Autonomous Underwater Vehicles(AUVs)are pivotal for deep-sea exploration and resource exploitation,yet their reliability in extreme underwater environments remains a critical barrier to widespread deployment.Through s...Autonomous Underwater Vehicles(AUVs)are pivotal for deep-sea exploration and resource exploitation,yet their reliability in extreme underwater environments remains a critical barrier to widespread deployment.Through systematic analysis of 150 peer-reviewed studies employing mixed-methods research,this review yields three principal advancements to the reliability analysis of AUVs.First,based on the hierarchical functional division of AUVs into six subsystems(propulsion system,navigation system,communication system,power system,environmental detection system,and emergency system),this study systematically identifies the primary failure modes and potential failure causes of each subsystem,providing theoretical support for fault diagnosis and reliability optimization.Subsequently,a comprehensive review of AUV reliability analysis methods is conducted from three perspectives:analytical methods,simulated methods,and surrogate model methods.The applicability and limitations of each method are critically analyzed to offer insights into their suitability for engineering applications.Finally,the study highlights key challenges and research hotpots in AUV reliability analysis,including reliability analysis under limited data,AI-driven reliability analysis,and human reliability analysis.Furthermore,the potential of multi-sensor data fusion,edge computing,and advanced materials in enhancing AUV environmental adaptability and reliability is explored.展开更多
Reliability analysis of soil slopes under rainfall is an important task for landslide risk assessment.Previous studies rarely contribute to the probabilistic analysis of slope stability under rainfall with reinforceme...Reliability analysis of soil slopes under rainfall is an important task for landslide risk assessment.Previous studies rarely contribute to the probabilistic analysis of slope stability under rainfall with reinforcement.A new method is suggested for reliability analysis of soil slopes stabilized with piles under rainfall.First,an efficient numerical model is exploited for slope stability analysis,where two types of slope failure modes,i.e.,plastic flow and local failure are considered.To address the blocking effect of piles during seepage analysis,the equivalent hydraulic conductivity of the pile area is estimated according to the effective medium theory.The stabilizing force of piles is investigated by an analytical approach.For saving computational effort,the response surface is established based on a multi-class classification model to predict two types of slope failure modes.Finally,uncertainties in soil parameters and rainfall events are both modelled,and the failure probability of soil slopes within a given time period is assessed through Monte Carlo simulation.An illustrative example is used to demonstrate the performance of the suggested method.It is found that the slope is mainly controlled by local failure.As the pile spacing increases,the likelihood of plastic flow significantly increases.As the piles are located near the slope crest,plastic flow is effectively prevented and the slope is better stabilized against rainfall.If rainfall uncertainties are not considered,the slope failure probability is significantly overestimated.Overall,this study can provide a useful guidance for the design of pile-stabilized slopes against rainfall infiltration.展开更多
The swinging-loading process is essential for automatic artillery loading systems.This study focuses on the problems of reliability analysis that affect swinging-loading positioning accuracy.A dynamic model for a mult...The swinging-loading process is essential for automatic artillery loading systems.This study focuses on the problems of reliability analysis that affect swinging-loading positioning accuracy.A dynamic model for a multi degree-of-freedom swinging-loading-integrated rigid-flexible coupling system is established.This model is based on the identification of key parameters and platform experiments.Based on the spatial geometric relationship between the breech and loader during modular charge transfer and the possible maximum interference depth of the modular charge,a new failure criterion for estimating the reliability of swinging-loading positioning accuracy is proposed.Considering the uncertainties in the operation of the pendulum loader,the direct probability integration method is introduced to analyze the reliability of the swinging-loading positioning accuracy under three different charge numbers.The results indicate that under two and four charges,the swinging-loading process shows outstanding reliability.However,an unstable stage appears when the swinging motion occurred under six charges,with a maximum positioning failure probability of 0.0712.A comparison between the results obtained under the conventional and proposed criteria further reveals the effectiveness and necessity of the proposed criterion.展开更多
Thetraditional first-order reliability method(FORM)often encounters challengeswith non-convergence of results or excessive calculation when analyzing complex engineering problems.To improve the global convergence spee...Thetraditional first-order reliability method(FORM)often encounters challengeswith non-convergence of results or excessive calculation when analyzing complex engineering problems.To improve the global convergence speed of structural reliability analysis,an improved coati optimization algorithm(COA)is proposed in this paper.In this study,the social learning strategy is used to improve the coati optimization algorithm(SL-COA),which improves the convergence speed and robustness of the newheuristic optimization algorithm.Then,the SL-COAis comparedwith the latest heuristic optimization algorithms such as the original COA,whale optimization algorithm(WOA),and osprey optimization algorithm(OOA)in the CEC2005 and CEC2017 test function sets and two engineering optimization design examples.The optimization results show that the proposed SL-COA algorithm has a high competitiveness.Secondly,this study introduces the SL-COA algorithm into the MPP(Most Probable Point)search process based on FORM and constructs a new reliability analysis method.Finally,the proposed reliability analysis method is verified by four mathematical examples and two engineering examples.The results show that the proposed SL-COA-assisted FORM exhibits fast convergence and avoids premature convergence to local optima as demonstrated by its successful application to problems such as composite cylinder design and support bracket analysis.展开更多
In the structural reliability analysis,the first-order reliability method(FORM)often yields significant errors when addressing nonlinear problems.Although the second-order reliability method(SORM)can provide higher ac...In the structural reliability analysis,the first-order reliability method(FORM)often yields significant errors when addressing nonlinear problems.Although the second-order reliability method(SORM)can provide higher accuracy,the additional computation of the Hessian matrix leads to lower computational efficiency.Additionally,when the dimensionality of the random variables is high,the approximation formula of SORM can result in larger errors.To address these issues,a structural reliability analysis method based on Kriging and spherical cap area integral is proposed.Firstly,this method integrates FORM with the quasi-Newton algorithm Broyden-Fletcher-Goldfarb-Shanno(BFGS),trains the Kriging model by using sample points from the algorithm’s iteration process,and combines the Kriging model with gradient information to approximate the Hessian matrix.Then,the failure surface is approximated as a rotating paraboloid,utilizing the spherical cap to replace the complex surface.For the n-dimensional case,the hyperspherical cap area expression is combined with the integral method to calculate the failure probability.Finally,the method is validated through three examples,demonstrating improved computational accuracy and efficiency compared to traditional methods.展开更多
