Probabilistic Engineering Mechanics最新文献

Fractional-order filter approximations for efficient stochastic response determination of wind-excited linear structural systems 用于风激线性结构系统高效随机响应确定的分数阶滤波器近似值

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Probabilistic Engineering Mechanics Pub Date : 2024-10-01 DOI: 10.1016/j.probengmech.2024.103696

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Seismic reliability analysis using Subset Simulation enhanced with an explorative adaptive conditional sampling algorithm 利用探索性自适应条件采样算法增强子集模拟进行地震可靠性分析

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Probabilistic Engineering Mechanics Pub Date : 2024-10-01 DOI: 10.1016/j.probengmech.2024.103690

{"title":"Seismic reliability analysis using Subset Simulation enhanced with an explorative adaptive conditional sampling algorithm","authors":"","doi":"10.1016/j.probengmech.2024.103690","DOIUrl":"10.1016/j.probengmech.2024.103690","url":null,"abstract":"<div><div>Reliability analysis of structures under earthquake loading represents a significant engineering challenge. This is due to the required and rather numerically involving non-linear dynamic analysis, the large computational burden when targeting small failure probabilities, and the synthetic earthquake model representation that may contain thousands of random variables. Subset Simulation is an efficient reliability analysis technique that can handle the challenge of a high-dimensional space with a reduced number of structural analysis calls compared to crude Monte Carlo Simulation. In this contribution, firstly, we investigate the conditions for which Subset Simulation performs efficiently. Thereafter we propose an enhancement to the existing Subset Simulation schemes that shows significant potentials for enhancing the strategy for the starting of the Markov Chain Monte Carlo simulations whenever a new level is reached in the Subset Simulation. Finally, the information gathered from the simulations is investigated to verify that Subset Simulation provides meaningful results from a physical point of view.</div></div>","PeriodicalId":54583,"journal":{"name":"Probabilistic Engineering Mechanics","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142421001","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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Efficient optimization-based method for simultaneous calibration of load and resistance factors considering multiple target reliability indices 基于优化的高效方法，可同时校准考虑多个目标可靠性指数的载荷系数和阻力系数

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Probabilistic Engineering Mechanics Pub Date : 2024-10-01 DOI: 10.1016/j.probengmech.2024.103695

{"title":"Efficient optimization-based method for simultaneous calibration of load and resistance factors considering multiple target reliability indices","authors":"","doi":"10.1016/j.probengmech.2024.103695","DOIUrl":"10.1016/j.probengmech.2024.103695","url":null,"abstract":"<div><div>This study introduces an innovative optimization process for calibrating probabilistic load and resistance factors (LRFs) in limit state designs, effectively accommodating multiple target reliability indices. Given the impracticality of direct Monte Carlo simulations (MCS) for this task, a response surface method (RSM) is proposed to approximate load and resistance components separately rather than fitting conventional safety factors. This approach eliminates the need for additional implicit evaluations, thereby improving the efficiency of LRF calibration across multiple targets. The process is further enhanced by an adaptive boundary algorithm that updates search domains in real-time, streamlining the optimization. Validation through three examples—including one explicit and two implicit performance functions (a structural and a geotechnical example)—demonstrates that the method achieves accurate results with fewer iterations by dynamically narrowing search domains. Specifically, the accuracy of the proposed method is confirmed by comparing results with those from the literature for the explicit example and with basic MCS results applied to the initial implicit problems. Performance on the illustrative examples shows that the structural example achieves calibration for three targets within ten iterations. Additionally, this method eliminates the need for approximately ten thousand implicit evaluations when calculating limit state points for the geotechnical example.</div></div>","PeriodicalId":54583,"journal":{"name":"Probabilistic Engineering Mechanics","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142421053","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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Nonprobabilistic time-dependent reliability analysis for uncertain structures under interval process loads 区间过程载荷下不确定结构的非概率时间相关可靠性分析

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Probabilistic Engineering Mechanics Pub Date : 2024-10-01 DOI: 10.1016/j.probengmech.2024.103687

{"title":"Nonprobabilistic time-dependent reliability analysis for uncertain structures under interval process loads","authors":"","doi":"10.1016/j.probengmech.2024.103687","DOIUrl":"10.1016/j.probengmech.2024.103687","url":null,"abstract":"<div><div>In this paper, a novel nonprobabilistic analysis framework is proposed to evaluate the time-dependent reliability of uncertain structures under time-varying loads. Firstly, a novel uncertainty propagation method is developed by combining interval process integration and surrogate-based interval analysis and the correlation coefficient between responses of adjacent time steps is further analyzed. Subsequently, the nonprobabilistic time-dependent reliability is analyzed base on the first-passage theory and the established interval model. Unlike existing nonprobabilistic methods that consider time-invariant external loads, the proposed method applies an interval process to describe time-varying external loads, thereby offering a broader range of applicability. Compared to existing nonprobabilistic methods that consider time-varying loads, the proposed method establishes a more refined nonprobabilistic time-dependent reliability model based on the first passage theory, achieving higher accuracy. The effectiveness and superiority of the proposed method are validated through a numerical example and an engineering application.</div></div>","PeriodicalId":54583,"journal":{"name":"Probabilistic Engineering Mechanics","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142420999","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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Reliability analysis of cutting tools using transformed inverse Gaussian process-based wear modelling considering parameter dependence 考虑到参数依赖性，使用基于反高斯过程的磨损模型进行切削工具可靠性分析

