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This paper pertains to the reliability analysis of transmission tower foundations under variable wind loads. The reliability analysis of the anchor foundation, frequently employed for transmission towers due to their huge uplift capacity at shallow depth, is presented using a systematic probabilistic approach. The paper presents the design of an anchor foundation for a typical transmission tower based on loads obtained by applying lateral wind velocity on the tower. The Multivariate Adaptive Regression Splines (MARS) surrogate model is first developed to construct a deterministic prediction model for the anchor uplift capacity. The developed model is further used in the reliability analysis of the anchor foundation considering variabilities in the soil properties and the uplift load, and the results of the probabilistic analysis are reported in the paper. The results indicate the importance of incorporating variability in design variables for the proper safety of the structure. Soil cohesion is found to be the most sensitive parameter in the uplift capacity of the tower foundation. The comparison of the deterministic factor of safety and failure probability of the foundation brings out the importance of the reliability-based design methodology. [ABSTRACT FROM AUTHOR]

Technological advancements and the expanding scope of offshore dynamic positioning (DP) applications introduce new challenges in terms of safety and risk control. This paper provides an overview of a proposed methodology for reliability and risk analysis in the context of DP operations. Firstly, two typical DP operation contexts are discussed. Subsequently, a comprehensive survey is conducted to assess the reliability analysis methodologies applicable to DP systems, encompassing techniques such as Failure Mode and Effects Analysis (FMEA), fault tree analysis, Markov models, and Bayesian networks, which are employed to evaluate the technical and human reliability of DP Class 2 and Class 3 systems. Finally, the paper concludes with a discussion of future research directions and challenges pertaining to reliability analysis and risk management in DP operations. [ABSTRACT FROM AUTHOR]

The feeding unit is an important functional part in the paper industry that requires the development of effective maintenance plan based on its reliability analysis. This study analyzes reliability of the considered unit employing trapezoidal fuzzy numbers in fuzzy λ – τ methodology with Petri net modeling. In this analysis, the collected crisp input data of each subsystem has been fuzzified using trapezoidal fuzzy numbers. Then fuzzy values of several reliability factors of feeding unit, namely, failure rate, repair time, reliability, availability, etc. at ±15%, ±25% and ±40% spread levels have been calculated. Further, the obtained fuzzy values of reliability factors have been defuzzified to analyze system behavior of feeding unit. The analysis is useful for plant maintenance and operation management to understand the behavior of feeding unit in a more realistic manner. [ABSTRACT FROM AUTHOR]

The use of UHPC as an overlay and repair material in the bridge industry has been increasing recently. Ensuring sufficient interface shear strength between the substrate and UHPC is necessary for adequate performance and the structural integrity of the composite section. Due to the lack of structural design code for UHPC members, designers often rely on experimental data developed by researchers or on existing conventional concrete design code to predict the interface shear capacity of sections containing UHPC. The various test methods currently used to quantify the interface shear strength oftentimes produce different results. The objectives of this paper are to create a database of the studies on the interface shear strength of UHPC members available in the literature and carry out a statistical assessment. Moreover, a reliability analysis is conducted on the collected experimental database, and the probability of failure is determined for UHPC–concrete, UHPC–UHPC, and monolithic UHPC interfaces. The paper also investigates the dependence of the reliability index on two different test methods used for interface shear capacity prediction. Additionally, a simpler interface shear capacity model with readily determined parameters is proposed for the monolithic UHPC interface, with a better reliability index compared to current design specification. [ABSTRACT FROM AUTHOR]

This paper focuses on predictive maintenance for simple machinery systems monitored by the Internet of Things (IoT). As these systems can be challenging to model due to their complexity, diverse typologies, and limited operational lifespans, traditional predictive maintenance approaches face obstacles due to the lack of extensive historical data. To address this issue, we propose a novel clustering-based process that identifies potential machinery faults. The proposed approach lies in empowering decision-makers to define predictive maintenance policies based on the reliability of the proposed fault classification. Through a case study involving real sensor data from the doors of a transportation vehicle, specifically a bus, we demonstrate the practical applicability and effectiveness of our method in preemptively preventing faults and enhancing maintenance practices. By leveraging IoT sensor data and employing clustering techniques, our approach offers a promising avenue for cost-effective predictive maintenance strategies in simple machinery systems. As part of the quality assurance, a comparison between the predictive maintenance model for a simple machinery system, pattern recognition neural network, and support vector machine approaches has been conducted. For the last two methods, the performance is lower than the first one proposed. [ABSTRACT FROM AUTHOR]

In this paper, a discussion on the applicability and limitations of the limit equilibrium method is presented. In this manner, the reliability of a concrete arch dam-foundation system under static loading is evaluated by considering a set of potentially moveable rock wedges in the foundation. The safety of the system is assessed utilizing a quasi-analytical method, which employs the limit equilibrium method and numerical analysis to calculate the sliding safety factors and the dam trust forces, respectively. The reliability is evaluated using the Latin Hypercube Sampling method. Random variables in the system are the friction angle, cohesion, and the Grout Curtain Efficiency Coefficient. In the end, the influence of two parametric variables of discontinuities, elastic slip and rock mass deformability modulus, on the rock wedges' sliding safety factor is evaluated by comparing the results of the quasi-analytical method with the purely numerical method. The results show that in the case of complicated geotechnical conditions, the limit equilibrium method may not reflect real-world failure scenarios. [ABSTRACT FROM AUTHOR]

During the operation of the gearless wind turbine, the phenomenon of heat generation in the main shaft bearing is inevitable and further affects the assembly preload. It is crucial to determine the effect of the thermal error on bearing assembly preload. In this paper, a reliability analysis method for main shaft bearing is proposed. Firstly, a finite element model for the thermal analysis of wind turbines is established based on heat transfer theory, and the thermal error of the preload is calculated. Subsequently, a reliability analysis of the main shaft bearing is conducted through Quasi-Monte Carlo simulation (QMCS) considering the influence of uncertainty factors. To further improve the computational efficiency, a surrogate model based on stochastic configuration network (SCN) is established to analyze the reliability and sensitivity of the bearing. Finally, the numerical example shows that the proposed model has high accuracy and applicability. [ABSTRACT FROM AUTHOR]

