Search

The computational burden becomes unbearable when reliability analysis involves time‐consuming finite element analysis, especially for rare events. Therefore, reducing the number of performance function calls is the only way to improve computing efficiency. This paper proposes a novel reliability analysis method that combines relevant vector machine (RVM) and improved cross‐entropy adaptive sampling (iCE). In this method, RVM is employed to approximate the limit state surface and iCE is performed based on the constructed RVM. To guarantee the precision of RVM, the first level samples and the last level samples of iCE are used as candidate samples and the last level samples are regenerated along with the RVM updates. To prevent unnecessary updates of RVM, the proposed method considers the positions of the samples in the current design of experiment. In addition, based on the statistical properties of RVM and iCE, an error‐based stopping criterion is proposed. The accuracy and efficiency of the proposed method were validated through four benchmark examples. Finally, the proposed method is applied to engineering problems which are working in extreme environment. [ABSTRACT FROM AUTHOR]

Drilling of stacks comprising carbon fibre-reinforced polymers (CFRP) and aluminium in a single shot is a typical operation in the assembly of aircraft. This paper proposes a novel approach to identify incidences in CFRP/Al stack drilling with 94 % classification accuracy based on signal features and support vector machine (SVM). This enables the application of adaptive drilling which aerospace industry tries to introduce, and cutting parameters (cutting speed, feed) are automatically adjusted based on features extracted from signals obtained to achieve optimal machining. The t-distributed stochastic neighbour embedding (T-SNE) algorithm is applied to evaluate the separability and invariance of features with the significant influence of tool wear. Collinear analysis and hierarchy dendrogram are conducted to test the accuracy and robustness of the new approach, and a distance-based feature pruning is then proposed to compress data while maintaining the algorithm's performance. The proposed SVM model achieves an accurate and reliable incidence identification, thereby enhancing the decision-making for adaptive drilling in machining stacked structures. [ABSTRACT FROM AUTHOR]

This paper examines multiscale theories and numerical methods for interconnect materials in electronic packaging, focusing on the interplay among micro-scale morphology, meso-scale structure, and macro-scale behavior to improve material reliability and performance prediction. It reviews advanced materials, such as sintered silver and lead-free solder, alongside methodologies like Molecular Dynamics (MD) simulations, cohesive modeling, crystal plasticity modeling, and phase-field modeling, to evaluate mechanical and thermal properties across scales and their long-term reliability. At the microscopic scale, MD simulations reveal the influence of atomic arrangements, grain orientations, and dislocation evolution on mechanical behavior. At the mesoscopic scale, phase-field and crystal plasticity models are combined to analyze pore evolution, grain sliding, and stress concentration under thermal cycling. Macroscopically, models like Anand and Unified Creep Plasticity (UCP) describe viscoplasticity, creep, and fatigue life, offering insights into performance under complex conditions. By systematically integrating diverse research methods and theoretical models, this review highlights the applicability of a multiscale coupling framework, providing a comprehensive understanding of the correlations between morphology, structure, and behavior. This framework serves as theoretical guidance for developing innovative packaging solutions and optimizing materials for high-density, low-power electronic devices. [ABSTRACT FROM AUTHOR]

This article explicitly models multi-level failure dependence existing in continuous degrading systems with multiple degrading components. Dependence within the component level is specified as that arrived shocks increase the vulnerability to inherent degradation. Dependence among components refers to the fact that the degradation of a particular component can be accelerated by others. The Gamma process is employed as a comprehensive framework for modeling the dependence above. A compound Poisson process and normal distribution are both introduced to describe the stochastic characteristics of random shocks. Procedures for obtaining analytical solutions and simulation results of reliability analysis are both presented. At last, an illustrative example of a hydraulic system is studied to demonstrate the effectiveness and advantages of the proposed method. [ABSTRACT FROM AUTHOR]