The study aims to determine the validity and reliability of the Wechsler Preschool and Primary Scale of Intelligence–Third Edition(WPPSI-III)scores in a sample of kindergarten and lower primary pupils from Khartoum S...The study aims to determine the validity and reliability of the Wechsler Preschool and Primary Scale of Intelligence–Third Edition(WPPSI-III)scores in a sample of kindergarten and lower primary pupils from Khartoum State,Sudan.It also aims to examine whether test’s factor structure in this sample replicated that of the original WPPSI-III.The study sample consisted of 384 kindergarten and primary school children in Khartoum State(females=50%mean age=4.14,SD=1.37),selected using stratified random sampling across its seven localities:Khartoum,Jebel Awliya,Khartoum Bahri,East Nile,Omdurman,Ombada,Karari.For concurrent validation,the children additionally completed the Goodenough Draw-a-Man Test,and the Colored Progressive Matrices.WPPSI-III scores demonstrated high internal consistency across the subtest items.Confirmatory factor analysis indicators for total,verbal,and performance intelligence were all excellent.The scale also showed weak to strong score stability ranging from 0.25(weak)to 0.88(strong)based on the Spearman-Brown equation,0.25 to 0.75 based on the Guttman split-half method.The Cronbach’s alpha coefficient scores ranged from 0.54 to 0.93.The WPPSI-III and Goodenough Draw-a-Man Test scores concurrent validity scores were poor(0.05)to modest(0.31),and while those with the Colored Progressive Matrices test were poor(r=0.04–0.18).Thesefindings provide evidence to suggest that the WPPSI-III is appropriate for research use with kindergarten and lower primary school students in Khartoum State,Sudan.展开更多
Thermal Protection System(TPS)with thick tiles,low thermal conductivity,and a short re-entry stage stands as a critical element within reusable aircraft,whose reliability is related to the function and changes with th...Thermal Protection System(TPS)with thick tiles,low thermal conductivity,and a short re-entry stage stands as a critical element within reusable aircraft,whose reliability is related to the function and changes with their physical properties,external conditions,and degradation.Meanwhile,due to the limitation of testing resources,epistemic uncertainties stemming from the small samples are present in TPS reliability modeling.However,current TPS reliability modeling methods face challenges in characterizing the relationships among reliability and physical properties,external conditions,degradation,and epistemic uncertainties.Therefore,under the framework of belief reliability theory,a TPS reliability model is constructed,which takes into account the physical principle,external conditions,performance degradation,and epistemic uncertainties.A reliability simulation algorithm is proposed to calculate TPS reliability.Through a case study and comparison analysis,the proposed method is validated as more effective than the existing method.Additionally,reliability sensitivity analysis is conducted to identify the sensitive factors of reliability under the condition of small samples,through which suggestions are provided for TPS functional design and improvement.展开更多
The utilization of high-strength steel bars(HSSB)within concrete structures demonstrates significant advantages in material conservation and mechanical performance enhancement.Nevertheless,existing design codes exhibi...The utilization of high-strength steel bars(HSSB)within concrete structures demonstrates significant advantages in material conservation and mechanical performance enhancement.Nevertheless,existing design codes exhibit limitations in addressing the distinct statistical characteristics of HSSB,particularly regarding strength design parameters.For instance,GB50010-2010 fails to specify design strength values for reinforcement exceeding 600 MPa,creating technical barriers for advancing HSSB implementation.This study systematically investigates the reliability of eccentric compression concrete columns reinforced with 600 MPa-grade HSSB through high-order moment method analysis.Material partial factors were calibrated against target reliability indices prescribed by GB50068-2018,incorporating critical variables including live-to-dead load ratios,design methodologies,and service conditions.The findings show that the value of k significantly affects the calibration of material partial factors,impacting the reliability of bearing capacity.Considering various k values and target reliability indices,it is recommended that the material partialfactorbe setat1.15,implyingthatthedesignstrengthfor600MPahigh-strengthsteelbars shouldbe considered as 522 MPa.For safety levels I and II,load adjustment factors of 1.1 and 0.9,respectively,may be applied.展开更多
Probabilistic back-analysis is an important means to infer the statistics of uncertain soil parameters,making the slope reliability assessment closer to the engineering reality.However,multi-source information(includi...Probabilistic back-analysis is an important means to infer the statistics of uncertain soil parameters,making the slope reliability assessment closer to the engineering reality.However,multi-source information(including test data,monitored data,field observation and slope survival records)is rarely used in current probabilistic back-analysis.Conducting the probabilistic back-analysis of spatially varying soil parameters and slope reliability prediction under rainfalls by integrating multi-source information is a challenging task since thousands of random variables and high-dimensional likelihood function are usually involved.In this paper,a framework by integrating a modified Bayesian Updating with Subset simulation(mBUS)method with adaptive Conditional Sampling(aCS)algorithm is established for the probabilistic back-analysis of spatially varying soil parameters and slope reliability prediction.Within this framework,the high-dimensional probabilistic back-analysis problem can be easily tackled,and the multi-source information(e.g.monitored pressure heads and slope survival records)can be fully used in the back-analysis.A real Taoyuan landslide case in Taiwan,China is investigated to illustrate the effectiveness and performance of the established framework.The findings show that the posterior knowledge of soil parameters obtained from the established framework is in good agreement with the field observations.Furthermore,the updated knowledge of soil parameters can be utilized to reliably predict the occurrence probability of a landslide caused by the heavy rainfall event on September 12,2004 or forecast the potential landslides under future rainfalls in the Fuhsing District of Taoyuan City,Taiwan,China.展开更多
This paper introduces a novel approach for parameter sensitivity evaluation and efficient slope reliability analysis based on quantile-based first-order second-moment method(QFOSM).The core principles of the QFOSM are...This paper introduces a novel approach for parameter sensitivity evaluation and efficient slope reliability analysis based on quantile-based first-order second-moment method(QFOSM).The core principles of the QFOSM are elucidated geometrically from the perspective of expanding ellipsoids.Based on this geometric interpretation,the QFOSM is further extended to estimate sensitivity indices and assess the significance of various uncertain parameters involved in the slope system.The proposed method has the advantage of computational simplicity,akin to the conventional first-order second-moment method(FOSM),while providing estimation accuracy close to that of the first-order reliability method(FORM).Its performance is demonstrated with a numerical example and three slope examples.The results show that the proposed method can efficiently estimate the slope reliability and simultaneously evaluate the sensitivity of the uncertain parameters.The proposed method does not involve complex optimization or iteration required by the FORM.It can provide a valuable complement to the existing approximate reliability analysis methods,offering rapid sensitivity evaluation and slope reliability analysis.展开更多