This paper presents a framework for assessing the reliability of fiber reinforced polymer (FRP)–strengthened reinforced concrete (RC) beams in flexure using stochastic nonlinear finite-element (SNFE) analysis and k-w-means clustering, based on active-learning kriging Monte Carlo simulation (AK-MCS), in which spatial variations in the concrete and bond material properties are considered. A computer algorithm was developed to augment commercially available nonlinear finite-element (FE) analysis software and automate the process for conducting the SNFE clustering-based AK-MCS analysis. The k-w-means clustering was based on the U learning function to provide multipoint enrichment to improve convergence of the stopping criteria by allowing parallel computation of the SNFE models. Parametric analysis indicated the accuracy of the reliability prediction of the examined member and proved the efficiency of the proposed analysis in reducing the number of calls to SNFE models compared with data in the existing literature, when using probability-based stopping criteria. Practical Applications: The quality of the FRP-to-concrete bond is affected by the integrity of the concrete at the interface, which varies across the dimensions of the strengthened member, causing added uncertainty in predicting the structural response, and hence the reliability of the FRP-strengthened member. This study proposes a computationally efficient approach to assess the reliability of FRP-strengthened concrete members by considering the spatial variation in the concrete properties (compressive strength, tensile strength, bulk modulus) and the quality of the FRP-to-concrete bond (shear and normal bond strength) by using an adaptive machine-learning technique. The proposed framework may be utilized by engineers to design FRP-strengthening systems for concrete members experiencing variation in the concrete properties due to poor quality control or active deterioration. [ABSTRACT FROM AUTHOR]

Reliability-based design optimization (RBDO) integrates various uncertainties into the design optimization process, offering a more realistic and robust approach compared to traditional deterministic design optimization methods. Thus, RBDO has emerged as a highly compelling and vital research direction within the design field. However, there is currently a dearth of comprehensive reviews on RBDO methodologies presented in a clear and concise manner. This paper aims to address this gap by providing a state-of-the-art review of RBDO methodologies across four key aspects: performance function evaluation, reliability analysis, optimization strategies and algorithms, and RBDO applications in five typical engineering fields. The paper commences by presenting basic RBDO formulations and providing an overall picture of various RBDO methodologies. Subsequently, performance function evaluation methodologies are explained and then categorized into three groups: physics-based performance function evaluation, data-driven performance function evaluation, and physics-informed performance function evaluation. Following this, two types of reliability analysis methodologies are introduced: time-independent reliability analysis and time-dependent reliability analysis. The review also delves into the realm of optimization strategies, with a comprehensive examination of three types: double-loop strategy, single-loop strategy, and decoupling strategy. Moreover, two types of optimization algorithms, the gradient-based algorithm and the meta-heuristic algorithm, are extensively surveyed. Each is scrutinized in terms of their specific methods, advantages, and disadvantages. In addition to methodological exploration, the paper scrutinizes RBDO applications in five engineering fields: wind engineering, aeronautical engineering, ocean engineering, bridge engineering, and vehicle engineering. These applications are carefully surveyed regarding the various uncertainties encountered, the methods employed, and the results of specific RBDO problems. The paper concludes by summarizing key challenges and charting the future work of RBDO research. It offers valuable insights that draw from the analysis of 174 surveyed papers, enabling readers to gain a comprehensive understanding of RBDO theories and facilitating the proper selection and development of appropriate methods for different RBDO stages and problems in diverse engineering contexts. [ABSTRACT FROM AUTHOR]

Model-Based Systems Engineering (MBSE) supports the system-level design of complex products effectively. Currently, system design and optimization for complex products are two distinct processes that must be executed using different software or platforms, involving intricate data conversion processes. Applying multidisciplinary optimization to validate system optimization often necessitates remodeling the optimization objects, which is time-consuming, labor-intensive, and highly error-prone. A critical activity in systems engineering is identifying the optimal design solution for the entire system. Multidisciplinary Design Optimization (MDO) and reliability analysis are essential methods for achieving this. This paper proposes a SysML-based multidisciplinary reliability design optimization modeling method. First, by analyzing the definitions and mathematical models of multidisciplinary reliability design optimization, the SysML extension mechanism is employed to represent the optimization model based on SysML. Next, model transformation techniques are used to convert the SysML optimization model generated in the first stage into an XML description model readable by optimization solvers. Finally, the proposed method's effectiveness is validated through an engineering case study of an in-vehicle environmental control integration system. The results demonstrate that this method fully utilizes SysML to express MDO problems, enhancing the efficiency of design optimization for complex systems. Engineers and system designers working on complex, multidisciplinary projects can particularly benefit from these advancements, as they simplify the integration of design and optimization processes, facilitating more reliable and efficient product development. [ABSTRACT FROM AUTHOR]

This study addresses the challenges of accurately analyzing the reliability of aviation power systems (APS) using traditional models by introducing the Aviation Power System Reliability Probability Network Model (APS-RPNM). The model directly transforms the system architecture into an equivalent probability network, aiming to develop a precise reliability model that captures system functions and fault logic. By classifying APS components into five distinct structural patterns and mapping them to corresponding nodes in the APS-RPNM, the model is successfully constructed. Specifically, None-Input-to-Multiple-Output components are transformed into two-state nodes, while Multiple-Input-to-None-Output, Single-Input-to-Multiple-Output, and Multiple-Input-to-Single-Output components are mapped to three-state nodes. For Multiple-Input-to-Multiple-Output components, a novel approach employing multiple two-state sub-nodes is adopted to capture their complex functional logic. A case study comparing the performance of the APS-RPNM with the traditional minimal path set method in reliability analysis was conducted. The results demonstrate that the APS-RPNM not only simplifies the model construction process and eliminates errors stemming from subjective engineering judgments but also enables the efficient computation of power supply reliability for all load points in a single inference by integrating all of the components. This significantly improves computational efficiency and system dependency analysis capabilities, highlighting the APS-RPNM's tremendous potential in optimizing the reliability design of APS. [ABSTRACT FROM AUTHOR]

As offshore wind farms move into deeper waters, around 80 m, the high costs necessitate replacing bottom-fixed turbines with floating offshore wind turbines, which require mooring systems to maintain stability within design limits. Data from previous projects in China indicate that mooring systems can constitute about 20% of the total investment. Thus, reducing mooring system costs can significantly benefit the development of next-generation floating wind farms. This paper discusses the reliability analysis of mooring chains for floating wind turbines to optimize inspection plans and strategies, thereby saving on maintenance costs over their design lifetime. A case study on S-N curve based fatigue reliability analysis is conducted using both Monte Carlo Simulation and First Order Reliability Method (FORM), with consistent results from both methods. Additionally, three sensitivity analysis cases identify key parameters for the fatigue reliability analysis. [ABSTRACT FROM AUTHOR]

