Probabilistic Engineering Mechanics最新文献

英文中文

IF 3.5 3区工程技术 Q2 ENGINEERING, MECHANICAL

Probabilistic Engineering Mechanics

Pub Date : 2026-01-01

引用次数: 0

An adaptive Kriging framework with Quantile-Huber loss and dynamic failure-aware sampling for efficient structural reliability analysis 基于分位数- huber损失和动态故障感知采样的自适应Kriging框架结构可靠性分析

IF 3.5 3区工程技术 Q2 ENGINEERING, MECHANICAL

Probabilistic Engineering Mechanics

Pub Date : 2026-01-01 DOI: 10.1016/j.probengmech.2026.103891

Fujia Li , Tianzhe Wang , Guofa Li , Yatao Huo , Xiaodian Meng

In the field of structural reliability analysis, the high computational cost of function calls has long been a significant challenge. The Adaptive Kriging combined with Monte Carlo Simulation (AK-MCS) method effectively improves the efficiency of structural reliability analysis by substituting the actual performance function with a surrogate. However, this method still suffers from poor fitting performance for complex failure boundaries. Therefore, this paper proposes a novel dynamic failure-aware adaptive learning method, referred to as QH-DFA, based on the Quantile–Huber (QH) loss function. The method constructs a risk-aware metric centered on the QH loss function, thereby enhancing the metamodel's robustness. Subsequently, a failure-aware sampling weight function is designed to direct sampling toward critical boundary regions, improving the metamodel's ability to capture failure boundaries. To clearly demonstrate the effectiveness of the proposed method, three numerical examples and one engineering example are used for comparative verification. The results indicate that, compared with the U/EFF/ERF, QH-DFA shows significant advantages in both efficiency and accuracy.

在结构可靠性分析领域，函数调用的高计算成本一直是一个重大挑战。自适应克里格与蒙特卡罗仿真相结合的方法（AK-MCS）通过用替代函数代替实际性能函数，有效地提高了结构可靠性分析的效率。然而，对于复杂的破坏边界，该方法的拟合性能仍然较差。因此，本文提出了一种基于分位数- huber （Quantile-Huber， QH）损失函数的动态故障感知自适应学习方法QH- dfa。该方法构建了以QH损失函数为中心的风险感知度量，从而增强了元模型的鲁棒性。随后，设计了故障感知采样权函数，将采样指向临界边界区域，提高了元模型捕获故障边界的能力。为了清楚地证明所提方法的有效性，用三个数值算例和一个工程算例进行了对比验证。结果表明，与U/EFF/ERF相比，QH-DFA在效率和精度上都具有显著优势。

{"title":"An adaptive Kriging framework with Quantile-Huber loss and dynamic failure-aware sampling for efficient structural reliability analysis","authors":"Fujia Li , Tianzhe Wang , Guofa Li , Yatao Huo , Xiaodian Meng","doi":"10.1016/j.probengmech.2026.103891","DOIUrl":"10.1016/j.probengmech.2026.103891","url":null,"abstract":"<div><div>In the field of structural reliability analysis, the high computational cost of function calls has long been a significant challenge. The Adaptive Kriging combined with Monte Carlo Simulation (AK-MCS) method effectively improves the efficiency of structural reliability analysis by substituting the actual performance function with a surrogate. However, this method still suffers from poor fitting performance for complex failure boundaries. Therefore, this paper proposes a novel dynamic failure-aware adaptive learning method, referred to as QH-DFA, based on the Quantile–Huber (QH) loss function. The method constructs a risk-aware metric centered on the QH loss function, thereby enhancing the metamodel's robustness. Subsequently, a failure-aware sampling weight function is designed to direct sampling toward critical boundary regions, improving the metamodel's ability to capture failure boundaries. To clearly demonstrate the effectiveness of the proposed method, three numerical examples and one engineering example are used for comparative verification. The results indicate that, compared with the U/EFF/ERF, QH-DFA shows significant advantages in both efficiency and accuracy.</div></div>","PeriodicalId":54583,"journal":{"name":"Probabilistic Engineering Mechanics","volume":"83 ","pages":"Article 103891"},"PeriodicalIF":3.5,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145976642","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

Data-driven dimensionally decomposed generalized polynomial chaos expansion for forward uncertainty quantification 面向前向不确定性量化的数据驱动维数分解广义多项式混沌展开