Reliability is a crucial metric in aerospace engineering.The results of reliability assessments for components like aerospace electromagnetic relays directly impact the development and operational reliability of aeros...Reliability is a crucial metric in aerospace engineering.The results of reliability assessments for components like aerospace electromagnetic relays directly impact the development and operational reliability of aerospace engineering systems.Current methods for analyzing the reliability of aerospace electromagnetic relays have limitations,such as neglecting the combined effects of multiple uncertain factors,degradation of key component properties,and the influence of fluctuations in aerospace environments.Additionally,these methods often assume a single-type uncertainty in the manufacturing process,leading to significant deviations between the analysis results and actual measurement results.To address these issues,this study proposes an efficient timedependent reliability analysis method based on the HL-RF algorithm,considering a hybrid of probabilistic and interval uncertainty that accounts for degradation and environmental conditions.The proposed method is applied to the reliability analysis of actual aerospace electromagnetic relay products and compared with traditional methods,demonstrating significant advantages.The proposed method has been applied to the time-dependent reliability analysis of actual aerospace electromagnetic relay products under different environmental conditions.The analysis results exhibit an error margin within 5.12% compared to actual measurement results.Compared to analysis methods solely based on probabilistic uncertainty quantification or interval uncertainty quantification,this method reduces the analysis error by 52% and 67% respectively.When compared to two other state-of-the-art methods that integrate probabilistic and interval uncertainty quantification,the error reduction is 23%.These demonstrate the superiority of the proposed method and validates its effectiveness.The presented approach has the potential to be extended for reliability analysis in other aerospace electromechanical systems.展开更多
To consider the complex soil-structure interaction in a pile-slope system,it is necessary to analyze the performance of pile-slope systems based on a three-dimensional(3D)numerical model.Reliability analysis of a pile...To consider the complex soil-structure interaction in a pile-slope system,it is necessary to analyze the performance of pile-slope systems based on a three-dimensional(3D)numerical model.Reliability analysis of a pile-slope system based on 3D numerical modeling is very challenging because it is computationally expensive and the performance function of the pile failure mode is only defined in the safe domain of soil stability.In this paper,an efficient hybrid response surface method is suggested to study the system reliability of pile-reinforced slopes,where the support vector machine and the Kriging model are used to approximate performance functions of soil failure and pile failure,respectively.The versatility of the suggested method is illustrated in detail with an example.For the example examined in this paper,it is found that the pile failure can significantly contribute to system failure,and the reinforcement ratio can effectively reduce the probability of pile failure.There exists a critical reinforcement ratio beyond which the system failure probability is not sensitive to the reinforcement ratio.The pile spacing affects both the probabilities of soil failure and pile failure of the pile-reinforced slope.There exists an optimal location and an optimal length for the stabilizing piles.展开更多
Estimating the failure probability of highly reliable structures in practice engineering,such as aeronautical components,is challenging because of the strong-coupling and the small failure probability traits.In this p...Estimating the failure probability of highly reliable structures in practice engineering,such as aeronautical components,is challenging because of the strong-coupling and the small failure probability traits.In this paper,an Expanded Learning Intelligent Back Propagation(EL-IBP)neural network approach is developed:firstly,to accurately characterize the engineering response coupling relationships,a high-fidelity Intelligent-optimized Back Propagation(IBP)neural network metamodel is developed;furthermore,to elevate the analysis efficacy for small failure assessment,a novel expanded learning strategy for adaptive IBP metamodeling is proposed.Three numerical examples and one typical practice engineering case are analyzed,to validate the effectiveness and engineering application value of the proposed method.Methods comparison shows that the ELIBP method holds significant efficiency and accuracy superiorities in engineering issues.The current study may shed a light on pushing the adaptive metamodeling technique deeply toward complex engineering reliability analysis.展开更多
For complex engineering problems,multi-fidelity modeling has been used to achieve efficient reliability analysis by leveraging multiple information sources.However,most methods require nested training samples to captu...For complex engineering problems,multi-fidelity modeling has been used to achieve efficient reliability analysis by leveraging multiple information sources.However,most methods require nested training samples to capture the correlation between different fidelity data,which may lead to a significant increase in low-fidelity samples.In addition,it is difficult to build accurate surrogate models because current methods do not fully consider the nonlinearity between different fidelity samples.To address these problems,a novel multi-fidelity modeling method with active learning is proposed in this paper.Firstly,a nonlinear autoregressive multi-fidelity Kriging(NAMK)model is used to build a surrogate model.To avoid introducing redundant samples in the process of NAMK model updating,a collective learning function is then developed by a combination of a U-learning function,the correlation between different fidelity samples,and the sampling cost.Furthermore,a residual model is constructed to automatically generate low-fidelity samples when high-fidelity samples are selected.The efficiency and accuracy of the proposed method are demonstrated using three numerical examples and an engineering case.展开更多
Surrogate models offer an efficient approach to tackle the computationally intensive evaluation of performance functions in reliability analysis.Nevertheless,the approximations inherent in surrogate models necessitate...Surrogate models offer an efficient approach to tackle the computationally intensive evaluation of performance functions in reliability analysis.Nevertheless,the approximations inherent in surrogate models necessitate the consideration of surrogate model uncertainty in estimating failure probabilities.This paper proposes a new reliability analysis method in which the uncertainty from the Kriging surrogate model is quantified simultaneously.This method treats surrogate model uncertainty as an independent entity,characterizing the estimation error of failure probabilities.Building upon the probabilistic classification function,a failure probability uncertainty is proposed by integrating the difference between the traditional indicator function and the probabilistic classification function to quantify the impact of surrogate model uncertainty on failure probability estimation.Furthermore,the proposed uncertainty quantification method is applied to a newly designed reliability analysis approach termed SUQ-MCS,incorporating a proposed median approximation function for active learning.The proposed failure probability uncertainty serves as the stopping criterion of this framework.Through benchmarking,the effectiveness of the proposed uncertainty quantification method is validated.The empirical results present the competitive performance of the SUQ-MCS method relative to alternative approaches.展开更多