In this paper, we generalize two new statistical distributions, to improve the ability to model failure rates with non-monotonic, monotonic, and mainly bathtub curve behaviors. We call these distributions Generalized Powered Uniform Distribution and MOE-Powered Uniform. The proposed distributions' approach is based on incorporating a parameter k in the power of the values of the random variables, which is associated with the Probability Density Function and includes an operator called the Powered Mean. Various statistical and mathematical features focused on reliability analysis are presented and discussed, to make the models attractive to reliability engineering or medicine specialists. We employed the Maximum Likelihood Estimator method to estimate the model parameters and we analyzed its performance through a Monte Carlo simulation study. To demonstrate the flexibility of the proposed approach, a comparative analysis was carried out on four case studies with the proposed MOE-Powered Uniform distribution, which can model failure times as a bathtub curve. The results showed that this new model is more flexible and useful for performing reliability analysis. [ABSTRACT FROM AUTHOR]

This paper proposes a multi-material topology optimization method based on the hybrid reliability of the probability-ellipsoid model with stress constraint for the stochastic uncertainty and epistemic uncertainty of mechanical loads in optimization design. The probabilistic model is combined with the ellipsoidal model to describe the uncertainty of mechanical loads. The topology optimization formula is combined with the ordered solid isotropic material with penalization (ordered-SIMP) multi-material interpolation model. The stresses of all elements are integrated into a global stress measurement that approximates the maximum stress using the normalized p-norm function. Furthermore, the sequential optimization and reliability assessment (SORA) is applied to transform the original uncertainty optimization problem into an equivalent deterministic topology optimization (DTO) problem. Stochastic response surface and sparse grid technique are combined with SORA to get accurate information on the most probable failure point (MPP). In each cycle, the equivalent topology optimization formula is updated according to the MPP information obtained in the previous cycle. The adjoint variable method is used for deriving the sensitivity of the stress constraint and the moving asymptote method (MMA) is used to update design variables. Finally, the validity and feasibility of the method are verified by the numerical example of L-shape beam design, T-shape structure design, steering knuckle, and 3D T-shaped beam. [ABSTRACT FROM AUTHOR]

The global shear capacity of steel–concrete composite downstand cellular beams with precast hollow-core units is an important calculation as it affects the span-to-depth ratios and the amount of material used, hence affecting the embodied CO2 calculation when designers are producing floor grids. This paper presents a reliable tool that can be used by designers to alter and optimise grip options during the preliminary design stages, without the need to run onerous calculations. The global shear capacity prediction formula is developed using five machine learning models. First, a finite element model database is developed. The influence of the opening diameter, web opening spacing, tee-section height, concrete topping thickness, interaction degree, and the number of shear studs above the web opening are investigated. Reliability analysis is conducted to assess the design method and propose new partial safety factors. The Catboost regressor algorithm presented better accuracy compared to the other algorithms. An equation to predict the shear capacity of composite cellular beams with hollow-core units is proposed using gene expression programming. In general, the partial safety factor for resistance, according to the reliability analysis, varied between 1.25 and 1.26. [ABSTRACT FROM AUTHOR]

In recent years, the high-strength friction-grip bolt (HSFGB) shear connectors in steel-concrete composite beams as deconstructable connectors are of great research interest since they are aligned with sustainable construction. However, the design strength equation for this type of connector has not yet been provided by the design codes such as AISC and Eurocode 4. In this paper, with regard to the reliability aspect, the design strength equation of HSFGB shear connectors is investigated which is projected into a partial safety factor according to Eurocode 4. Accordingly, the three-dimension finite element (FE) push-out model of the composite connection is developed and verified via experimental results to predict the load-slip behavior and failure modes in this type of bolted shear connector. Then One-Factor-at-a-Time (OFAT) method as a sensitivity analysis and Plackett-Burman (PB) design method as a screening analysis are utilized to identify the effective factors of the ultimate strength of HSFGB connectors. Finally, based on a complete database of all existing experimental findings, the results obtained from 55 three-dimension finite element models conducted in this paper, and appropriate statistical analysis, a high-precision equation for predicting ultimate strength is provided. Then the value of the partial safety factor is determined through a reliability study in accordance with EN1990, which provides an acceptable level of safety for the proposed design equation based on Eurocodes. Reliability analysis found that a partial factor of 1.52 was justified for the design strength of HSFGB connectors, which differed from the partial factor of 1.25 stated in the EC for welded stud shear connectors. [ABSTRACT FROM AUTHOR]

In this study, we explore a new probability distribution termed as the Length-Biased Transmuted Mukherjee-Islam (LBTMI) distribution. This exploration enhances the conventional Transmuted Mukherjee-Islam distribution by integrating a weighted transformation approach. The paper examines the probability density function and the corresponding cumulative distribution function associated with the LBTMI distribution. A comprehensive examination of the unique structural properties of the proposed model is carried out, including the survival function, conditional survival function, hazard function, cumulative hazard function, mean residual life, moments, moment generating function (MGF), characteristic function (CF), cumulant generating function (CGF), likelihood ratio test, ordered statistics, entropy measures, and Bonferroni and Lorenz curves. To ensure precise estimation of model parameters, the study employs the maximum likelihood estimation method, contributing significantly to the advancement of statistical modelling in this domain. [ABSTRACT FROM AUTHOR]

In this study, we employ a weighted transformation approach to introduce a novel model that generalises the Transmuted Mukherjee-Islam distribution. The resulting generalized distribution is referred to as the Weighted Transmuted Mukherjee-Islam (WTMI) distribution The paper thoroughly explores the probability density function (PDF) and the corresponding cumulative distribution function (CDF) associated with the WTMI distribution. A thorough investigation of the distinctive structural properties of the proposed model is conducted, including survival function, conditional survival function, hazard function, cumulative hazard function, mean residual life, moments, moment generating function (MGF), characteristics function (CF), cumulant generating function (CGF), likelihood ratio test, ordered statistics, entropy measures, and Bonferroni and Lorenz curves. The maximum likelihood estimation method is employed for the precise estimation of model parameters. [ABSTRACT FROM AUTHOR]

Offshore floating structures rely heavily on their mooring systems, which can be disrupted by various events during long‐term operation. These could lead to a mooring failure, affecting the usual operations of the structure or even causing more severe hazards. Resilience provides a comprehensive evaluation of how the mooring system performs after a disaster, which is key to optimizing the structural design and operational safety. In this paper, we develop a general and user‐friendly method to quantitatively assess the resilience of mooring systems under mooring failure. We use the reliability index to represent the performance of the mooring system. We then derive its RV, ACI, and RCI, which are based on a system performance curve and reliability analysis. We also consider the effects of climate change and the corrosion of the mooring chain. These factors can significantly affect environmental loads, structural performance, and the recovery process. Moreover, an illustrative example is provided that guides us through the methodology. The proposed method is applied to assess the resilience of a certain mooring system in the South China Sea over a 30‐year service life under different failure scenarios. Our results indicate that overlooking climate change in the design and operation of the mooring system can lead to a significant overestimation of its reliability index and resilience value. [ABSTRACT FROM AUTHOR]

