopula model based on three famous Copula functions: Gumbel Copula, Clayton Copula, and Frank Copula functions. Secondly, we use and improve the particle swarm optimization (PSO) method to optimally calculate the weight coefficients in the proposed MCopula model. Thirdly, the maximum likelihood estimation (MLE) method is adopted to estimate related parameters in the proposed MCopula model. Finally, we verify the proposed reliability analysis methodology with a standard life-prediction case of a strut system and a practical life-problem case of a gear pair system. Comparison results of both cases show that, by using the proposed methodology, the failure probability of a gear pair system with multiple failure correlations can be quickly calculated through a small number of samples and can be estimated as accurately as that by the Monte Carlo scheme. Consequently, our proposed novel methodology successfully analyzes the reliability problems for a gear pair system with multiple failure modes. The proposed methodology can be further applied to solve the reliability problem for other machine parts.

Active Learning-Based Kriging Model with Noise Responses and Its Application to Reliability Analysis of Structures

This study introduces a reliability analysis methodology employing Kriging modeling enriched by a hybrid active learning process. Emphasizing noise integration into structural response predictions, this research presents a framework that combines Kriging modeling with regression to handle noisy data. The framework accommodates either constant variance of noise for all observed responses or varying, uncorrelated noise variances. Hyperparameters and the variance of the Kriging model with noisy data are determined through maximum likelihood estimation to address inherent uncertainties in structural predictions. An adaptive hybrid learning function guides design of experiment (DoE) point identification through an iterative enrichment process. This function strategically targets points near the limit-state approximation, farthest from existing training points, and explores candidate points to maximize the probability of misclassification. The framework’s application is demonstrated through metamodel-based reliability analysis for continuum and discrete structures with relatively large degrees of freedom, employing subset simulations. Numerical examples validate the framework’s effectiveness, highlighting its potential for accurate and efficient reliability assessments in complex structural systems.

A Methodology for Reliability Analysis of Explainable Machine Learning: Application to Endocrinology Diseases

A Methodology for Reliability Analysis of Explainable Machine Learning: Application to Endocrinology Diseases

Logical Resolving-Based Methodology for Efficient Reliability Analysis.

With the CMOS technology downscaling to the deep nanoscale, the aging effects of devices degrade circuit performance and even lead to functional failure. The stress analysis is critical to evaluate the influence of aging effects on digital circuits. Some related analytical work has recently focused on reliability-aware circuit analysis. Nevertheless, the aging dependence among different devices is not considered, which will induce errors of degradation evaluation in the digital circuit. In order to improve the accuracy of reliability-aware static timing analysis, an improved analytical method is proposed by employing logical resolving. Experimental results show that the proposed method has a better evaluation accuracy of aging path delay than traditional strategies. For aging timing evaluation on aging paths, excessive pessimism can be reduced by employing the proposed method. And, a 378× speedup is achieved while having a 0.56% relative error compared with precise SPICE simulation. Moreover, the circuit performance sacrifice of an aging-aware synthesis flow with the proposed method can be decreased. Due to the high efficiency and high accuracy, the proposed method can meet the speed demands of large-scale digital circuit reliability analysis while achieving transistor simulation accuracy.

Reliability Analysis and Optimization Method of a Mechanical System Based on the Response Surface Method and Sensitivity Analysis Method

Mechanical system reliability analysis constitutes a primary research focus in the field of engineering. This study aims to address the issue of complex mechanical systems with intricate mechanisms and nonlinear reliability equations that are challenging to solve. To this end, we present a reliability analysis and optimization methodology that merges the response surface and sensitivity analysis methods. A comprehensive formation of reliability assessment and optimization of complex mechanical systems is achieved by creating a response surface model to fit the complex state function and solving the reliability parameters, followed by an error sensitivity analysis to determine the mechanical system’s reliability adjustment strategy. Finally, these methods are applied to a cylindrical material transport device to preliminarily realize the reliability assessment and average reliability optimization goals. The study’s findings may offer a theoretical framework and research opportunities to evaluate and enhance the reliability of intricate mechanical systems.