To address the seismic face stability challenges encountered in urban and subsea tunnel construction,an efficient probabilistic analysis framework for shield tunnel faces under seismic conditions is proposed.Based on ...To address the seismic face stability challenges encountered in urban and subsea tunnel construction,an efficient probabilistic analysis framework for shield tunnel faces under seismic conditions is proposed.Based on the upper-bound theory of limit analysis,an improved three-dimensional discrete deterministic mechanism,accounting for the heterogeneous nature of soil media,is formulated to evaluate seismic face stability.The metamodel of failure probabilistic assessments for seismic tunnel faces is constructed by integrating the sparse polynomial chaos expansion method(SPCE)with the modified pseudo-dynamic approach(MPD).The improved deterministic model is validated by comparing with published literature and numerical simulations results,and the SPCE-MPD metamodel is examined with the traditional MCS method.Based on the SPCE-MPD metamodels,the seismic effects on face failure probability and reliability index are presented and the global sensitivity analysis(GSA)is involved to reflect the influence order of seismic action parameters.Finally,the proposed approach is tested to be effective by a engineering case of the Chengdu outer ring tunnel.The results show that higher uncertainty of seismic response on face stability should be noticed in areas with intense earthquakes and variation of seismic wave velocity has the most profound influence on tunnel face stability.展开更多
Spatial variability of soil properties imposes a challenge for practical analysis and design in geotechnical engineering.The latter is particularly true for slope stability assessment,where the effects of uncertainty ...Spatial variability of soil properties imposes a challenge for practical analysis and design in geotechnical engineering.The latter is particularly true for slope stability assessment,where the effects of uncertainty are synthesized in the so-called probability of failure.This probability quantifies the reliability of a slope and its numerical calculation is usually quite involved from a numerical viewpoint.In view of this issue,this paper proposes an approach for failure probability assessment based on Latinized partially stratified sampling and maximum entropy distribution with fractional moments.The spatial variability of geotechnical properties is represented by means of random fields and the Karhunen-Loève expansion.Then,failure probabilities are estimated employing maximum entropy distribution with fractional moments.The application of the proposed approach is examined with two examples:a case study of an undrained slope and a case study of a slope with cross-correlated random fields of strength parameters under a drained slope.The results show that the proposed approach has excellent accuracy and high efficiency,and it can be applied straightforwardly to similar geotechnical engineering problems.展开更多
In time-variant reliability problems,there are a lot of uncertain variables from different sources.Therefore,it is important to consider these uncertainties in engineering.In addition,time-variant reliability problems...In time-variant reliability problems,there are a lot of uncertain variables from different sources.Therefore,it is important to consider these uncertainties in engineering.In addition,time-variant reliability problems typically involve a complexmultilevel nested optimization problem,which can result in an enormous amount of computation.To this end,this paper studies the time-variant reliability evaluation of structures with stochastic and bounded uncertainties using a mixed probability and convex set model.In this method,the stochastic process of a limit-state function with mixed uncertain parameters is first discretized and then converted into a timeindependent reliability problem.Further,to solve the double nested optimization problem in hybrid reliability calculation,an efficient iterative scheme is designed in standard uncertainty space to determine the most probable point(MPP).The limit state function is linearized at these points,and an innovative random variable is defined to solve the equivalent static reliability analysis model.The effectiveness of the proposed method is verified by two benchmark numerical examples and a practical engineering problem.展开更多
The escalating need for reliability analysis(RA)and reliability-based design optimization(RBDO)within engineering challenges has prompted the advancement of saddlepoint approximationmethods(SAM)tailored for such probl...The escalating need for reliability analysis(RA)and reliability-based design optimization(RBDO)within engineering challenges has prompted the advancement of saddlepoint approximationmethods(SAM)tailored for such problems.This article offers a detailed overview of the general SAM and summarizes the method characteristics first.Subsequently,recent enhancements in the SAM theoretical framework are assessed.Notably,the mean value first-order saddlepoint approximation(MVFOSA)bears resemblance to the conceptual framework of the mean value second-order saddlepoint approximation(MVSOSA);the latter serves as an auxiliary approach to the former.Their distinction is rooted in the varying expansion orders of the performance function as implemented through the Taylor method.Both the saddlepoint approximation and third-moment(SATM)and saddlepoint approximation and fourth-moment(SAFM)strategies model the cumulant generating function(CGF)by leveraging the initial random moments of the function.Although their optimal application domains diverge,each method consistently ensures superior relative precision,enhanced efficiency,and sustained stability.Every method elucidated is exemplified through pertinent RA or RBDO scenarios.By juxtaposing them against alternative strategies,the efficacy of these methods becomes evident.The outcomes proffered are subsequently employed as a foundation for contemplating prospective theoretical and practical research endeavors concerning SAMs.The main purpose and value of this article is to review the SAM and reliability-related issues,which can provide some reference and inspiration for future research scholars in this field.展开更多
For high-reliability systems in military,aerospace,and railway fields,the challenges of reliability analysis lie in dealing with unclear failure mechanisms,complex fault relationships,lack of fault data,and uncertaint...For high-reliability systems in military,aerospace,and railway fields,the challenges of reliability analysis lie in dealing with unclear failure mechanisms,complex fault relationships,lack of fault data,and uncertainty of fault states.To overcome these problems,this paper proposes a reliability analysismethod based on T-S fault tree analysis(T-S FTA)and Hyper-ellipsoidal Bayesian network(HE-BN).The method describes the connection between the various systemfault events by T-S fuzzy gates and translates them into a Bayesian network(BN)model.Combining the advantages of T-S fault tree modeling with the advantages of Bayesian network computation,a reliability modeling method is proposed that can fully reflect the fault characteristics of complex systems.Experts describe the degree of failure of the event in the form of interval numbers.The knowledge and experience of experts are fused with the D-S evidence theory to obtain the initial failure probability interval of the BN root node.Then,the Hyper-ellipsoidal model(HM)constrains the initial failure probability interval and constructs a HE-BN for the system.A reliability analysismethod is proposed to solve the problem of insufficient failure data and uncertainty in the degree of failure.The failure probability of the system is further calculated and the key components that affect the system’s reliability are identified.The proposedmethod accounts for the uncertainty and incompleteness of the failure data in complex multi-state systems and establishes an easily computable reliability model that fully reflects the characteristics of complex faults and accurately identifies system weaknesses.The feasibility and accuracy of the method are further verified by conducting case studies.展开更多
基金The National Key R&D Program Projects(Grant No.2022YFC2803601)the Natural Science Foundation of Shandong Province(Grant No.ZR2021YQ29)+1 种基金the Natural Science Foundation of Heilongjiang Province(Grant No.YQ2024E036)the Taishan Scholars Project(Grant No.tsqn202312317).