Historically, seepage failure has always been the primary cause of failures and accidents of earth dams after overtopping. However, research on the reliability of earth dams against seepage failure remains scarce. Meanwhile, the influence of the uncertainty of the soil–water characteristic curve (SWCC) on the reliability of dams against seepage failure is still unclear. In this study, the van Genuchten–Mualem model is used to describe SWCC, and its uncertainty is characterized by discretizing SWCC fitting parameters a and n into cross-correlation lognormal random fields. Based on the random finite element method, this paper systematically studies the influence of the heterogeneity, autocorrelation, and cross-correlation of hydraulic parameters Ks, a, and n on the reliability of a dam against seepage failure. Sensitivity analysis indicates that Ks and autocorrelation distance crucially affect the reliability of the dam against seepage failure. Additionally, this paper also emphasizes and proves that considering the correlation between the hydraulic conductivity field and the critical hydraulic gradient field has a significant impact on evaluating the reliability of earth dams against seepage failure. However, this subtle but important potential characteristic has not been paid attention to in previous studies. [ABSTRACT FROM AUTHOR]

The study presents a comparative analysis of the reliability of marine and industrial diesel engines, emphasizing the role of heat exchangers. Diesel engines in marine vessels and industrial applications face distinct challenges, influencing their reliability. This paper examines these differences, focusing on operational conditions, load profiles, redundancy, safety measures, and maintenance practices. Three types of heat exchangers (Fin fan, Plate, and Shell & Tube) are analyzed which are used in these engines. The assessment covers failure rates, Mean Time to Failure (MTTF), and the impact of independent and dependent failures on reliability. The study identifies unique failure modes like insufficient heat transfer, external leakage, parameter deviation, and structural deficiencies and their differing impacts in marine and industrial contexts. The research highlights the sensitivity of marine engine heat exchangers to seawaterinduced corrosion and fouling, affecting heat transfer efficiency. In contrast, industrial engines display varying failure characteristics due to system controls and operational parameters. A significant finding is the decrease in reliability over time for all heat exchanger types, underscoring the importance of maintenance and monitoring. Our results show slight shifts in failure rates due to equipment inefficiencies markedly affecting heat exchangers' operational lifecycles. The study concludes with a necessity for tailored maintenance strategies and design considerations for marine and industrial diesel engine heat exchangers. This focused approach offers insights into optimizing diesel engine reliability, particularly by understanding the main role of heat exchangers. [ABSTRACT FROM AUTHOR]

This paper introduces a novel probability distribution known as the Ejaz distribution (ED), which is characterized by two parameters. The study offers a comprehensive analysis of this distribution, including an examination of key properties such as moments, moment-generating functions, order statistics, and reliability functions. Additionally, the paper explores the graphical representation of essential functions like the probability density function, cumulative distribution function, and hazard rate function, enhancing our visual understanding of their behavior. The distribution's parameters are estimated using the widely accepted method of maximum likelihood estimation. Through real-world examples, the paper highlights the practical applicability of the Ejaz distribution, demonstrating its performance and relevance in diverse scenarios. [ABSTRACT FROM AUTHOR]

This paper presents a consecutive k$k$‐out‐of‐n$n$: F$F$ balance system in a shock environment, where the state transition of component is induced by external shocks. If a predetermined threshold number of effective shocks are applied to component in a critical state, the component will fail. The state of the system is defined by the number of consecutive failing component groups in the system, which leads to system failure when a critical number of consecutive failing components is reached. To minimize maintenance costs, we propose a preventive maintenance method with an optimization model. We use finite Markov chain imbedding and Phase‐type distribution to calculate component group failure rates and associated probability functions in discrete and continuous time, respectively. The validity and accuracy of the model are confirmed by numerical examples and Monte Carlo simulations. [ABSTRACT FROM AUTHOR]

Electromagnets are often used in indirect control for industrial applications. The ability of an electromagnet to control objects should decrease with performance degradation. And electromagnets product poses a danger to people and objects in the working environment. So, it is very difficult to analyze the reliability of electromagnetic performance degradation because of the complicated working condition. Failure Mode and Effect Analysis (FMEA) is the most commonly used tool for product reliability analysis. The new version of FMEA uses integer as evaluation value, which cannot represent the hesitation psychology of the evaluator. The Action Priority (AP) table of the FMEA describes the relationship between the evaluation of influencing factors and the risk level of the failure mode, which provides rules for determining the risk level of the failure mode. However, the AP table may result in multiple failure modes having the same ranking, which does not align with the intention of FMEA to prevent failures. Therefore, this paper proposes a reliability analysis method for electromagnetic performance degradation based on FMEA and FIS. Firstly, the Double Hierarchy Hesitant Fuzzy Linguistic Term Set (DHHFLTS) is used as the evaluation language to describe the hesitation psychology of evaluators. Secondly, the AP table of FMEA is used as FIS fuzzy inference rule. In this way, the idea of FMEA to determine the risk level of failure mode is retained and the problem of FIS fuzzy rule making is overcome. Then, FIS defuzzification AP table inference results to determine the risk ranking of failure modes. This avoids situations where the order of failure modes is equal. Finally, a performance degradation model of the electromagnet is constructed based on the Wiener process, and the calculation results of the new method are verified. [ABSTRACT FROM AUTHOR]

Failure probability function (FPF) can reflect quantitative effects of random input distribution parameter (DP) on failure probability, and it is significant for decoupling reliability‐based design optimization (RBDO). But the FPF estimation is time‐consuming since it generally requires repeated reliability analyses at different DPs. For efficiently estimating FPF, an augmented directional sampling (A‐DS) is proposed in this paper. By using the property that the limit state surface (LSS) in physical input space is independent of DP, the A‐DS establishes transformation of LSS samples in standard normal spaces corresponding to different DPs. By the established transformation in different standard normal spaces, the LSS samples obtained by DS at a given DP can be transformed to those at other DPs. After simple interpolation post‐processing on those transformed samples, the failure probability at other DPs can be estimated by DS simultaneously. The main novelty of A‐DS is that a strategy of sharing DS samples is designed for estimating the failure probability at different DPs. The A‐DS avoids repeated reliability analyses and inherits merit of DS suitable for solving problems with multiple failure modes and small failure probability. Compared with other FPF estimation methods, the examples sufficiently verify the accuracy and efficiency of A‐DS. [ABSTRACT FROM AUTHOR]