A Methodology for Performance and Reliability Analysis of Prosumers’ Local Heat Sources in District Heating System

A Methodology for Performance and Reliability Analysis of Prosumers’ Local Heat Sources in District Heating System

Reliability Analysis of Seismic Slope Incorporating Interactions among Multiple Sliding Blocks Using Imbalance Thrust Force Method in Primary Sliding Direction

This paper proposes a methodology for reliability analysis of seismic slope stability that incorporates interactions among multiple sliding blocks. The primary sliding direction is first determined using the vector sum method and then the imbalance thrust force along the primary sliding direction is calculated using the slice-wise strategy and, finally, the double integration strategy is adopted to calculate the accumulated sliding displacement within the earthquake duration. The interactions among multiple sliding blocks are incorporated by checking the potential of occurrence for each of the multiple sliding modes. The proposed method is applied to a soil slope with two sliding surfaces. The comparative studies demonstrate that the mean and standard deviation of the sliding displacement considering the interaction of multiple sliding blocks are approximately three times larger than that of a single sliding mode, and the COV (mean value divided by standard deviation) of the two are slightly different. For the single sliding mode, the mean and standard deviation of the sliding displacement calculated using the proposed method are about 1/2 of the traditional Newmark sliding block model, and the failure probability obtained by the proposed method is lower than that from the traditional Newmark sliding block model owing to the difference in the sliding direction. The Peak Ground Acceleration (PGA) exhibits a significant effect on the statistics of 10,000 sliding displacements. The interactions among multiple sliding blocks and the PGA are required to be properly considered in seismic slope reliability analysis.

Reliability analysis of an embankment dam slope based on an ellipsoid model and PSO-ELM

Accurately comprehending the intricate characteristics of geotechnical parameters can be challenging due to the complexity of geotechnical engineering and the lack of available information. The accuracy and efficiency of conventional reliability analysis methodologies are impeded by the copiousness of raw data. This study proposed a non-probabilistic method for reliability analysis, which employed an ellipsoidal model integrated with a machine learning algorithm. This novel method characterized parameter uncertainties by their boundaries, utilized the synergy between particle swarm optimization and extreme learning machine for performance function surrogate, and employed an ellipsoidal model to calculate the reliability index. The proposed method was applied to evaluate the stability of an embankment dam slope in France. The results revealed the significant influence of the alterations in internal friction and cohesion on the stability of the embankment dam slope. Therefore, it can be imperative to account for the variability and correlation of soil parameters when conducting a rigorous analysis of embankment dam slope stability. Notably, the proposed method can circumvent the requirement for employing probability distribution forms to represent parameters, while concurrently ensuring a commendable level of computational efficiency and accuracy. Furthermore, the proposed method can be compatible with probabilistic analysis methods in evaluating the stability of the embankment dam, thereby providing an effective method for conducting reliability analyses of such structures.

Toward a Comprehensive Pavement Reliability Analysis Approach

Reliability has been incorporated in pavement design tools to account for input variability influence on predicted performance. As they are not based on a probabilistic method of uncertainty propagation, the reliability analysis methodologies that are currently implemented in pavement performance tools lack rigor and robustness. This paper investigates the potential of three reliability analysis methodologies for pavement application: the Pavement ME reliability analysis methodology, Monte Carlo simulation (MCS), and the first-order reliability method (FORM). The MCS and FORM involve a response surface method for the generation of a second-order surrogate model. The investigation was performed using inputs and performance data from accelerated pavement testing structures. Inputs that were identified as significant were characterized as random variables and their associated variability was established using measured structural and material properties. Pavement performance with respect to rutting was predicted using the ERAPave performance prediction tool, while MCS was used to generate the actual variability of the distress. The reliability analysis results have shown that a comprehensive reliability analysis methodology is required that effectively captures input variabilities and the error associated with surrogate models.

A GSPN-based method for dynamic system reliability modelling and evaluation

As dynamics has been significant characteristics of modern systems, many dynamic reliability analysis methods are studied, such as Markov method and dynamic Bayesian network. Among these existing methods, generalized stochastic Petri net (GSPN) has an excellent ability of characterizing the dynamic behaviors of systems, but because of its poor computing capability it can only be applied to simple system reliability analysis. Monte Carlo simulation method is powerful in computing, but it has no ability to model system dynamic behavior intuitively. In view of the fact that both GSPN and Monte Carlo method have a potential of making up the disadvantage of each other, this paper proposes a dynamic reliability analysis methodology by the combination of these two methods. Specifically, the paper defines a new GSPN for modeling dynamic systems. Afterwards, a simulation algorithm for analyzing the new GSPN is developed based on the Monte Carlo theory. Finally, the correctness and effectiveness of the proposed methodology is validated with a dynamic electromechanical system, which is analyzed by using the new proposed method and traditional Markov method, separately.