IF 3.5 3区工程技术 Q2 ENGINEERING, MECHANICAL

Probabilistic Engineering Mechanics

Pub Date : 2026-01-01 DOI: 10.1016/j.probengmech.2026.103890

Hojun Choi, Eunho Heo, Dongjin Lee

Dimensionally decomposed generalized polynomial chaos expansion (DD-GPCE) efficiently performs forward uncertainty quantification (UQ) in complex engineering systems with high-dimensional random inputs of arbitrary distributions. However, constructing the measure-consistent orthonormal polynomial bases in DD-GPCE requires prior knowledge of input distributions, which is often unavailable in practice. This work introduces a data-driven DD-GPCE method that eliminates the need for such prior knowledge, extending its applicability to UQ with high-dimensional inputs. Input distributions are inferred directly from sample data using smoothed-bootstrap kernel density estimation (KDE), while the DD-GPCE framework enables KDE to handle high-dimensional inputs through low-dimensional marginal estimation. We then use the estimated input distributions to perform a whitening transformation via Monte Carlo Simulation, which enables generation of measure-consistent orthonormal basis functions. We demonstrate the accuracy of the proposed method in both mathematical examples and stochastic dynamic analysis for a practical three-dimensional mobility design involving twenty random inputs. The results indicate that the proposed method produces more accurate estimates of the output mean and variance compared to the conventional data-driven approach that assumes Gaussian input distributions.

维分解广义多项式混沌展开（DD-GPCE）有效地解决了具有任意分布的高维随机输入的复杂工程系统的前向不确定性量化问题。然而，在DD-GPCE中构造测度一致的标准正交多项式基需要事先知道输入分布，这在实践中往往是不可用的。这项工作引入了一种数据驱动的DD-GPCE方法，该方法消除了对此类先验知识的需求，将其扩展到具有高维输入的UQ。输入分布是使用平滑引导核密度估计（smooth -bootstrap kernel density estimation， KDE）直接从样本数据推断出来的，而DD-GPCE框架使KDE能够通过低维边际估计处理高维输入。然后，我们使用估计的输入分布通过蒙特卡罗模拟执行白化变换，从而生成测量一致的标准正交基函数。我们在数学实例和随机动力学分析中证明了所提出方法的准确性，该方法涉及20个随机输入的实际三维机动性设计。结果表明，与假设高斯输入分布的传统数据驱动方法相比，该方法可以更准确地估计输出均值和方差。

{"title":"Data-driven dimensionally decomposed generalized polynomial chaos expansion for forward uncertainty quantification","authors":"Hojun Choi, Eunho Heo, Dongjin Lee","doi":"10.1016/j.probengmech.2026.103890","DOIUrl":"10.1016/j.probengmech.2026.103890","url":null,"abstract":"<div><div>Dimensionally decomposed generalized polynomial chaos expansion (DD-GPCE) efficiently performs forward uncertainty quantification (UQ) in complex engineering systems with high-dimensional random inputs of arbitrary distributions. However, constructing the measure-consistent orthonormal polynomial bases in DD-GPCE requires prior knowledge of input distributions, which is often unavailable in practice. This work introduces a data-driven DD-GPCE method that eliminates the need for such prior knowledge, extending its applicability to UQ with high-dimensional inputs. Input distributions are inferred directly from sample data using smoothed-bootstrap kernel density estimation (KDE), while the DD-GPCE framework enables KDE to handle high-dimensional inputs through low-dimensional marginal estimation. We then use the estimated input distributions to perform a whitening transformation via Monte Carlo Simulation, which enables generation of measure-consistent orthonormal basis functions. We demonstrate the accuracy of the proposed method in both mathematical examples and stochastic dynamic analysis for a practical three-dimensional mobility design involving twenty random inputs. The results indicate that the proposed method produces more accurate estimates of the output mean and variance compared to the conventional data-driven approach that assumes Gaussian input distributions.</div></div>","PeriodicalId":54583,"journal":{"name":"Probabilistic Engineering Mechanics","volume":"83 ","pages":"Article 103890"},"PeriodicalIF":3.5,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145976645","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