As an essential mechanical component, a rolling bearing can exhibit multiple failure modes that may occur independently or in correlation with one another. A reliability analysis method that meticulously accounts for the interdependencies among various bearing failure modes is presented in this paper. The examination of wear and fatigue failure mechanisms in rolling bearings is carried out using the Physics of Failure (PoF) approach. By considering the influence of uncertain variables, the limit state functions for individual failure modes are formulated through the application of stress‐strength interference theory. In the context of wear failure, the limit state function is derived using working clearance as the characteristic quantity. On the other hand, the limit state function for fatigue failure is constructed with a focus on fatigue damage accumulation. The Copula function is used to characterize the relationship between wear failure and fatigue failure, and a reliability calculation model for rolling bearings is developed, considering the correlation between these failure modes. Ultimately, the proposed method is utilized to assess the reliability of bearings under two different sets of test conditions. The feasibility of this method is confirmed through test data, demonstrating its effectiveness in predicting bearing reliability. Through the application of this method, engineers can optimize bearing size parameters, select appropriate initial clearances, and enhance the reliability design of bearing. [ABSTRACT FROM AUTHOR]

In this work, a probabilistic analysis of the soil–pile interaction problem is described, taking into account stochastic fields for soil parameters and a parametric analysis of the impact of pile length on the reliability index. The paper's major contribution is based on the notion that a spatial stochastic field represents the random soil nature. According to the obtained results, the inclusion of the hypothesis of soil heterogeneity is the essential feature in the probabilistic analysis of soil behavior, and the corresponding reliability indexes vary significantly with increasing pile length until a certain value, where changes in the reliability index are no longer significant for pile safety. [ABSTRACT FROM AUTHOR]

When the weight function depends on the lengths of the units of interest, the resulting distribution is called length biased. Length biased distribution is thus a special case of the more general form, known as weighted distribution. In this study, we introduce a novel probability distribution named the Length-Biased Sabur distribution (LBSD). This new distribution enhances the traditional Sabur distribution by incorporating a weighted transformation approach. The paper investigates the probability density function (pdf) and the cumulative distribution function (cdf) associated with the LBSD. A thorough examination of the distinctive structural properties of the proposed model is conducted, covering the survival function, conditional survival function, hazard function, cumulative hazard function, mean residual life, moments, moment generating function, characteristic function, likelihood ratio test, ordered statistics, entropy measures, and Bonferroni and Lorenz curve. [ABSTRACT FROM AUTHOR]

Solar photovoltaic (PV) systems are becoming increasingly popular because they offer a sustainable and cost-effective solution for generating electricity. PV panels are the most critical components of PV systems as they convert solar energy into electric energy. Therefore, analyzing their reliability, risk, safety, and degradation is crucial to ensuring continuous electricity generation based on its intended capacity. This paper develops a failure mode and effects analysis (FMEA) methodology to assess the reliability of and risk associated with polycrystalline PV panels. Generalized severity, occurrence, and detection rating criteria are developed that can be used to analyze various solar PV systems as they are or with few modifications. The analysis is based on various data sources, including field failures, literature reviews, testing, and expert evaluations. Generalized severity, occurrence, and detection rating tables are developed and applied to solar panels to estimate the risk priority number (RPN) and the overall risk value. The results show that the encapsulant, junction box, and failures due to external events are the most critical components from both the RPN and risk perspectives. Delamination and soiling are the panels' most critical FMs, with RPN values of 224 and 140, respectively, contributing 16.2% to the total RPN. Further, moderately critical FMs are also identified which contribute 56.3% to the RPN. The encapsulant is the most critical component, with RPN and risk values of 940 (40.30%) and 145 (23.40%), respectively. This work crucially contributes to sustainable energy practices by enhancing the reliability of solar PV systems, thus reducing potential operational inefficiencies. Additionally, recommendations are provided to enhance system reliability and minimize the likelihood and severity of consequences. [ABSTRACT FROM AUTHOR]

In this paper, we introduce a new algorithm for rare event estimation based on adaptive importance sampling. We consider a smoothed version of the optimal importance sampling density, which is approximated by an ensemble of interacting particles. The particle dynamics is governed by a McKean--Vlasov stochastic differential equation, which was introduced and analyzed in [Carrillo et al., Stud. Appl. Math., 148 (2022), pp. 1069-1140] for consensus-based sampling and optimization of posterior distributions arising in the context of Bayesian inverse problems. We develop automatic updates for the internal parameters of our algorithm. This includes a novel time step size controller for the exponential Euler method, which discretizes the particle dynamics. The behavior of all parameter updates depends on easy to interpret accuracy criteria specified by the user. We show in numerical experiments that our method is competitive to state-of-the-art adaptive importance sampling algorithms for rare event estimation, namely a sequential importance sampling method and the ensemble Kalman filter for rare event estimation. [ABSTRACT FROM AUTHOR]

The B4 topology is an interesting alternative to the conventional B6 inverter due to its reduced number of parts and lower cost. Although it has been widely used in the past, especially in low-power motor drive applications, its application as a grid-connected inverter is an open area of research. In this regard, this paper analyses the feasibility of the B4 inverter topology for grid-connected applications. A versatile 7 kW inverter prototype, which may be configured as B4 and B6, was built, allowing for a comprehensive evaluation of the performance of both topologies. Through an analytical study and experimental tests, the performance of the B4 and B6 topologies was comparatively evaluated in terms of efficiency, total harmonic distortion of line currents, current unbalance, cost, and mean time between failures. The study was carried out in the context of microgrid systems, highlighting their role in the integration of renewable energy and distributed generation. [ABSTRACT FROM AUTHOR]

Six-phase motors are becoming more popular because of their advantages such as lower torque ripple, better power distribution per phase, higher efficiency, and fault-tolerant capability compared to three-phase ones. This paper presents the fault-tolerant capability analysis of a symmetrical six-phase induction motor equipped with distributed, conventional concentrated, and pseudo-concentrated windings under opencircuit fault scenarios. For further investigation, different load types such as constant-speed, constant-torque, and constant-power are applied to the motor. Two concepts of magnetic and physical phase separations are introduced as factors affecting motor reliability. Analytically, these factors give an insight into how the pseudo-concentrated winding could be a fault-tolerant alternative. Moreover, five parameters including change of output power, power loss, power factor, efficiency, and expected load loss are considered as fault-tolerant capability parameters to evaluate the windings' reliability. The aforementioned parameters are reported using infinite element analysis for different fault scenarios and load types. Although the baseline motor dimensions are not optimized for applying the pseudo-concentrated winding, the pseudo-concentrated winding shows promising performance with high fault-tolerant capability. [ABSTRACT FROM AUTHOR]