文摘Autonomous Underwater Vehicles(AUVs)are pivotal for deep-sea exploration and resource exploitation,yet their reliability in extreme underwater environments remains a critical barrier to widespread deployment.Through systematic analysis of 150 peer-reviewed studies employing mixed-methods research,this review yields three principal advancements to the reliability analysis of AUVs.First,based on the hierarchical functional division of AUVs into six subsystems(propulsion system,navigation system,communication system,power system,environmental detection system,and emergency system),this study systematically identifies the primary failure modes and potential failure causes of each subsystem,providing theoretical support for fault diagnosis and reliability optimization.Subsequently,a comprehensive review of AUV reliability analysis methods is conducted from three perspectives:analytical methods,simulated methods,and surrogate model methods.The applicability and limitations of each method are critically analyzed to offer insights into their suitability for engineering applications.Finally,the study highlights key challenges and research hotpots in AUV reliability analysis,including reliability analysis under limited data,AI-driven reliability analysis,and human reliability analysis.Furthermore,the potential of multi-sensor data fusion,edge computing,and advanced materials in enhancing AUV environmental adaptability and reliability is explored.
基金supported by the National Key Research and Development Program of China(Grant No.2021YFB2600504)the National Natural Science Foundation of China(Grant No.42072302)the Postdoctoral Fellowship Program of CPSF(Grant No.GZB20240533).
文摘Reliability analysis of soil slopes under rainfall is an important task for landslide risk assessment.Previous studies rarely contribute to the probabilistic analysis of slope stability under rainfall with reinforcement.A new method is suggested for reliability analysis of soil slopes stabilized with piles under rainfall.First,an efficient numerical model is exploited for slope stability analysis,where two types of slope failure modes,i.e.,plastic flow and local failure are considered.To address the blocking effect of piles during seepage analysis,the equivalent hydraulic conductivity of the pile area is estimated according to the effective medium theory.The stabilizing force of piles is investigated by an analytical approach.For saving computational effort,the response surface is established based on a multi-class classification model to predict two types of slope failure modes.Finally,uncertainties in soil parameters and rainfall events are both modelled,and the failure probability of soil slopes within a given time period is assessed through Monte Carlo simulation.An illustrative example is used to demonstrate the performance of the suggested method.It is found that the slope is mainly controlled by local failure.As the pile spacing increases,the likelihood of plastic flow significantly increases.As the piles are located near the slope crest,plastic flow is effectively prevented and the slope is better stabilized against rainfall.If rainfall uncertainties are not considered,the slope failure probability is significantly overestimated.Overall,this study can provide a useful guidance for the design of pile-stabilized slopes against rainfall infiltration.
文摘The swinging-loading process is essential for automatic artillery loading systems.This study focuses on the problems of reliability analysis that affect swinging-loading positioning accuracy.A dynamic model for a multi degree-of-freedom swinging-loading-integrated rigid-flexible coupling system is established.This model is based on the identification of key parameters and platform experiments.Based on the spatial geometric relationship between the breech and loader during modular charge transfer and the possible maximum interference depth of the modular charge,a new failure criterion for estimating the reliability of swinging-loading positioning accuracy is proposed.Considering the uncertainties in the operation of the pendulum loader,the direct probability integration method is introduced to analyze the reliability of the swinging-loading positioning accuracy under three different charge numbers.The results indicate that under two and four charges,the swinging-loading process shows outstanding reliability.However,an unstable stage appears when the swinging motion occurred under six charges,with a maximum positioning failure probability of 0.0712.A comparison between the results obtained under the conventional and proposed criteria further reveals the effectiveness and necessity of the proposed criterion.
基金funded by the National Key Research and Development Program(Grant No.2022YFB3706904).
文摘Thetraditional first-order reliability method(FORM)often encounters challengeswith non-convergence of results or excessive calculation when analyzing complex engineering problems.To improve the global convergence speed of structural reliability analysis,an improved coati optimization algorithm(COA)is proposed in this paper.In this study,the social learning strategy is used to improve the coati optimization algorithm(SL-COA),which improves the convergence speed and robustness of the newheuristic optimization algorithm.Then,the SL-COAis comparedwith the latest heuristic optimization algorithms such as the original COA,whale optimization algorithm(WOA),and osprey optimization algorithm(OOA)in the CEC2005 and CEC2017 test function sets and two engineering optimization design examples.The optimization results show that the proposed SL-COA algorithm has a high competitiveness.Secondly,this study introduces the SL-COA algorithm into the MPP(Most Probable Point)search process based on FORM and constructs a new reliability analysis method.Finally,the proposed reliability analysis method is verified by four mathematical examples and two engineering examples.The results show that the proposed SL-COA-assisted FORM exhibits fast convergence and avoids premature convergence to local optima as demonstrated by its successful application to problems such as composite cylinder design and support bracket analysis.
基金National Natural Science Foundation of China(No.52375236)Fundamental Research Funds for the Central Universities,China(No.23D110316)。
文摘In the structural reliability analysis,the first-order reliability method(FORM)often yields significant errors when addressing nonlinear problems.Although the second-order reliability method(SORM)can provide higher accuracy,the additional computation of the Hessian matrix leads to lower computational efficiency.Additionally,when the dimensionality of the random variables is high,the approximation formula of SORM can result in larger errors.To address these issues,a structural reliability analysis method based on Kriging and spherical cap area integral is proposed.Firstly,this method integrates FORM with the quasi-Newton algorithm Broyden-Fletcher-Goldfarb-Shanno(BFGS),trains the Kriging model by using sample points from the algorithm’s iteration process,and combines the Kriging model with gradient information to approximate the Hessian matrix.Then,the failure surface is approximated as a rotating paraboloid,utilizing the spherical cap to replace the complex surface.For the n-dimensional case,the hyperspherical cap area expression is combined with the integral method to calculate the failure probability.Finally,the method is validated through three examples,demonstrating improved computational accuracy and efficiency compared to traditional methods.