IF 3.5 3区工程技术 Q2 ENGINEERING, MECHANICAL

Probabilistic Engineering Mechanics

Pub Date : 2026-01-01

引用次数: 0

IF 3.5 3区工程技术 Q2 ENGINEERING, MECHANICAL

Probabilistic Engineering Mechanics

Pub Date : 2026-01-01

引用次数: 0

IF 3.5 3区工程技术 Q2 ENGINEERING, MECHANICAL

Probabilistic Engineering Mechanics

Pub Date : 2026-01-01

引用次数: 0

Hybrid-surrogate-based prediction and reliability-based optimization of curling strength for bistable cylindrical shells 基于混合代币法的双稳圆柱壳卷曲强度预测与可靠性优化

IF 3.5 3区工程技术 Q2 ENGINEERING, MECHANICAL

Probabilistic Engineering Mechanics

Pub Date : 2026-01-01 DOI: 10.1016/j.probengmech.2025.103888

Haoyu Wang , Ning Guo , Hao Wang , Shilong Li , Zexing Yu , Chao Xu

Bistable composite thin-walled column–shells combine high specific stiffness and compact stowage, making them critical elements in aerospace deployable structures. Their curling and deployment involve strong geometric nonlinearities and stress concentrations near edges and transition zones, while material and manufacturing uncertainties complicate reliable design. This paper presents a hybrid surrogate model that couples enhanced Kriging, polynomial chaos expansion, and a radial basis function neural network to accurately predict curling strength under uncertainty. Leveraging the HSM (hybrid surrogate model), a derivative-based global sensitivity measure is employed to identify the dominant design variables, and a reliability-based design optimization is utilized to minimize the probability of matrix tensile failure. Numerical validation demonstrates that the proposed framework achieves a favorable balance between predictive accuracy and computational efficiency, substantially improving the reliability and engineering applicability of bistable composite structures.

双稳态复合薄壁柱壳结合了高比刚度和紧凑的装载，使其成为航空航天可展开结构的关键元素。它们的卷曲和展开涉及强烈的几何非线性和边缘和过渡区附近的应力集中，而材料和制造的不确定性使可靠的设计复杂化。提出了一种结合增强Kriging、多项式混沌展开和径向基函数神经网络的混合代理模型，以准确预测不确定条件下的卷曲强度。利用HSM（混合代理模型），利用基于导数的全局灵敏度度量来识别主导设计变量，并利用基于可靠性的设计优化来最小化基体拉伸破坏的概率。数值验证表明，该框架在预测精度和计算效率之间取得了良好的平衡，大大提高了双稳态复合材料结构的可靠性和工程适用性。

{"title":"Hybrid-surrogate-based prediction and reliability-based optimization of curling strength for bistable cylindrical shells","authors":"Haoyu Wang , Ning Guo , Hao Wang , Shilong Li , Zexing Yu , Chao Xu","doi":"10.1016/j.probengmech.2025.103888","DOIUrl":"10.1016/j.probengmech.2025.103888","url":null,"abstract":"<div><div>Bistable composite thin-walled column–shells combine high specific stiffness and compact stowage, making them critical elements in aerospace deployable structures. Their curling and deployment involve strong geometric nonlinearities and stress concentrations near edges and transition zones, while material and manufacturing uncertainties complicate reliable design. This paper presents a hybrid surrogate model that couples enhanced Kriging, polynomial chaos expansion, and a radial basis function neural network to accurately predict curling strength under uncertainty. Leveraging the HSM (hybrid surrogate model), a derivative-based global sensitivity measure is employed to identify the dominant design variables, and a reliability-based design optimization is utilized to minimize the probability of matrix tensile failure. Numerical validation demonstrates that the proposed framework achieves a favorable balance between predictive accuracy and computational efficiency, substantially improving the reliability and engineering applicability of bistable composite structures.</div></div>","PeriodicalId":54583,"journal":{"name":"Probabilistic Engineering Mechanics","volume":"83 ","pages":"Article 103888"},"PeriodicalIF":3.5,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145884334","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

IF 3.5 3区工程技术 Q2 ENGINEERING, MECHANICAL

Probabilistic Engineering Mechanics

Pub Date : 2026-01-01

引用次数: 0

IF 3.5 3区工程技术 Q2 ENGINEERING, MECHANICAL

Probabilistic Engineering Mechanics

Pub Date : 2026-01-01

引用次数: 0

Plug-in adaptive sampling for structural reliability: Key region Voronoi partitioning with cross-validated failure probability 结构可靠性的插件自适应采样：交叉验证失效概率的关键区域Voronoi划分