Quantum neural networks (QNNs) leverage the strengths of both quantum computing and neural networks, offering solutions to challenges that are often beyond the reach of traditional neural networks. QNNs are being used in areas such as computer games, function approximation, and big data processing. Moreover, quantum neural network algorithms are finding utility in social network modeling, associative memory systems, and automatic control mechanisms. Nevertheless, ensuring the reliability of quantum neural networks is crucial as it directly influences network performance and stability. To investigate the reliability of quantum neural networks, this paper proposes a methodology wherein operator measurements are performed on the final states of the output quantum states of a quantum neural network. The proximity of these measurements to the target value is compared, and the fidelity value, combined with a quantum gate operation, is utilized to assess the reliability of the quantum neural network. Through network training, the results demonstrate that, under optimal parameters, both the fidelity of the final state measurement value and the target value of the model approach are approximately equal to 1. It indicates that training mitigates the errors stemming from encoding into the initial quantum state, thereby resulting in enhanced system reliability and accuracy. [ABSTRACT FROM AUTHOR]

Analytical probability density function of acceleration random response for cubic nonlinear product package has seldomly been investigated due to the difficulty of solving nonlinear random problem and less attention paid to the acceleration response. In this paper, the analytical solution for the probability density function of linear package acceleration response under random vibration is obtained, and on this basis, the equivalent linearization method is introduced to further explore the approximate analytical solution of acceleration response probability density function for cubic nonlinear product package under random vibration. Some suggestions on packaging optimal design are given by analyzing the sensitivity of acceleration random response to the external excitation and system parameters, and the first-passage failure probability for the acceleration response of cubic nonlinear package under random vibration is analyzed. The results show that the equivalent linearization method can precisely predict the acceleration response probability density function for the cubic nonlinear package under certain conditions, and the applicable range of the method is given by the equivalent stiffness formula. Nonlinear characteristic parameter γ of the cubic package activates acceleration response a completely different trend from the quasi-linear system. Properly increasing the system stiffness and decreasing the damping ratio can effectively reduce the acceleration response strength of the product package. As the vibration time increases and the product fragility decreases, the first-passage failure probability of the product package increases. The analysis provides theoretical foundation and guidance for packaging optimization design. [ABSTRACT FROM AUTHOR]

Temperature and humidity coupling has a more significant effect on the failure properties of bonded joints than a single factor, and there is not enough research on this. In this paper, joints bonded with strong toughness structural adhesives are selected for the experimental analysis of joints aged for 240 h, 480 h, and 720 h at temperatures of 40 °C and 60 °C and a humidity of 95% and 100%. The sequential double Fick's model was used to fit the water absorption of the joints, and the comparison yielded that the water absorption of the adhesive was in accordance with Fick's law. The quasi-static tensile tests revealed that the reduction in mechanical properties of the joints was positively correlated with the moisture content in the environment, while the competing mechanisms of post-temperature curing and hydroplasticization resulted in a slight increase in the failure strength and energy uptake of the aged joints, which is in agreement with the experimental results of the Fourier infrared spectroscopy. A combination of macroscopic failure sections and scanning electron microscope (SEM) images yielded that the failure mode of the joints changed from cohesive failure to interfacial failure with increasing ageing time. In addition, reliability analyses for the fatigue testing of joints are expected to provide guidance for the life design of bonding technology in the vehicle service temperature range. [ABSTRACT FROM AUTHOR]

A major challenge in structural reliability is that computational accuracy and efficiency are hard to balance, especially the time-variant hybrid reliability problem. To improve the reliability analysis efficiency under the condition that the accuracy is assured, this paper develops a new learning function named Regional Learning with Weighted Simulation (RLWS) to estimate the failure probability. The Kriging model is first employed as a response surface to fit the response of limit state function. An adaptive regional learning model as the core of the most probable point is then proposed to update the Kriging model, where the boundary of the updated region is determined by an attenuation function. Furthermore, based on the RLWS model, this paper develops a continuous maximin salp swarm algorithm to calculate the upper bound of the response value for time-variant hybrid performance function. Meanwhile, the Salp Swarm Algorithm is employed to calculate the lower bound of the response value. The Monte Carlo simulation can thus facilitate evaluation based on the final generated Kriging response surface. Several case studies are performed to test and validate the effectiveness of the proposed method and its applicability to practical engineering problems. [ABSTRACT FROM AUTHOR]

The traditional probabilistic reliability analysis method has problems such as poor convergence, low calculation accuracy, and long time consumption in calculating the reliability of concrete arch bridges due to factors such as the uncertainty of the structural parameters and the performance function being highly nonlinear. This paper proposes a method for calculating the reliability of concrete arch bridges based on the Kriging model and particle swarm optimization algorithm (PSOSA) of the simulated annealing algorithm. This method takes advantage of the Kriging model in small samples and high-dimensional nonlinear data processing capabilities and establishes a response surface model to approximate the actual limit state function. The optimization of the PSO algorithm is realized through the self-adaptive and variable probability mutation operation of the SA algorithm, which enhances the ability of the PSO algorithm to get rid of the local minimum, effectively avoids falling into the local minimum, and finally makes the calculation result tend to the global optimum. It overcomes the problems of slow convergence speed and premature maturity of traditional PSO algorithms. The correctness and effectiveness of the method proposed in this paper are verified through the example analysis and the actual engineering application of a concrete arch bridge. The research results show that the method proposed in this paper has obvious advantages in sample size, calculation accuracy, and iteration times compared with the existing reliability calculation methods for concrete arch bridges. This paper provides a fast and effective method for the structural reliability calculation of concrete arch bridges. [ABSTRACT FROM AUTHOR]

The paper focuses on presenting the concept of two-dimensional modeling of passenger car reliability during the warranty period. The main objective of this paper is to detect the regularity in the intensity of the number of first failure reports during the warranty period. The two-dimensional distribution of the time and mileage of failure-free exploitation is estimated. The period from the date of purchase to the first warranty repair is analysed. The concept presented incorporates the existing state of knowledge on two-dimensional warranties, expanding it through the use of a nonparametric approach and probability smoothing with the use of P-splines. The estimation involved censored data, i.e., data on vehicles that were not submitted for warranty repair within the warranty limits of time and mileage. The originality of this paper entails the combination of a nonparametric approach with probability smoothing. The statistical analyses presented in the paper were carried out on a population of 1005 vehicles of two car brands sold and serviced in 2011-2021 at the Authorized Service Station (Dealership). There were sales, repair, and warranty claim databases. [ABSTRACT FROM AUTHOR]

