Structural Safety最新文献_第4页

Multi-point Bayesian active learning reliability analysis 多点贝叶斯主动学习信度分析

IF 5.7 1区工程技术

Structural Safety Pub Date : 2024-12-06 DOI: 10.1016/j.strusafe.2024.102557

Tong Zhou , Xujia Zhu , Tong Guo , You Dong , Michael Beer

{"title":"Multi-point Bayesian active learning reliability analysis","authors":"Tong Zhou , Xujia Zhu , Tong Guo , You Dong , Michael Beer","doi":"10.1016/j.strusafe.2024.102557","DOIUrl":"10.1016/j.strusafe.2024.102557","url":null,"abstract":"<div><div>This manuscript presents a novel Bayesian active learning reliability method integrating both Bayesian failure probability estimation and Bayesian decision-theoretic multi-point enrichment process. First, an epistemic uncertainty measure called integrated margin probability (IMP) is proposed as an upper bound for the mean absolute deviation of failure probability estimated by Kriging. Then, adhering to the Bayesian decision theory, a look-ahead learning function called multi-point stepwise margin reduction (MSMR) is defined to quantify the possible reduction of IMP brought by adding a batch of new samples in expectation. The cost-effective implementation of MSMR-based multi-point enrichment process is conducted by three key workarounds: (a) Thanks to analytical tractability of the inner integral, the MSMR reduces to a single integral. (b) The remaining single integral in the MSMR is numerically computed with the rational truncation of the quadrature set. (c) A heuristic treatment of maximizing the MSMR is devised to fastly select a batch of best next points per iteration, where the prescribed scheme or adaptive scheme is used to specify the batch size. The proposed method is tested on two benchmark examples and two dynamic reliability problems. The results indicate that the adaptive scheme in the MSMR gains a good balance between the computing resource consumption and the overall computational time. Then, the MSMR fairly outperforms those existing leaning functions and parallelization strategies in terms of the accuracy of failure probability estimate, the number of iterations, as well as the number of performance function evaluations, especially in complex dynamic reliability problems.</div></div>","PeriodicalId":21978,"journal":{"name":"Structural Safety","volume":"114 ","pages":"Article 102557"},"PeriodicalIF":5.7,"publicationDate":"2024-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143151098","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

Disaster risk-informed optimization using buffered failure probability for regional-scale building retrofit strategy 区域尺度建筑改造策略中基于缓冲失效概率的灾害风险优化

IF 5.7 1区工程技术

Structural Safety Pub Date : 2024-12-05 DOI: 10.1016/j.strusafe.2024.102556

Uichan Seok , Ji-Eun Byun , Junho Song

{"title":"Disaster risk-informed optimization using buffered failure probability for regional-scale building retrofit strategy","authors":"Uichan Seok , Ji-Eun Byun , Junho Song","doi":"10.1016/j.strusafe.2024.102556","DOIUrl":"10.1016/j.strusafe.2024.102556","url":null,"abstract":"<div><div>Regional retrofit planning of buildings is critical to address the increasing threat of natural disasters exacerbated by urban growth and climate change. To identify an optimal plan, this paper introduces a novel optimization framework. By integrating performance-based engineering (PBE) and reliability-based optimization (RBO), we propose buffered optimization and reliability method based mixed integer linear programming (BORM-MILP). The proposed formulation can identify optimal solutions using general optimization solvers, while handling a large number of PBE samples and buildings. Furthermore, the formulation introduces a modified active-set strategy tailored to regional-scale building retrofit optimization problems, further reducing computational memory. The proposed optimization framework is validated by a benchmark example of Seaside, Oregon. The optimization results are presented along in a map, offering visual support for decision-making processes. The application results are further investigated to analyze computational efficiency of the proposed active-set strategy, study convergence to the normal distribution, and identify a dominant factor for the building retrofit selection.</div></div>","PeriodicalId":21978,"journal":{"name":"Structural Safety","volume":"114 ","pages":"Article 102556"},"PeriodicalIF":5.7,"publicationDate":"2024-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143151099","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

Reliability analysis for data-driven noisy models using active learning 基于主动学习的数据驱动噪声模型可靠性分析