Active-learning Kriging (AK) was developed as a surrogate-aided reliability technique to address the need for efficient reliability estimation when assessing complex limit states. The results of AK analyses are sensitive to the choice of the regression function, correlation function, learning function and associated stopping criteria, and reliability estimation technique, with unique sets of these input parameters referred to as AK configurations. For the reliable use of AK analysis in bridge reliability assessment, recommendations regarding the best-performing AK configurations are needed to balance the desired accuracy-to-efficiency of the simulation. The objective of this study was to recommend sets of AK configurations for the reliability analysis of reinforced-concrete bridge girders and piers that can be readily used by engineers to perform AK analysis for bridge design optimization and assessment. An extensive parametric analysis, using 432 unique AK configurations and over 3,000 AK analyses, was performed, combined with the application of a comprehensive metric system to recommend the top five best-performing AK configurations for bridge analysis based on the root mean square error, the absolute average error, the degree of consistency, and total number of training points. [ABSTRACT FROM AUTHOR]

In slope stability analysis, it is crucial to take into account the inherent spatial variation (ISV) in the soil properties as it affects the analysis and design of the slope. The majority of research work employs a probabilistic based approach to incorporate the variability of soil parameters using a random field to evaluate the reliability analysis (RA) of slope. The reliability of a slope can be evaluated by various methods such as First-order second moment, Monte Carlo simulation (MCS) and Subset simulation (SS). The main drawback of MCS is that, the efficiency of this method is very less for the problems having small failure probability. The SS method can be used more efficiently to estimate the slope reliability at small failure probability. The approach is demonstrated in this paper using a finite soil slope having ISV in the cohesion and angle of internal friction with the depth of soil. The RA has been performed in a MS-Excel spreadsheet platform using Ordinary method of slices. The study investigates the effect of ISV and correlation length (λ) on the probability of failure (P f) of the soil slope. The P f and reliability index (β) of the soil slope is evaluated using the above-mentioned methods. It is shown that SS method uses many fewer samples to obtain a given accuracy as compared to the MCS. In addition, this method ensures that samples are generated in the failure region, which is not always achievable with the MCS method. Also, the result shows that the proposed method accurately calculates the slope reliability and greatly improves the efficiency at small failure probability levels, while taking into consideration the ISV in soil properties. [ABSTRACT FROM AUTHOR]

The development of bivariate distributions is a challenging area of research, and a novel class of bivariate distributions is proposed in this paper. Certain useful mathematical characteristics of the proposed class of bivariate distributions are explored in general. These properties include the joint and conditional and joint survival function moments and measures of dependence. The parameters of the proposed class of distributions are estimated using the maximum likelihood method of estimation. A multivariate version of the proposed class is also proposed, and some necessary properties are discussed. The multivariate dependence measures are obtained for the proposed multivariate class of distributions. A new bivariate power function distribution is proposed using the power function distribution as a baseline distribution in the proposed bivariate class. Some useful properties of the proposed bivariate distribution are studied. The parameters of the proposed bivariate distribution are estimated using the maximum likelihood method. An extensive simulation study has been conducted to see the performance of the estimation method. The new bivariate power function distribution is used to model some real data. [ABSTRACT FROM AUTHOR]

Urban underground space development has heightened concerns over the safety of deep foundation pit construction. This study conducted time-series monitoring of critical safety-influencing factors and applied the Gaussian copula-based Bayesian network (GCBN) model for comprehensive reliability analysis of deep foundation pit support structures. The GCBN model, integrating the multivariate data management of pair copula with Bayesian network's uncertainty handling, found that building settlement has the greatest impact on the safety of deep foundation pit and revealed a reliability index (β) of 0.44 in an actual case, suggesting a hazardous condition. Based on the reliability index β , emergency measures were promptly taken. Compared to traditional reliability methods, the approach presented in this paper takes into account the dependence among monitoring indicators, which is more aligned with actual engineering conditions and holds significant reference value for the safety assessment of underground engineering structures. [ABSTRACT FROM AUTHOR]

Let G be a connected simple graph with vertex set V (G) and edge set E (G). For any subset η of V (G) with | η | ≥ 2 , let π G (η) denote the maximum number t of paths P 1 , P 2 , ... , P t in G such that P i contains η , V (P i) ∩ V (P j) = η and E (P i) ∩ E (P j) = 0̸ for any distinct i , j ∈ { 1 , 2 , ... , t }. For an integer k with 2 ≤ k ≤ | V (G) | , the k -path connectivity π k (G) of G , which can more accurately assess the reliability of networks, is defined as min { π G (η) | η ⊆ V (G) and | η | = k }. Since deciding whether π G (η) ≥ ℓ for a general graph is NP-complete in [Graphs Combin. 37(2021)2521–2533], there are few results about k -path connectivity even for k = 3. In this paper, we obtain the exact value of the 3-path connectivity of the exchanged hypercube E H (s , t) and show that π 3 (E H (s , t)) = 3 × min { s , t } + 2 4 which improves the known result about the 3-tree connectivity [Appl. Math. Comput. 347(2019)342–353]. As a corollary, the 3-path connectivity of the n -dimensional dual cube D n is obtained directly. [ABSTRACT FROM AUTHOR]