Structural reliability analysis, when accounting for non-parameterized probability box (P-box) uncertainty, typically entails multiple calls to performance functions and poses significant computational hurdles, largely attributable to its inherently nested double-loop structure. Therefore, this paper proposes a new reliability analysis method tailored for structures with uncertain parameters represented using non-parameterized P-boxes. This method leverages double-loop deep learning models to efficiently calculate both the upper and lower bounds of the failure probability. In the development phase of the double-loop deep learning model, an active learning function is devised that integrates the local prediction uncertainty of the deep learning model, based on the K-fold cross-validation principle with the proximity of training samples to candidate sample points. Different stopping criteria are formulated at distinct stages of the model construction process. Firstly, within the inner loop, a deep learning model is established to represent the original performance function in relation to the input parameters. Secondly, based on the inner-loop deep learning model for the performance function, an outer-loop deep learning model is established for the auxiliary response function corresponding to the P-box bound curves of the performance function response with respect to standard uniform distribution variables. Thirdly, utilizing the outer-loop deep learning approximate model, the Monte Carlo simulation technique is employed to compute the upper and lower bounds of the structural failure probability. Finally, the effectiveness of the proposed method is validated through the investigation of two numerical examples and a practical engineering problem. The influence of parameters in the active learning function, threshold values for stopping criteria, and the number of sample points on the computational results is deliberated. [ABSTRACT FROM AUTHOR]

Assessing the risk of hazards in the operation of rail vehicles is an important part of verifying their reliability and safety. This paper presents an example of the application of the Failure Mode and Effects Analysis included in dedicated railway standards, extended to the Failure Mode, Effects and Criticality Analysis with additional criticality factors. This extension allows for the assessment of the risk of failures of assemblies, subassemblies, and components of rail vehicles. Compared to the Failure Mode and Effects Analysis method, the Failure Mode, Effects and Criticality Analysis method introduces the indicators, such as: failure mode criticality, criticality of component, criticality of subassembly, and the criticality of assembly. It enables the more precise identification of critical components, subassemblies, and assemblies of the rail vehicles. This precision is crucial for planning preventive actions and maintenance strategies. The new method can be used to validate the results of classical Failure Mode and Effects Analysis using the indicator of failure mode criticality, which relies on the actual failure frequency function obtained from the reliability model of individual failure modes. This would increase the accuracy of the analysis and allow for a better representation of the system's actual behavior. The extended Failure Mode, Effects and Criticality Analysis method enables a more comprehensive criticality analysis, considering not only the value of criticality indicators but also their quantities in the reliability hierarchical structure of the analyzed wagons. [ABSTRACT FROM AUTHOR]

Cyber‐Physical Systems (CPS) are at the forefront of the intersection between information technology and physical operations, revolutionizing industries such as communication, automation, control, and intelligent transportation. However, evaluating the reliability of CPS, especially in the presence of multilateral random attacks, poses a complex challenge. This paper introduces an inventive probabilistic modeling approach to enhance the analysis of CPS reliability under the influence of multilateral random attacks. We provide an overview of fundamental CPS concepts and their significant role in modern society, followed by a comprehensive exploration of multilateral random attacks. Our novel probabilistic modeling approach is presented as a means to improve the accuracy and robustness of CPS reliability assessments when faced with multilateral random attacks. Numerical experiments and simulations validate the effectiveness of our innovative approach, emphasizing its potential to fortify the reliability of Cyber‐Physical Systems. [ABSTRACT FROM AUTHOR]

During the operational phase, complex systems are subject to various shocks, both random and continuous degradation. This coupling effect poses significant challenges for reliability assessment. The proposed method aims to provide a clear and objective evaluation of the system's reliability. In this paper, we propose a competitive failure reliability analysis method that considers the coupling effect of multiple shock processes and degradation processes. The random shocks are subdivided into external and internal shocks. The degradation increment is mainly influenced by external shocks, whereas internal shocks affect the degradation rate. The combined effect of these two shock processes accelerates the failure processes. Additionally, two different shock models are combined to create an extreme‐cumulative shock model to assess the impact of various shock processes and the degradation process on system reliability. The analysis examines the coupling effect between the degradation process and shock processes to explore the influence law between hard failure and soft failure. Subsequently, the reliability modeling of competitive failure processes is completed. Finally, practical case studies demonstrate the effectiveness and practicality of the proposed method. [ABSTRACT FROM AUTHOR]

In this paper, a probabilistic double linear damage accumulation model is proposed to characterize the uncertainty of damage accumulation using the statistics of material fatigue life. The probabilistic modeling framework is performed under normal distribution assumption of fatigue life. By applying a one‐to‐one probability density function transformation method, the probability distribution of critical damage and cumulative damage can be extracted and derived. The proposed probabilistic damage accumulation model is then used for fatigue reliability analysis by building the reliability formulas with respect to the cumulative damage and the critical damage under both constant and variable amplitude loadings. It treats the mean and variance of cumulative damage as time‐dependent values that dynamically vary with increase in usage life. The reliability can be easily calculated by limited data necessary from fatigue life samples. The applicability of the proposed method is demonstrated using the experimental data of two metallic materials under constant amplitude and two‐block loadings. The results show that the model predictions are in good agreement with the experimental observations under constant amplitude loading and L‐H loading sequence. In addition, the proposed method provides better predictive ability than the Miner rule under constant amplitude loading and H‐L loading sequence. Moreover, the predicted reliability plots can typically present a two‐stage fatigue characteristic during crack initiation and crack propagation periods. [ABSTRACT FROM AUTHOR]