IF 5.7 1区工程技术

Structural Safety Pub Date : 2024-11-26 DOI: 10.1016/j.strusafe.2024.102543

Anderson V. Pires, Maliki Moustapha, Stefano Marelli, Bruno Sudret

{"title":"Reliability analysis for data-driven noisy models using active learning","authors":"Anderson V. Pires, Maliki Moustapha, Stefano Marelli, Bruno Sudret","doi":"10.1016/j.strusafe.2024.102543","DOIUrl":"10.1016/j.strusafe.2024.102543","url":null,"abstract":"<div><div>Reliability analysis aims at estimating the failure probability of an engineering system. It often requires multiple runs of a limit-state function, which usually relies on computationally intensive simulations. Traditionally, these simulations have been considered deterministic, <em>i.e.</em> running them multiple times for a given set of input parameters always produces the same output. However, this assumption does not always hold, as many studies in the literature report non-deterministic computational simulations (also known as noisy models). In such cases, running the simulations multiple times with the same input will result in different outputs. Similarly, data-driven models that rely on real-world data may also be affected by noise. This characteristic poses a challenge when performing reliability analysis, as many classical methods, such as FORM and SORM, are tailored to deterministic models. To bridge this gap, this paper provides a novel methodology to perform reliability analysis on models contaminated by noise. In such cases, noise introduces latent uncertainty into the reliability estimator, leading to an incorrect estimation of the real underlying reliability index, even when using Monte Carlo simulation. To overcome this challenge, we propose the use of denoising regression-based surrogate models within an active learning reliability analysis framework. Specifically, we combine Gaussian process regression with a noise-aware learning function to efficiently estimate the probability of failure of the underlying noise-free model. We showcase the effectiveness of this methodology on standard benchmark functions and a finite element model of a realistic structural frame.</div></div>","PeriodicalId":21978,"journal":{"name":"Structural Safety","volume":"112 ","pages":"Article 102543"},"PeriodicalIF":5.7,"publicationDate":"2024-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142748044","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An Adaptive Gaussian Mixture Model for structural reliability analysis using convolution search technique 利用卷积搜索技术进行结构可靠性分析的自适应高斯混合模型

IF 5.7 1区工程技术

Structural Safety Pub Date : 2024-11-24 DOI: 10.1016/j.strusafe.2024.102548

Futai Zhang , Jun Xu , Zhiqiang Wan

{"title":"An Adaptive Gaussian Mixture Model for structural reliability analysis using convolution search technique","authors":"Futai Zhang , Jun Xu , Zhiqiang Wan","doi":"10.1016/j.strusafe.2024.102548","DOIUrl":"10.1016/j.strusafe.2024.102548","url":null,"abstract":"<div><div>Non-parametric probability density estimation has gained popularity due to its flexibility and ease of use without requiring prior assumptions about distribution types. Notable examples include Kernel Density Estimation, Gaussian Mixture Model (GMM), the Mellin transform, and the Generalized Distribution Reconstruction (GDR) method, etc. However, these methods can encounter issues such as tail oscillation and sensitivity to initial guesses, particularly in the context of structural reliability analysis. To improve accuracy, this paper proposes an Adaptive Gaussian Mixture Model method. This method uses the inverse Fourier relationship between the Characteristic Function (CF) and the Probability Density Function (PDF), combined with a convolution search technique for parameter estimation. First, a more accurate expression for the CF is introduced, where the undetermined parameters are specified based on the numerically estimated CF curve. Then, a convolution search domain is developed to determine these parameters, including weight coefficients, mean domain, and standard deviation domain. Compared to the conventional methods for parameter estimation, the proposed convolution search technique can effectively avoid the problems of overfitting and initial parameter sensitivity. Using these parameters, the PDF is reconstructed and evolves into an Adaptive Gaussian Mixture Model. Numerical investigations are conducted to validate the efficacy of the proposed method, with comparisons made to the Mellin transform, GDR, Classic GMM, and other parametric methods.</div></div>","PeriodicalId":21978,"journal":{"name":"Structural Safety","volume":"112 ","pages":"Article 102548"},"PeriodicalIF":5.7,"publicationDate":"2024-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142719985","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

The generalized first-passage probability considering temporal correlation and its application in dynamic reliability analysis 考虑时间相关性的广义首通概率及其在动态可靠度分析中的应用