基金The authors extend their appreciation to the Ongoing Research Funding Program,number(ORF2025R705),King Saud University,Riyadh,Saudi Arabia,for funding this work.
文摘The study aims to determine the validity and reliability of the Wechsler Preschool and Primary Scale of Intelligence–Third Edition(WPPSI-III)scores in a sample of kindergarten and lower primary pupils from Khartoum State,Sudan.It also aims to examine whether test’s factor structure in this sample replicated that of the original WPPSI-III.The study sample consisted of 384 kindergarten and primary school children in Khartoum State(females=50%mean age=4.14,SD=1.37),selected using stratified random sampling across its seven localities:Khartoum,Jebel Awliya,Khartoum Bahri,East Nile,Omdurman,Ombada,Karari.For concurrent validation,the children additionally completed the Goodenough Draw-a-Man Test,and the Colored Progressive Matrices.WPPSI-III scores demonstrated high internal consistency across the subtest items.Confirmatory factor analysis indicators for total,verbal,and performance intelligence were all excellent.The scale also showed weak to strong score stability ranging from 0.25(weak)to 0.88(strong)based on the Spearman-Brown equation,0.25 to 0.75 based on the Guttman split-half method.The Cronbach’s alpha coefficient scores ranged from 0.54 to 0.93.The WPPSI-III and Goodenough Draw-a-Man Test scores concurrent validity scores were poor(0.05)to modest(0.31),and while those with the Colored Progressive Matrices test were poor(r=0.04–0.18).Thesefindings provide evidence to suggest that the WPPSI-III is appropriate for research use with kindergarten and lower primary school students in Khartoum State,Sudan.
基金supported by the steady supports scientific research of Key Laboratory of Defense Science and Technology,China(No.WDZC20220105)the National Natural Science Foundation of China(Nos.51775020,62073009,U20B2002)the Science Challenge Project,China(No.TZ2018007)。
文摘Thermal Protection System(TPS)with thick tiles,low thermal conductivity,and a short re-entry stage stands as a critical element within reusable aircraft,whose reliability is related to the function and changes with their physical properties,external conditions,and degradation.Meanwhile,due to the limitation of testing resources,epistemic uncertainties stemming from the small samples are present in TPS reliability modeling.However,current TPS reliability modeling methods face challenges in characterizing the relationships among reliability and physical properties,external conditions,degradation,and epistemic uncertainties.Therefore,under the framework of belief reliability theory,a TPS reliability model is constructed,which takes into account the physical principle,external conditions,performance degradation,and epistemic uncertainties.A reliability simulation algorithm is proposed to calculate TPS reliability.Through a case study and comparison analysis,the proposed method is validated as more effective than the existing method.Additionally,reliability sensitivity analysis is conducted to identify the sensitive factors of reliability under the condition of small samples,through which suggestions are provided for TPS functional design and improvement.
基金supported by grants from the Natural Science Foundation of Fujian Province(Grant No.2022J05184).
文摘The utilization of high-strength steel bars(HSSB)within concrete structures demonstrates significant advantages in material conservation and mechanical performance enhancement.Nevertheless,existing design codes exhibit limitations in addressing the distinct statistical characteristics of HSSB,particularly regarding strength design parameters.For instance,GB50010-2010 fails to specify design strength values for reinforcement exceeding 600 MPa,creating technical barriers for advancing HSSB implementation.This study systematically investigates the reliability of eccentric compression concrete columns reinforced with 600 MPa-grade HSSB through high-order moment method analysis.Material partial factors were calibrated against target reliability indices prescribed by GB50068-2018,incorporating critical variables including live-to-dead load ratios,design methodologies,and service conditions.The findings show that the value of k significantly affects the calibration of material partial factors,impacting the reliability of bearing capacity.Considering various k values and target reliability indices,it is recommended that the material partialfactorbe setat1.15,implyingthatthedesignstrengthfor600MPahigh-strengthsteelbars shouldbe considered as 522 MPa.For safety levels I and II,load adjustment factors of 1.1 and 0.9,respectively,may be applied.
文摘Probabilistic back-analysis is an important means to infer the statistics of uncertain soil parameters,making the slope reliability assessment closer to the engineering reality.However,multi-source information(including test data,monitored data,field observation and slope survival records)is rarely used in current probabilistic back-analysis.Conducting the probabilistic back-analysis of spatially varying soil parameters and slope reliability prediction under rainfalls by integrating multi-source information is a challenging task since thousands of random variables and high-dimensional likelihood function are usually involved.In this paper,a framework by integrating a modified Bayesian Updating with Subset simulation(mBUS)method with adaptive Conditional Sampling(aCS)algorithm is established for the probabilistic back-analysis of spatially varying soil parameters and slope reliability prediction.Within this framework,the high-dimensional probabilistic back-analysis problem can be easily tackled,and the multi-source information(e.g.monitored pressure heads and slope survival records)can be fully used in the back-analysis.A real Taoyuan landslide case in Taiwan,China is investigated to illustrate the effectiveness and performance of the established framework.The findings show that the posterior knowledge of soil parameters obtained from the established framework is in good agreement with the field observations.Furthermore,the updated knowledge of soil parameters can be utilized to reliably predict the occurrence probability of a landslide caused by the heavy rainfall event on September 12,2004 or forecast the potential landslides under future rainfalls in the Fuhsing District of Taoyuan City,Taiwan,China.
基金supported by the National Natural Science Foundation of China(Grant Nos.52109144,52025094 and 52222905).
文摘This paper introduces a novel approach for parameter sensitivity evaluation and efficient slope reliability analysis based on quantile-based first-order second-moment method(QFOSM).The core principles of the QFOSM are elucidated geometrically from the perspective of expanding ellipsoids.Based on this geometric interpretation,the QFOSM is further extended to estimate sensitivity indices and assess the significance of various uncertain parameters involved in the slope system.The proposed method has the advantage of computational simplicity,akin to the conventional first-order second-moment method(FOSM),while providing estimation accuracy close to that of the first-order reliability method(FORM).Its performance is demonstrated with a numerical example and three slope examples.The results show that the proposed method can efficiently estimate the slope reliability and simultaneously evaluate the sensitivity of the uncertain parameters.The proposed method does not involve complex optimization or iteration required by the FORM.It can provide a valuable complement to the existing approximate reliability analysis methods,offering rapid sensitivity evaluation and slope reliability analysis.