The failure of rock slopes leads to disastrous consequences and thus necessitates their reliability analysis. There are various methods to perform the reliability analysis of a rock slope, viz., conventional, numerical, and Soft Computing (SoCom). However, due to different environmental and loading conditions, there is a need to explore SoCom methods to evaluate the probability of failure of rock slopes. Therefore, in this paper, three soft computing techniques viz., Monte Carlo Simulation (MCS), Generalized Regression Neural Networks (GRNN), and Gaussian Process Regression (GPR) have been proposed to analyze the rock slope stability under various conditions. The variables used in this study were c, ϕ, and σt, and the output was the factor of safety (Fs), which was further utilized for the training and testing of the models. The training and testing of the models were performed on different sets of datapoints (viz., 50, 100, 300, 500, 1000, 5000, 10,000). The MCS algorithm was used to generate various sets of datapoints. Furthermore, statistical parameters were used to assess the performance of the proposed SoCom. A comparative study has been performed to check the adaptability of MCS, GRNN, and GPR models for performing the reliability analysis of rock slopes. Training vs. testing datasets has been plotted to realize the fitting of these models. Furthermore, the observations from Taylor's plot indicate that the MCS, GRNN, and GPR models are capable of predicting the reliability of slope in terms of reliability index; however, GPR outperformed the other two models. The findings imply that the performance assessment of MCS, GRNN, and GPR should also be tried for reliability and risk analysis of other geotechnical and rock engineering problems. [ABSTRACT FROM AUTHOR]

The pivotal problem in reliability analysis is how to use as few actual assessments as possible to obtain an accurate failure probability. Although adaptive Kriging provides a viable method to address this problem, unsatisfied Kriging surrogate accuracy and reliability modeling samples often lead to an unacceptable computing burden. In this paper, an adaptive optimized Kriging combining efficient sampling (AOK‐ES) is proposed: first, to enhance the Kriging approximation ability, a high‐fidelity optimized Kriging model (OKM) is established; further, to ensure the samples quality of OKM modeling and reliability calculation, an improved Latin hypercube sampling method and an optimized importance sampling approach are developed correspondingly. Six different types of case studies demonstrate the superiority of the proposed AOK‐ES. The analysis results demonstrate that the proposed AOK‐ES holds the potential to reduce computing cost while ensuring accuracy. [ABSTRACT FROM AUTHOR]

Turning is one of the most common cutting methods in metal machining. During turning, relative movement occurs between the lathe, the tool, and the workpiece, causing self-excited vibration, i.e., chatter. Moreover, the structural and dynamic parameters of the turning system are uncertain due to the inevitable effect of the processing environment, assembly errors, and other factors. To better predict and prevent turning chatter, it is necessary to analyze the chatter stability and reliability of the turning tool system with its dynamic characteristics to better predict and avoid turning chatter. Despite the importance of chatter prediction in turning, there is a lack of research on the reliability of chatter stability for the turning tool system. In this paper, the dynamic model and the chatter stability model of a turning tool system are established, and the measured parameters are used to analyze the chatter stability of the system. Modal test, static stiffness test, and cutting force tests of the turning system are conducted on a CNC lathe test platform to determine the dynamic parameters of the turning chatter stability analysis. Parameter randomness is considered, and the distribution and characteristic numbers of the parameters are analyzed. The results are consistent with the turning test results, verifying the accuracy of the chatter stability model of the turning tool and machining systems. Furthermore, the chatter reliability model of a turning tool system is established. Since the turning chatter reliability model is more complex and nonlinear, this paper proposes an efficient method for solving the high nonlinear reliability problem is proposed in this paper. The control variable (CV) method is used, and the second-order reliability method is introduced. In addition, a small number of samples generated by Latin hypercube sampling are used to estimate the failure probability by the saddle point approximation method. The applicability of this method is verified by comparison with a variety of methods. The results show that the proposed improved CV method can efficiently analyze the chatter reliability of the turning tool system. [ABSTRACT FROM AUTHOR]

This paper first explores the use of digital technology in health management and the functional modules of digital health management and establishes a digital health system. Then the penetration of digital health in intelligent vocational education in colleges and universities is studied, and different forms of penetration are used according to different teaching contents. Then the health literacy of students in vocational colleges and universities was measured, and the data were analyzed by exploratory factor analysis and reliability analysis to comprehensively assess the level of students' digital health literacy and the influencing factors. Finally, the efficiency of digital health education integration in higher education institutions was analyzed, and the overall efficiency evaluation of basic digital health education teaching in each discipline before SFA correction was obtained. The total score of the scale and the scores of each dimension were positively correlated with the total score of the e-health literacy scale and the scores of each dimension with correlation coefficients > 0.4, indicating that the validity of the calibration correlation of the data was good. The overall efficiency of the process output of digital health education teaching before SFA revision items showed an overall increasing trend with an average annual increase of 16.0%. The research in this paper provides a reference for improving people's digital health and promoting the healthy development of China's digital health service system. [ABSTRACT FROM AUTHOR]

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Due to the importance of both environmental protection and economic development, environmental auditing and accounting is a crucial subject. To assess the acceptable amount of environmental deterioration and the level of development, a detailed analysis of the benefits and costs of environmental harm is required. This paper provides guidelines on economic, social, and environmental issues for green accounting and reporting practises (GARPs) in the Indian automobile industry. A critical aspect in GARP is the implementation and evaluation of key performance indicators (KPIs). The data analysis for this work uses both qualitative and quantitative methodologies. A systematic questionnaire was utilised to gather the data, and barriers were found using the MINITAB analysis of variance (ANOVA) programme. The reliability of the identified KPI was tested using reliability analysis. The weights of KPI are calculated by analytical hierarchical process (AHP) analysis. The study report identified the lack of knowledge and inadequate strategic planning as significant barriers with a mean value of 3.53 and 3.3, and the calculated index value by AHP analysis for economic, social, and environmental factors is 0.364, 0.342, and 0.294. The identified weight of each GARP-KPI helps to automobile industries with self-assessment. It also supports improving the present situation by comparing the organisations on the basis of the KPI of GARP. This method compares the entire GARP performance and serves as a useful manual for an organisation looking to improve its green audit and reporting procedures. [ABSTRACT FROM AUTHOR]

Offshore aquaculture is gaining traction due to space limitations in nearshore waters, more pristine water, cooler temperatures, and better waste dispersal. This move has spurred the development of new technologies for offshore aquaculture. Despite the numerous analysis methods for designing aquaculture infrastructure, limitations and challenges remain in modeling the influence of fish cages on flow fields and in addressing fluid–structure interaction. This paper presents a comprehensive review of analysis methods and modeling techniques applied in the design of offshore aquaculture systems, emphasizing the structural reliability analysis. This review includes statistical and predictive analysis of extreme sea conditions, evaluation of environmental loads and hydrodynamic analysis, structural reliability modeling and assessment, and seabed geotechnical responses to mooring anchors. For each design consideration, the relevant theories and applicability are elaborated upon and discussed. This review provides valuable insights for engineers involved in the development and design of offshore aquaculture infrastructure. [ABSTRACT FROM AUTHOR]