IF 5.7 1区工程技术

Structural Safety Pub Date : 2024-11-22 DOI: 10.1016/j.strusafe.2024.102547

Xian-Lin Yang , Ming-Ming Jia , Da-Gang Lu

{"title":"The generalized first-passage probability considering temporal correlation and its application in dynamic reliability analysis","authors":"Xian-Lin Yang , Ming-Ming Jia , Da-Gang Lu","doi":"10.1016/j.strusafe.2024.102547","DOIUrl":"10.1016/j.strusafe.2024.102547","url":null,"abstract":"<div><div>In the traditional up-crossing rate approaches, the absence of consideration for correlation among crossing events often results in significant inaccuracies, particularly in scenarios involving stochastic processes with high autocorrelation and low thresholds. To fundamentally address these issues and limitations, the probability density function of the first passage time represented by the high-dimensional joint probability density function was investigated, and the equiprobable joint Gaussian (E-PHIn) method is proposed to prevent the redundant counting of the same crossing event. The innovation of the developed method is that it accounts for the correlation among different time instances of the stochastic process and allows for direct integration to derive the first-passage probabilities. When dealing with stochastic processes with unknown marginal distributions, the method of moments was introduced, complementing the E-PHIn method. Meanwhile, corresponding dimensionality reduction strategies are offered to improve computational efficiency. Through theoretical analysis and case studies, the results indicate that the conditional up-crossing rate represents the probability density function of the first-passage time. The E-PHIn method effectively addresses the first-passage problem for stochastic processes with either known or unknown marginal probability density functions. It fills the gap in traditional up-crossing rate approaches within the domain of nonlinear dynamic reliability. For the example structures, the E-PHIn method demonstrates higher accuracy compared to traditional point-based PDEM. Compared to MCS, the E-PHIn method significantly improves analytical efficiency while maintaining high precision for low-probability failure events.</div></div>","PeriodicalId":21978,"journal":{"name":"Structural Safety","volume":"112 ","pages":"Article 102547"},"PeriodicalIF":5.7,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142748043","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

A novel deterministic sampling approach for the reliability analysis of high-dimensional structures 用于高维结构可靠性分析的新型确定性抽样方法

IF 5.7 1区工程技术

Structural Safety Pub Date : 2024-11-20 DOI: 10.1016/j.strusafe.2024.102545

Yang Zhang , Jun Xu , Enrico Zio

{"title":"A novel deterministic sampling approach for the reliability analysis of high-dimensional structures","authors":"Yang Zhang , Jun Xu , Enrico Zio","doi":"10.1016/j.strusafe.2024.102545","DOIUrl":"10.1016/j.strusafe.2024.102545","url":null,"abstract":"<div><div>Overcoming the “curse of dimensionality” in high-dimensional reliability analysis is still an enduring challenge. This paper proposes an innovative deterministic sampling method designed to overcome this challenge. The approach starts with a two-dimensional uniform point set, generated using the good lattice point method. This set is then refined through the cutting method to produce a specific number of points. A novel generating vector is computed based on this method, enabling the generation of the targeted high-dimensional point set through a strategic dimension-by-dimension mapping. Notably, this method eliminates the need for complex congruence computation and primitive root optimization, enhancing its efficiency for high-dimensional sampling. The resulting point set is deterministic and uniform, greatly reducing variability in reliability analysis. Then, the proposed approach is integrated into the fractional exponential moment-based maximum entropy method with the Box–Cox transform. This integration efficiently recovers the probability distribution for the limit state function (LSF) with high-dimensional inputs, enabling precise assessment of the failure probability. The efficacy of the proposed method is demonstrated through three high-dimensional numerical examples, involving both explicit and implicit LSFs, highlighting its applicability for high-dimensional reliability analysis of structures.</div></div>","PeriodicalId":21978,"journal":{"name":"Structural Safety","volume":"112 ","pages":"Article 102545"},"PeriodicalIF":5.7,"publicationDate":"2024-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142700580","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

A stratified beta-sphere sampling method combined with important sampling and active learning for rare event analysis 用于罕见事件分析的分层贝塔球取样法与重要取样和主动学习相结合

IF 5.7 1区工程技术

Structural Safety Pub Date : 2024-11-20 DOI: 10.1016/j.strusafe.2024.102546

Fangqi Hong , Jingwen Song , Pengfei Wei , Ziteng Huang , Michael Beer

{"title":"A stratified beta-sphere sampling method combined with important sampling and active learning for rare event analysis","authors":"Fangqi Hong , Jingwen Song , Pengfei Wei , Ziteng Huang , Michael Beer","doi":"10.1016/j.strusafe.2024.102546","DOIUrl":"10.1016/j.strusafe.2024.102546","url":null,"abstract":"<div><div>Accurate and efficient estimation of small failure probability subjected to high-dimensional and multiple failure domains is still a challenging task in structural reliability engineering. In this paper, we propose a stratified beta-spheres sampling method (SBSS) to tackle this task. Initially, the whole support space of random input variables is divided into a series of subdomains by using multiple specified beta-spheres, which is a hypersphere centered in the origin in standard normal space, then, the corresponding samples truncated by beta-spheres are generated explicitly and efficiently. Based on the truncated samples, the real failure probability can be estimated by the sum of failure probabilities of these subdomains. Next, we discuss and demonstrate the unbiasedness of the estimation of failure probability. The proposed method stands out for inheriting the advantages of Monte Carlo simulation (MCS) for highly nonlinear, high-dimensional problems, and problems with multiple failure domains, while overcoming the disadvantages of MCS for rare event. Furthermore, the SBSS method equipped with importance sampling technique (SBSS-IS) is also proposed to improve the robustness of estimation. Additionally, we combine the proposed SBSS and SBSS-IS methods with GPR model and active learning strategy so as to further substantially reduce the computational cost under the desired requirement of estimated accuracy. Finally, the superiorities of the proposed methods are demonstrated by six examples with different problem settings.</div></div>","PeriodicalId":21978,"journal":{"name":"Structural Safety","volume":"112 ","pages":"Article 102546"},"PeriodicalIF":5.7,"publicationDate":"2024-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142700579","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