Forecasting flood characteristics (e.g., water levels and velocity) is a growing concern due to climate change. It is therefore necessary to consider the stability conditions of earthen levees used to mitigate floods during a flood risk assessment. This technical note presents a method to assess probabilistic flood hazard that takes into account levee failures, for a levee located along Etobicoke Creek in Toronto, Canada. We compute flood scenario probabilities resulting from multiple flood scenarios that accounts for both the levee failures across the length of the levee, and different levee-failure mechanisms (e.g., backward erosion and overtopping). Then, for each location of the flooded area, we compute a cumulative flood exceedance probability curve for flood depth and velocity. This method provides a flood-hazard map (depth and velocity) for a given probability and probabilistic maps for given values of depth or velocity. [ABSTRACT FROM AUTHOR]

AbstractAs an extension of CO2 replacement method, the depressurization-assisted replacement method can improve extraction efficiency. For these two methods, this paper takes the hole GMGS2-08 as a case study. By integrating infinite slope model and random field theory, this text analyses the impact of three factors on the slope safety, such as the strength differences from different hydrate types and saturations, the strength discounting due to the earthquake, and the seismic addition force. And, the impact degree of the three factors increases in sequence. In the non-stationary random field, this paper sets cohesion (c), friction angle (φ), unit weight (γ) and reduction coefficient (ξ) as four random variables. There is found that the correlation between the c and φ, the cohesion variability, the friction angle variability, the reduction coefficient variability, the cohesion trendiness, the friction angle trendiness, and the unit weight trendiness have a more important impact on the failure probability (Pf) of submarine slops. When the Pf is about 65%, the safety factors with different slopes angles are around 1.05, despite the different effects of seismic forces and strength discounting. The combined analysis of safety factor and the Pf can offer excellent early warning for safe hydrate extraction. [ABSTRACT FROM AUTHOR]

This paper proposed a new generalization of the Samade distribution. The term "area biased samade distribution" refers to the recently created distribution model. After studying the various structural features, entropies, order statistics, moments, generating functions for moments, survival functions, and hazard functions were calculated. The parameters of the suggested model are estimated using the maximum likelihood estimation technique. Ultimately, a fitting of an application to a real-life blood cancer data set reveals a good fit. [ABSTRACT FROM AUTHOR]

Wave loads are critical factor for the design and safe operation of offshore structures. The accurate determination of these loads is essential to ensure the structural reliability and operational efficiency of such platforms at sea. This study develops analytical expressions for calculating wave loadings that affect the support of various Condeep-type offshore structures. In this regard, wave load calculations for the Draugen Monopile Condeep platform, previously constructed in Norway, were analyzed in the context of a case study. The results of this assessment provide useful information regarding the characteristics of wave loads and their relevance to the overall structural analysis. Furthermore, the investigation also covers recommendations for design and safety improvements that consider the calculated wave loads and the assessment of the structural reliability. Study is expected to contribute to the knowledge base surrounding offshore engineering practices and improve resilience and functionality against dynamic wave forces. [ABSTRACT FROM AUTHOR]

The paper provides an introduction to the concept of Probabilistic Safety Assessment, an evaluation of its recent developments, and perspectives on the future research directions in this area. To do so, a conceptual understanding to safety assessment is first provided, followed by an introduction to what Probabilistic Safety Assessment is about. From this, the historical background and development of Probabilistic Safety Assessment in the context of nuclear safety are discussed, including a brief description and evaluation of some methods implemented to perform such analysis. After this, the paper reviews some of the recent research developments in Probabilistic Safety Assessment in the aspects of multi-unit safety assessment, dynamic Probabilistic Safety Assessment, reliability analysis, cyber-security, and policy-making. Each aspect is elaborated in detail, with perspectives provided on its potential limitations. Finally, the paper discusses research topics in six areas and challenges within the Probabilistic Safety Assessment discipline, for which further investigation might be conducted in the future. Hence, the objectives of the review paper are (1) to serve as a tutorial for readers who are new to the concept of Probabilistic Safety Assessment; (2) to provide a historical perspective on the development of the Probabilistic Safety Assessment field over the past seven decades; (3) to review the state-of-the-art developments in the use of Probabilistic Safety Assessment in the context of nuclear safety; (4) to provide an evaluative perspective on the methods implemented for Probabilistic Safety Assessment within the current literature; and (5) to provide perspectives on the future research directions that can potentially be explored, thereby also targeting the wider research community within the nuclear safety discipline towards pushing the frontiers of Probabilistic Safety Assessment research. [ABSTRACT FROM AUTHOR]