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Probabilistic Engineering Mechanics Pub Date : 2024-10-01 DOI: 10.1016/j.probengmech.2024.103698

{"title":"Reliability analysis of cutting tools using transformed inverse Gaussian process-based wear modelling considering parameter dependence","authors":"","doi":"10.1016/j.probengmech.2024.103698","DOIUrl":"10.1016/j.probengmech.2024.103698","url":null,"abstract":"<div><div>Reliability analysis is crucial for ensuring the performability of the desired function. The cutting tool performs the machining operation at varied conditions to manufacture diverse products. During operation, the tool degrades stochastically in the form of wear. To avoid the unfavourable consequences occurring from severe tool wear, appropriate formulation of the tool reliability, considering threshold degradation level as the failure criterion, is crucial. However, the degradation of the tool during machining is impacted by the current state of the tool wear and operating conditions. Considering these, the present study proposes a state-dependent transformed inverse Gaussian (TIG) process incorporating the effects of operating conditions to develop the tool wear model. In order to evaluate the proposed method, tool wear experiments are conducted at different operating conditions following the Taguchi orthogonal array experimental design. The experimental data are utilised to estimate the parameters of the developed model using the Bayesian approach. Following the parameter estimation, tool reliability is evaluated under varying operating conditions. The comparison of the estimated median time to failure of the tools with the failure time observed in the validation experiments ensures the effectiveness of the proposed model. The proposed approach has the potential to estimate the reliability of the industrial products subjected to state-dependent degradation under varied operating conditions.</div></div>","PeriodicalId":54583,"journal":{"name":"Probabilistic Engineering Mechanics","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142531093","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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Laplace and Mellin transform for reconstructing the probability distribution by a limited amount of information 利用有限信息量重建概率分布的拉普拉斯和梅林变换

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Probabilistic Engineering Mechanics Pub Date : 2024-10-01 DOI: 10.1016/j.probengmech.2024.103700

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Quantified active learning Kriging model for structural reliability analysis 用于结构可靠性分析的量化主动学习克里金模型

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Probabilistic Engineering Mechanics Pub Date : 2024-10-01 DOI: 10.1016/j.probengmech.2024.103699

{"title":"Quantified active learning Kriging model for structural reliability analysis","authors":"","doi":"10.1016/j.probengmech.2024.103699","DOIUrl":"10.1016/j.probengmech.2024.103699","url":null,"abstract":"<div><div>A <em>quantified</em> active learning Kriging-based (qAK) methodology for structural reliability analysis is presented. The proposed approach is based on an updated probability density function (PDF), which is dominant in the vicinity of the limit-state surface. This PDF is created using weights based on an improved learning function called the <em>most probable misclassification</em> function. This function is used as a metric for efficiently updating the Kriging model, as it symmetrically quantifies the uncertainty of candidate points in terms of the model’s accuracy. The proposed approach accurately approximates the points that lie on the limit-state surface. Moreover, a probabilistic-based stopping criterion is proposed. The new support points are selected using the weighted <span><math><mi>K</mi></math></span>-means algorithm and the sample from the updated PDF. Thus, the method does not require solving an optimization problem or using a sampling algorithm. The proposed qAK methods are more reliable and robust than previous implementations of the Kriging method for structural reliability assessment. The proposed approach is presented within the framework of standard reliability methods, i.e., the Monte Carlo and the Subset Simulation methods. The efficiency of the proposed qAK methods is demonstrated with the aid of six case studies.</div></div>","PeriodicalId":54583,"journal":{"name":"Probabilistic Engineering Mechanics","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142552657","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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Real-time anomaly detection of the stochastically excited systems on spherical (S2) manifold 球形（S2）流形上随机激发系统的实时异常检测

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Probabilistic Engineering Mechanics Pub Date : 2024-10-01 DOI: 10.1016/j.probengmech.2024.103689