基金supported by the National Natural Science Foundation of China(No.52277141).
文摘Reliability is a crucial metric in aerospace engineering.The results of reliability assessments for components like aerospace electromagnetic relays directly impact the development and operational reliability of aerospace engineering systems.Current methods for analyzing the reliability of aerospace electromagnetic relays have limitations,such as neglecting the combined effects of multiple uncertain factors,degradation of key component properties,and the influence of fluctuations in aerospace environments.Additionally,these methods often assume a single-type uncertainty in the manufacturing process,leading to significant deviations between the analysis results and actual measurement results.To address these issues,this study proposes an efficient timedependent reliability analysis method based on the HL-RF algorithm,considering a hybrid of probabilistic and interval uncertainty that accounts for degradation and environmental conditions.The proposed method is applied to the reliability analysis of actual aerospace electromagnetic relay products and compared with traditional methods,demonstrating significant advantages.The proposed method has been applied to the time-dependent reliability analysis of actual aerospace electromagnetic relay products under different environmental conditions.The analysis results exhibit an error margin within 5.12% compared to actual measurement results.Compared to analysis methods solely based on probabilistic uncertainty quantification or interval uncertainty quantification,this method reduces the analysis error by 52% and 67% respectively.When compared to two other state-of-the-art methods that integrate probabilistic and interval uncertainty quantification,the error reduction is 23%.These demonstrate the superiority of the proposed method and validates its effectiveness.The presented approach has the potential to be extended for reliability analysis in other aerospace electromechanical systems.
基金substantially supported by the National Natural Science Foundation of China(Grant No.42072302)Shuguang Program from Shanghai Education Development Foundation and Shanghai Municipal Education Commission(Grant No.19SG19)Fundamental Research Funds for the Central Universities.
文摘To consider the complex soil-structure interaction in a pile-slope system,it is necessary to analyze the performance of pile-slope systems based on a three-dimensional(3D)numerical model.Reliability analysis of a pile-slope system based on 3D numerical modeling is very challenging because it is computationally expensive and the performance function of the pile failure mode is only defined in the safe domain of soil stability.In this paper,an efficient hybrid response surface method is suggested to study the system reliability of pile-reinforced slopes,where the support vector machine and the Kriging model are used to approximate performance functions of soil failure and pile failure,respectively.The versatility of the suggested method is illustrated in detail with an example.For the example examined in this paper,it is found that the pile failure can significantly contribute to system failure,and the reinforcement ratio can effectively reduce the probability of pile failure.There exists a critical reinforcement ratio beyond which the system failure probability is not sensitive to the reinforcement ratio.The pile spacing affects both the probabilities of soil failure and pile failure of the pile-reinforced slope.There exists an optimal location and an optimal length for the stabilizing piles.
基金co-supported by the National Key R&D Program of China(No.2021YFB1715000)the National Natural Science Foundation of China(No.52105136)the Hong Kong Scholars Program,China(No.XJ2022013).
文摘Estimating the failure probability of highly reliable structures in practice engineering,such as aeronautical components,is challenging because of the strong-coupling and the small failure probability traits.In this paper,an Expanded Learning Intelligent Back Propagation(EL-IBP)neural network approach is developed:firstly,to accurately characterize the engineering response coupling relationships,a high-fidelity Intelligent-optimized Back Propagation(IBP)neural network metamodel is developed;furthermore,to elevate the analysis efficacy for small failure assessment,a novel expanded learning strategy for adaptive IBP metamodeling is proposed.Three numerical examples and one typical practice engineering case are analyzed,to validate the effectiveness and engineering application value of the proposed method.Methods comparison shows that the ELIBP method holds significant efficiency and accuracy superiorities in engineering issues.The current study may shed a light on pushing the adaptive metamodeling technique deeply toward complex engineering reliability analysis.
基金supported by the Major Projects of Zhejiang Provincial Natural Science Foundation of China(No.LD22E050009)the National Natural Science Foundation of China(No.51475425)the College Student’s Science and Technology Innovation Project of Zhejiang Province(No.2022R403B060),China.
文摘For complex engineering problems,multi-fidelity modeling has been used to achieve efficient reliability analysis by leveraging multiple information sources.However,most methods require nested training samples to capture the correlation between different fidelity data,which may lead to a significant increase in low-fidelity samples.In addition,it is difficult to build accurate surrogate models because current methods do not fully consider the nonlinearity between different fidelity samples.To address these problems,a novel multi-fidelity modeling method with active learning is proposed in this paper.Firstly,a nonlinear autoregressive multi-fidelity Kriging(NAMK)model is used to build a surrogate model.To avoid introducing redundant samples in the process of NAMK model updating,a collective learning function is then developed by a combination of a U-learning function,the correlation between different fidelity samples,and the sampling cost.Furthermore,a residual model is constructed to automatically generate low-fidelity samples when high-fidelity samples are selected.The efficiency and accuracy of the proposed method are demonstrated using three numerical examples and an engineering case.
基金supported by the National Key Research and Development Program of China(No.2023YFB3406900)the National Natural Science Foundation of China(No.52075068).
文摘Surrogate models offer an efficient approach to tackle the computationally intensive evaluation of performance functions in reliability analysis.Nevertheless,the approximations inherent in surrogate models necessitate the consideration of surrogate model uncertainty in estimating failure probabilities.This paper proposes a new reliability analysis method in which the uncertainty from the Kriging surrogate model is quantified simultaneously.This method treats surrogate model uncertainty as an independent entity,characterizing the estimation error of failure probabilities.Building upon the probabilistic classification function,a failure probability uncertainty is proposed by integrating the difference between the traditional indicator function and the probabilistic classification function to quantify the impact of surrogate model uncertainty on failure probability estimation.Furthermore,the proposed uncertainty quantification method is applied to a newly designed reliability analysis approach termed SUQ-MCS,incorporating a proposed median approximation function for active learning.The proposed failure probability uncertainty serves as the stopping criterion of this framework.Through benchmarking,the effectiveness of the proposed uncertainty quantification method is validated.The empirical results present the competitive performance of the SUQ-MCS method relative to alternative approaches.