This study addresses the challenges of estimating decay times for chemical components, focusing on hydroxylated fullerene C 60 (O H) 29 , which poses potential environmental risks due to its persistence and transformation in soil. Given the complexities of real-world experiments such as limited sample availability, time constraints, and the need for efficient resource use, a framework using the Gamma distribution based on hybrid Type-II censoring schemes was developed to model the decay time. The Gamma distribution's flexibility and mathematical properties make it well-suited for reliability and decay analysis, capturing variable hazard rates and accommodating different censoring structures. We employ maximum likelihood estimation (MLE) and Bayesian methods to estimate the model's parameters, consequently estimating the reliability and hazard functions. The large sample theory for MLE is used to approximate variances for constructing asymptotic confidence intervals. Additionally, we utilize the Markov chain Monte Carlo technique within the Bayesian framework to ensure robust parameter estimation. Through simulation studies and statistical tests—such as Chi-Square, Kolmogorov–Smirnov, and others—we assess the Gamma distribution's fit and compare its performance with other distributions, validating the proposed model's effectiveness. [ABSTRACT FROM AUTHOR]

The increasing demand for renewable energy sources indicates that an increasing number of single-family homeowners are choosing photovoltaic installations. These systems facilitate self-sufficient electricity production, increasing the energy independence of households. Although the prevalence and advancements in this field are growing, users frequently experience technical problems and failures that can affect the efficiency of the installation and the satisfaction with their use. The aim of this paper is to analyze the problems encountered by users of photovoltaic installations in single-family buildings. The study relies on user feedback, enabling the identification of the most frequently occurring issues from their viewpoint and the assessment of the reliability of the systems. While the analysis does not allow for a direct technical diagnosis of faults, the results obtained from the surveys provide valuable information on the frequency of problems and the elements of the system that require special attention. On this basis, conclusions can be drawn that can support further work on improving the reliability of the installation. The presented analysis includes not only specific failures, but also the overall satisfaction of users. [ABSTRACT FROM AUTHOR]

Summary: This study evaluates the performance of two major artificial intelligence‐based tools (ChatGPT‐4 and Google Bard) in debunking sleep‐related myths. More in detail, the present research assessed 20 sleep misconceptions using a 5‐point Likert scale for falseness and public health significance, comparing responses of artificial intelligence tools with expert opinions. The results indicated that Google Bard correctly identified 19 out of 20 statements as false (95.0% accuracy), not differing from ChatGPT‐4 (85.0% accuracy, Fisher's exact test p = 0.615). Google Bard's ratings of the falseness of the sleep misconceptions averaged 4.25 ± 0.70, showing a moderately negative skewness (−0.42) and kurtosis (−0.83), and suggesting a distribution with fewer extreme values compared with ChatGPT‐4. In assessing public health significance, Google Bard's mean score was 2.4 ± 0.80, with skewness and kurtosis of 0.36 and −0.07, respectively, indicating a more normal distribution compared with ChatGPT‐4. The inter‐rater agreement between Google Bard and sleep experts had an intra‐class correlation coefficient of 0.58 for falseness and 0.69 for public health significance, showing moderate alignment (p = 0.065 and p = 0.014, respectively). Text‐mining analysis revealed Google Bard's focus on practical advice, while ChatGPT‐4 concentrated on theoretical aspects of sleep. The readability analysis suggested Google Bard's responses were more accessible, aligning with 8th‐grade level material, versus ChatGPT‐4's 12th‐grade level complexity. The study demonstrates the potential of artificial intelligence in public health education, especially in sleep health, and underscores the importance of accurate, reliable artificial intelligence‐generated information, calling for further collaboration between artificial intelligence developers, sleep health professionals and educators to enhance the effectiveness of sleep health promotion. [ABSTRACT FROM AUTHOR]