{"title":"Real-time anomaly detection of the stochastically excited systems on spherical (S2) manifold","authors":"","doi":"10.1016/j.probengmech.2024.103689","DOIUrl":"10.1016/j.probengmech.2024.103689","url":null,"abstract":"<div><div>Advanced analytical tools have become crucial in today’s constantly changing and complex systems. Real-time Principal Geodesic Analysis (RPGA) is a novel technique that provides a unique perspective for analyzing nonlinear data on differentiable manifolds. Traditional linear methods are often inadequate when exploring the complexities of such data. Orthogonal transformation techniques such as Principal Component Analysis (PCA) and Principal Geodesic Analysis (PGA) are highly desirable for condition monitoring stochastically excited systems in domains like mechanical, aerospace, and civil engineering. However, uncertainties and dynamic fluctuations necessitate robust analytical methods for early change detection to ensure safety, performance, and cost-effectiveness. Limitations posed by linear orthogonal transformation techniques such as PCA and its recursive counterparts make it imperative to adapt these techniques to nonlinear situations where data does not evolve in a flat Euclidean space. Significant advancements have been made in this field over recent decades, with data-driven real-time algorithms such as RPCA, RCCA, and RSSA providing reliable solutions for complex multidimensional problems. However, for curved space, the nonlinear RPGA technique takes center stage. It is known for its effectiveness in extracting meaningful information from the complex data stream. This paper explores the foundational concepts and methodologies underlying the transition from linear to nonlinear data analysis. By examining examples such as stochastic geometric oscillator on <span><math><msup><mrow><mtext>S</mtext></mrow><mrow><mn>2</mn></mrow></msup></math></span>, and the inverted spherical pendulum cart system navigating a rough surface, we illustrate the significance of reliable, real-time damage detection techniques provided by tools such as RPGA.</div></div>","PeriodicalId":54583,"journal":{"name":"Probabilistic Engineering Mechanics","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142420998","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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Confidence-based design optimization using multivariate kernel density estimation under insufficient input data 在输入数据不足的情况下，利用多变量核密度估计进行基于置信度的设计优化

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Probabilistic Engineering Mechanics Pub Date : 2024-10-01 DOI: 10.1016/j.probengmech.2024.103702

{"title":"Confidence-based design optimization using multivariate kernel density estimation under insufficient input data","authors":"","doi":"10.1016/j.probengmech.2024.103702","DOIUrl":"10.1016/j.probengmech.2024.103702","url":null,"abstract":"<div><div>The uncertainty quantification of the input statistical model in reliability-based design optimization (RBDO) has been widely investigated for accurate reliability analysis, and it could be estimated through its characteristics, cumulative experiences, and available data. However, uncertainty quantification of random variables in existing RBDO studies has exploited parametric distributions quantifying the uncertainty through the Bayes' theorem. In addition, a correlation between random variables is often underestimated due to a lack of knowledge and difficulty to describe the high-dimensional correlation. Hence, it has been a challenge to properly quantify input statistical model and its uncertainty. Therefore, a multivariate kernel density estimation (KDE) is employed to perform data-driven confidence-based design optimization (CBDO) for effective quantification of input model uncertainty. Any assumption on input distribution is not necessary since it is established only with the given input data. Moreover, the input model uncertainty due to insufficient data is quantified using bootstrapping and optimal adaptive bandwidth matrices through the Bayes’ theorem using cross-validation error. Consequently, the proposed CBDO with given input data is capable of finding a conservative optimum of RBDO accounting for both aleatory uncertainty of random variables and epistemic uncertainty induced by a limited number of input data through the multivariate KDE.</div></div>","PeriodicalId":54583,"journal":{"name":"Probabilistic Engineering Mechanics","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142437726","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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Stochastic design optimization of nonlinear structures under random seismic excitations using incremental dynamic analysis 利用增量动态分析对随机地震激励下的非线性结构进行随机优化设计

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Probabilistic Engineering Mechanics Pub Date : 2024-10-01 DOI: 10.1016/j.probengmech.2024.103707

{"title":"Stochastic design optimization of nonlinear structures under random seismic excitations using incremental dynamic analysis","authors":"","doi":"10.1016/j.probengmech.2024.103707","DOIUrl":"10.1016/j.probengmech.2024.103707","url":null,"abstract":"<div><div>The increasing demand for mitigating earthquake hazards has prompted substantial research attention towards performance-based seismic design of civil structures. Nevertheless, there remains limited exploration into optimizing complex structures while accounting for seismic uncertainties. This study seeks to address this gap by introducing an effective approach for optimizing designs of nonlinear structures under random seismic excitations. The key innovation lies in approximating structural failure probability through incremental dynamic analysis (IDA), leading to the development of a novel double-loop optimization method tailored for designing nonlinear structures exposed to stochastic seismic loading conditions. In the outer loop, geometric variables of structures are optimized using sequential quadratic programming; within the inner loop, IDA is adopted for structural analysis to quantify seismic uncertainty, and the resulting failure probability is then served as the optimization constraint for the outer loop. To validate its accuracy and efficacy, numerical investigations have been performed on two representative case studies utilizing <em>OpenSees</em>: a reinforced concrete column and a three-story steel frame. The findings affirm that IDA can precisely estimate failure probabilities associated with nonlinear structures experiencing random ground motions and demonstrate that this proposed methodology can effectively determine optimal geometries aimed at enhancing structural resilience against earthquakes across various levels of failure probabilities and bound constraints.</div></div>","PeriodicalId":54583,"journal":{"name":"Probabilistic Engineering Mechanics","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142572221","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0