基金Project([2018]3010)supported by the Guizhou Provincial Science and Technology Major Project,China。
文摘To address the seismic face stability challenges encountered in urban and subsea tunnel construction,an efficient probabilistic analysis framework for shield tunnel faces under seismic conditions is proposed.Based on the upper-bound theory of limit analysis,an improved three-dimensional discrete deterministic mechanism,accounting for the heterogeneous nature of soil media,is formulated to evaluate seismic face stability.The metamodel of failure probabilistic assessments for seismic tunnel faces is constructed by integrating the sparse polynomial chaos expansion method(SPCE)with the modified pseudo-dynamic approach(MPD).The improved deterministic model is validated by comparing with published literature and numerical simulations results,and the SPCE-MPD metamodel is examined with the traditional MCS method.Based on the SPCE-MPD metamodels,the seismic effects on face failure probability and reliability index are presented and the global sensitivity analysis(GSA)is involved to reflect the influence order of seismic action parameters.Finally,the proposed approach is tested to be effective by a engineering case of the Chengdu outer ring tunnel.The results show that higher uncertainty of seismic response on face stability should be noticed in areas with intense earthquakes and variation of seismic wave velocity has the most profound influence on tunnel face stability.
基金funding support from the China Scholarship Council(CSC).
文摘Spatial variability of soil properties imposes a challenge for practical analysis and design in geotechnical engineering.The latter is particularly true for slope stability assessment,where the effects of uncertainty are synthesized in the so-called probability of failure.This probability quantifies the reliability of a slope and its numerical calculation is usually quite involved from a numerical viewpoint.In view of this issue,this paper proposes an approach for failure probability assessment based on Latinized partially stratified sampling and maximum entropy distribution with fractional moments.The spatial variability of geotechnical properties is represented by means of random fields and the Karhunen-Loève expansion.Then,failure probabilities are estimated employing maximum entropy distribution with fractional moments.The application of the proposed approach is examined with two examples:a case study of an undrained slope and a case study of a slope with cross-correlated random fields of strength parameters under a drained slope.The results show that the proposed approach has excellent accuracy and high efficiency,and it can be applied straightforwardly to similar geotechnical engineering problems.
基金partially supported by the National Natural Science Foundation of China(52375238)Science and Technology Program of Guangzhou(202201020213,202201020193,202201010399)GZHU-HKUST Joint Research Fund(YH202109).
文摘In time-variant reliability problems,there are a lot of uncertain variables from different sources.Therefore,it is important to consider these uncertainties in engineering.In addition,time-variant reliability problems typically involve a complexmultilevel nested optimization problem,which can result in an enormous amount of computation.To this end,this paper studies the time-variant reliability evaluation of structures with stochastic and bounded uncertainties using a mixed probability and convex set model.In this method,the stochastic process of a limit-state function with mixed uncertain parameters is first discretized and then converted into a timeindependent reliability problem.Further,to solve the double nested optimization problem in hybrid reliability calculation,an efficient iterative scheme is designed in standard uncertainty space to determine the most probable point(MPP).The limit state function is linearized at these points,and an innovative random variable is defined to solve the equivalent static reliability analysis model.The effectiveness of the proposed method is verified by two benchmark numerical examples and a practical engineering problem.
基金funded by the National Natural Science Foundation of China under Grant No.52175130the Sichuan Science and Technology Program under Grants Nos.2022YFQ0087 and 2022JDJQ0024+1 种基金the Guangdong Basic and Applied Basic Research Foundation under Grant No.2022A1515240010the Students Go Abroad for Scientific Research and Internship Funding Program of University of Electronic Science and Technology of China.
文摘The escalating need for reliability analysis(RA)and reliability-based design optimization(RBDO)within engineering challenges has prompted the advancement of saddlepoint approximationmethods(SAM)tailored for such problems.This article offers a detailed overview of the general SAM and summarizes the method characteristics first.Subsequently,recent enhancements in the SAM theoretical framework are assessed.Notably,the mean value first-order saddlepoint approximation(MVFOSA)bears resemblance to the conceptual framework of the mean value second-order saddlepoint approximation(MVSOSA);the latter serves as an auxiliary approach to the former.Their distinction is rooted in the varying expansion orders of the performance function as implemented through the Taylor method.Both the saddlepoint approximation and third-moment(SATM)and saddlepoint approximation and fourth-moment(SAFM)strategies model the cumulant generating function(CGF)by leveraging the initial random moments of the function.Although their optimal application domains diverge,each method consistently ensures superior relative precision,enhanced efficiency,and sustained stability.Every method elucidated is exemplified through pertinent RA or RBDO scenarios.By juxtaposing them against alternative strategies,the efficacy of these methods becomes evident.The outcomes proffered are subsequently employed as a foundation for contemplating prospective theoretical and practical research endeavors concerning SAMs.The main purpose and value of this article is to review the SAM and reliability-related issues,which can provide some reference and inspiration for future research scholars in this field.
基金the National Natural Science Foundation of China(51875073).
文摘For high-reliability systems in military,aerospace,and railway fields,the challenges of reliability analysis lie in dealing with unclear failure mechanisms,complex fault relationships,lack of fault data,and uncertainty of fault states.To overcome these problems,this paper proposes a reliability analysismethod based on T-S fault tree analysis(T-S FTA)and Hyper-ellipsoidal Bayesian network(HE-BN).The method describes the connection between the various systemfault events by T-S fuzzy gates and translates them into a Bayesian network(BN)model.Combining the advantages of T-S fault tree modeling with the advantages of Bayesian network computation,a reliability modeling method is proposed that can fully reflect the fault characteristics of complex systems.Experts describe the degree of failure of the event in the form of interval numbers.The knowledge and experience of experts are fused with the D-S evidence theory to obtain the initial failure probability interval of the BN root node.Then,the Hyper-ellipsoidal model(HM)constrains the initial failure probability interval and constructs a HE-BN for the system.A reliability analysismethod is proposed to solve the problem of insufficient failure data and uncertainty in the degree of failure.The failure probability of the system is further calculated and the key components that affect the system’s reliability are identified.The proposedmethod accounts for the uncertainty and incompleteness of the failure data in complex multi-state systems and establishes an easily computable reliability model that fully reflects the characteristics of complex faults and accurately identifies system weaknesses.The feasibility and accuracy of the method are further verified by conducting case studies.