Accurately analyzing the reliability of driveshaft systems is crucial in engineering vehicles and mechanical equipment. A complex system reliability modeling and analysis method based on a dynamic Bayesian network (DBN) is proposed to repair accurately and reduce the cost in time. Considering the logical structure of the drive shaft system, the reliability block diagram (RBD) of the manufacturing system is constructed in a hierarchical and graded manner, and a method of obtaining the Bayesian network (BN) directly from the RBD is adopted based on the conversion relationship between the RBD, fault tree and BN. A variable‐structure DBN model of the system is constructed based on a static BN extended in time series and incorporating dynamic reliability parameters of the components. Reliability analyses based on DBN reasoning, including reliability assessment, significance metrics, and sensitivity analyses, were performed to identify critical subsystems and critical components. This research contributes to enhancing product reliability, equipment utilization, and improving economic efficiency. [ABSTRACT FROM AUTHOR]

A novel structural reliability analysis method that combines the improved beluga whale optimization (IBWO) and the arctangent function‐based maximum entropy method (AMEM) is proposed in this paper. It aims to augment the accuracy of failure probability prediction in structural reliability analysis based on the traditional maximum entropy method (MEM). First, the arctangent function is introduced to avoid the effects of truncation error and numerical overflow in the traditional MEM. The arctangent function can nonlinearly transform the structural performance function defined on the infinite interval into a transformed performance function defined on the bounded interval. Subsequently, the undetermined Lagrange multipliers in the maximum entropy probability density function (MEPDF) of the transformed performance function are obtained using IBWO at a swifter convergence speed with heightened convergence accuracy. Finally, the MEPDF of the transformed performance function can be obtained by combining the IBWO and AMEM, and the structural failure probability can be predicted. The analysis of the metro bogie frame as an engineering example reveals that compared with the traditional MEM using the genetic algorithm to solve the Lagrange multipliers, the proposed method diminishes the relative error in failure probability prediction from 20.51% to only 0.09%. This method significantly enhances the prediction accuracy of failure probability. [ABSTRACT FROM AUTHOR]

This paper introduces the exponentiated Ailamujia distribution (EAD), extending the Ailamujia distribution (AD) to offer greater versatility in modeling positively skewed data. The core contribution lies in the rigorous derivation and examination of EAD's reliability functions, including failure rate, survival, and mean waiting time functions, among others. Our analysis reveals novel algebraic expressions that deepen the understanding of these functions' behavior under varying conditions. Key findings include the identification of the increasing failure rate and mean waiting time (MWT) functions, contrasted by the decreasing mean residual life (MRL) function. We provide a thorough parameter estimation framework for EAD within both classical and Bayesian contexts, introducing innovative comparisons across different estimators. Through a Monte Carlo simulation study, we establish the superior accuracy of Bayesian estimators (BEs) over Maximum Likelihood Estimators (MLEs) and Generalized Maximum Likelihood Estimators (GMLEs). These results are not only theoretically significant but also practically impactful, as evidenced by the application of EAD to real-world data, particularly active repair times for an airborne communication transceiver, where EAD outperforms other one- and two-parameter existing models. [ABSTRACT FROM AUTHOR]

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Donghua University (Natural Science Edition) is the property of Journal of Donghua University (Natural Science) Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The design of structures to resist progressive collapse primarily focuses on enhancing structural safety and robustness. However, given the low probability of accidental events, such designs often lead to a negative cost–benefit. To address this problem, this paper uses risk analysis to optimize the progressive collapse resistance design of steel frame structures. The elements' cross-section design for the progressive collapse resistance of steel frame structures is optimized using genetic algorithms and SAP2000 23, which identify the structural model with the minimum robustness index while ensuring safety. The results show that the risk-based robustness index can effectively assess the cost of progressive collapse design. More importantly, the optimization model can rapidly identify the most cost-effective structural design solution that complies with progressive collapse resistance guidelines, enhancing the simplicity and usability of the structure design optimization process. Additionally, the integration of the SAP2000 API with Python 3.8 automation streamlines the parameterization process, minimizes manual errors, and enhances the precision and efficiency of the structural design optimization. Finally, the model's effectiveness is validated through a case study, where the refined single-frame structure shows a reduction in initial construction and collapse-related costs by 2.4% and 9.1%, respectively. Meanwhile, the three-dimensional frame shows a 2.9% rise in initial costs but a 13.5% decrease in total collapse-resistant design costs, illustrating the model's ability to balance safety with cost-effectiveness. [ABSTRACT FROM AUTHOR]