An augmented integral method for probability distribution evaluation of performance functions 性能函数概率分布评估的增强积分法

IF 5.7 1区工程技术

Structural Safety Pub Date : 2024-11-06 DOI: 10.1016/j.strusafe.2024.102544

Yan-Gang Zhao, Chang-Xing Zou, Xuan-Yi Zhang, Ye-Yao Weng

{"title":"An augmented integral method for probability distribution evaluation of performance functions","authors":"Yan-Gang Zhao, Chang-Xing Zou, Xuan-Yi Zhang, Ye-Yao Weng","doi":"10.1016/j.strusafe.2024.102544","DOIUrl":"10.1016/j.strusafe.2024.102544","url":null,"abstract":"<div><div>The paper proposes an efficient augmented integral method for probability distribution evaluation of performance functions. In the proposed method, the performance function is augmented by adding an auxiliary random variable, whose probability density function (PDF) and cumulative distribution function (CDF) are formulated as the integrations of the original performance function with respect to basic random variables. The optimal auxiliary random variable is determined to provide an accurate estimation of the integrations by investigating the geometric properties of integrands and a distribution parameter optimization approach based on moment analysis. According to the convolution formula, the relationship between the PDFs of the augmented performance function and the original performance function is clarified. Then, the PDF of the original performance function is calculated by solving an unconstrained optimization problem that is established using the convolution formula. Finally, four numerical examples are investigated to demonstrate the efficiency and accuracy of the proposed method for structural reliability analysis. The results indicate that the proposed method can evaluate the probability distribution of performance functions accurately and efficiently, even when the performance functions are strongly nonlinear and implicit.</div></div>","PeriodicalId":21978,"journal":{"name":"Structural Safety","volume":"112 ","pages":"Article 102544"},"PeriodicalIF":5.7,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142700578","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

Preface of the special issue: The Joint Committee of Structural Safety: past, present and a perspective on the future 特刊前言：结构安全联合委员会：过去，现在和对未来的展望

IF 5.7 1区工程技术

Structural Safety Pub Date : 2024-10-23 DOI: 10.1016/j.strusafe.2024.102542

Jochen Köhler, Ton Vrouwenvelder, Michael Havbro Faber (President, and Past Presidents of the Joint Committee on Structural Safety), Maria Pina Limongelli (JCSS Reporter and Executive Guest Editor of the Special Issue)

引用次数: 0

Bivariate cubic normal distribution for non-Gaussian problems 非高斯问题的二元三次正态分布

IF 5.7 1区工程技术

Structural Safety Pub Date : 2024-10-16 DOI: 10.1016/j.strusafe.2024.102541

Xiang-Wei Li, Xuan-Yi Zhang, Yan-Gang Zhao

{"title":"Bivariate cubic normal distribution for non-Gaussian problems","authors":"Xiang-Wei Li, Xuan-Yi Zhang, Yan-Gang Zhao","doi":"10.1016/j.strusafe.2024.102541","DOIUrl":"10.1016/j.strusafe.2024.102541","url":null,"abstract":"<div><div>Probabilistic models play critical role in various engineering fields. Numerous critical issues exist in probabilistic modeling, especially for non-Gaussian correlated random variables. Traditional parameter-based bivariate distribution models are typically developed for specific types of random variables, which limits their flexibility and applicability. In this study, a flexible bivariate distribution model is proposed, in which the joint cumulative distribution function (JCDF) is derived by expressing the probability as the summation of three basic probabilities corresponding to simple functions. These probabilities are computed using a univariate cubic normal distribution, and thus the proposed model is named as bivariate cubic normal (BCN) distribution. The proposed BCN distribution has been applied in modeling several common bivariate distributions and actual engineering datasets. Results show that the BCN distribution accurately fits the JCDFs of both theoretical distributions and practical datasets, offering significant improvement over existing models. Furthermore, the proposed BCN distribution is utilized in seismic reliability assessment and the calculation of the mean recurrence interval and hazard curve of hurricane wind speed and storm size. Results demonstrate that the BCN distribution excels in modeling and matching capabilities, proving its accuracy and effectiveness in civil engineering applications.</div></div>","PeriodicalId":21978,"journal":{"name":"Structural Safety","volume":"112 ","pages":"Article 102541"},"PeriodicalIF":5.7,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142533296","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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