Unsupported excavations are frequently performed in several geological and geotechnical projects, particularly for constructing roads and railways, and they are often carried out in different materials. The design of such cuts in soils needs the determination of representative values of its mechanical properties, particularly of the strength parameters, and the application of adequate safety factors. The procedure should ensure a sustainable design of those cuts, allowing for economical solutions that guarantee a low probability of geological–geotechnical failure. This paper assesses the reliability of unsupported cuts in soils, under drained conditions, assuming a Mohr–Coulomb strength criterion. Statistical meshes are generated considering the spatial variability of the friction angle and of the true effective cohesion, which are assumed to be uncorrelated. In this process, typical values of the coefficients of variation and of the horizontal and vertical scales of fluctuation are applied. Soil characterisation is simulated in each statistical mesh, and the characteristic values of the strength parameters are determined using statistical methods. Unsupported cuts of different heights and inclinations are designed using typical safety factors. Slope stability analyses are carried out using Random Finite Element Limit Analysis. The uncertainty in the actions is considered, and the probability of failure is determined by direct reliability analysis. The results show the relevance of the ratio between the scale of fluctuation and the excavation depth, the slope inclination, and the characteristic value of the soil strength parameters on the probability of failure. Values of adequate safety factors are proposed towards obtaining an appropriate probability of failure, compatible with the sustainable design of the cuts. [ABSTRACT FROM AUTHOR]

We developed a method to evaluate the failure rate (FR) for the cylindrical part of a composite high-pressure hydrogen vessel with arbitrary dimensions, number of layers, and stacking sequences by giving the mean and standard deviation of the fiber strength development rate and the stiffness reduction rate under various damage types. The FR is the rate at which the residual strength of a vessel is below the maximum service pressure (125%NWP). Assuming a case in which the vessel is designed with the damage-tolerance design, the FR was calculated for the case of all-layer matrix cracking (assuming beginning-of-life) and fiber failure in two layers in addition to the matrix cracking (assuming the most severe damage that is tolerable during service). The observations demonstrate the importance of improving the probability of detection for non-destructive testing in ensuring the safety of damage-tolerance-designed vessels after fiber failure. The mean strength of the fibers mainly affected the mean residual strength of the composite high-pressure hydrogen vessels when the dimensions and stacking sequence remained unchanged. [ABSTRACT FROM AUTHOR]

With the increasing standards of safety management in nuclear power plants, Living Probabilistic Safety Assessment (Living PSA) technology has begin to play an increasingly important role in their operation. This paper aims to provide an overview of the application and development of Living Probabilistic Safety Assessment (Living PSA) technology in nuclear power plant safety monitoring and risk assessment, examining the key technologies and future challenges. Initially, we summarize the current safety needs in regard to nuclear power, examine the policy on configuration risk management technology for nuclear power plants, and outline its importance and development process in nuclear power plant safety management. Subsequently, we discuss the basic principle of Living PSAs and the working method of risk monitoring based on Living PSAs, including information monitoring data collection, online identification, real-time model updating, and risk calculation. Within the Living PSA framework, model development is not merely about creating a theoretical or static representation; it is a dynamic and ongoing process that involves a deep understanding and precise simulation of the behavior of nuclear power plant systems and components. This represents the main research efforts in Living PSAs at present. Additionally, this paper identifies the key technologies of Living PSAs in an in-depth manner, such as the reliability-model-updating technology and model building in dynamic reliability analyses, including the fault tree model, multi-layer flow model, GO-FLOW model etc. The paper lists the work of some scholars in this area in recent years, which helps readers and researchers to clearly understand the current progress of Living PSA technologies in terms of model establishment and updating. Finally, the paper summarizes the challenges and future development of Living PSA and emphasizes the possible problems in data quality, human factor engineering, and the development of Living PSA technologies in the future. In the future, Living PSAs will provide more solid support for the realization of safer and more economical methods of operating nuclear power plants. [ABSTRACT FROM AUTHOR]