IF 3.5 3区工程技术 Q2 ENGINEERING, MECHANICAL

Probabilistic Engineering Mechanics

Pub Date : 2026-01-01 DOI: 10.1016/j.probengmech.2025.103887

Guangquan Yu , Ning Li , Xiaohang Zhang , Yuchen Hu , Cheng Chen

Accurate reliability estimation under tight computational budgets requires sampling strategies that both concentrate evaluations near the limit state and maintain sufficient global coverage. This study introduces Voronoi partitioning of key regions with cross-validation (CV) of failure probabilities (VK-CVF), a plug-in, learning-function-agnostic adaptive sampling framework that (i) identifies a critical region via surrogate model, (ii) partitions only that region into Voronoi subdomains, and (iii) ranks subdomains with a leave-one-out (LOO) failure-probability CV metric. New samples are placed preferentially in the most influential subdomains and, importantly, also near the centers of sub-sensitive units to provide directional, exploration-oriented guidance that balances exploitation and global learning. This targeted partitioning avoids global tessellation, yields quasi-uniform refinement near the limit state, and remains fully compatible with common learning functions (e.g., U-function, H-function). Across four benchmarks and a multi-hazard offshore jacket case, VK-CVF achieves accuracy comparable to that of AK-MCS while requiring about 50% fewer performance-function calls (range 35%–65%) and yields more uniform near-limit-state sampling. As a plug-in wrapper, it integrates with standard acquisition rules without altering their definitions.

在计算预算紧张的情况下，准确的可靠性估计要求采样策略既要集中在极限状态附近的评估，又要保持足够的全局覆盖。本研究引入了失效概率交叉验证（CV）关键区域的Voronoi划分（VK-CVF），这是一个插件式的、学习函数不可知的自适应采样框架，它(i)通过代理模型识别关键区域，（ii）仅将该区域划分为Voronoi子域，以及（iii）使用留一（LOO）失效概率CV度量对子域进行排序。新样本被优先放置在最具影响力的子域，重要的是，也靠近亚敏感单元的中心，以提供定向的、面向探索的指导，平衡开发和全局学习。这种有针对性的划分避免了全局镶嵌，在极限状态附近产生准一致的细化，并且与常见的学习函数（例如，u函数，h函数）保持完全兼容。在四个基准测试和多危险海上套管情况下，VK-CVF达到了与AK-MCS相当的精度，而所需的性能函数调用减少了约50%（范围为35%-65%），并产生了更均匀的近极限状态采样。作为一个插件包装器，它集成了标准的获取规则，而不改变它们的定义。

{"title":"Plug-in adaptive sampling for structural reliability: Key region Voronoi partitioning with cross-validated failure probability","authors":"Guangquan Yu , Ning Li , Xiaohang Zhang , Yuchen Hu , Cheng Chen","doi":"10.1016/j.probengmech.2025.103887","DOIUrl":"10.1016/j.probengmech.2025.103887","url":null,"abstract":"<div><div>Accurate reliability estimation under tight computational budgets requires sampling strategies that both concentrate evaluations near the limit state and maintain sufficient global coverage. This study introduces Voronoi partitioning of key regions with cross-validation (CV) of failure probabilities (<span>VK-CVF</span>), a plug-in, learning-function-agnostic adaptive sampling framework that (i) identifies a critical region via surrogate model, (ii) partitions only that region into Voronoi subdomains, and (iii) ranks subdomains with a leave-one-out (LOO) failure-probability CV metric. New samples are placed preferentially in the most influential subdomains and, importantly, also near the centers of sub-sensitive units to provide directional, exploration-oriented guidance that balances exploitation and global learning. This targeted partitioning avoids global tessellation, yields quasi-uniform refinement near the limit state, and remains fully compatible with common learning functions (e.g., U-function, H-function). Across four benchmarks and a multi-hazard offshore jacket case, <span>VK-CVF</span> achieves accuracy comparable to that of AK-MCS while requiring about 50% fewer performance-function calls (range 35%–65%) and yields more uniform near-limit-state sampling. As a plug-in wrapper, it integrates with standard acquisition rules without altering their definitions.</div></div>","PeriodicalId":54583,"journal":{"name":"Probabilistic Engineering Mechanics","volume":"83 ","pages":"Article 103887"},"PeriodicalIF":3.5,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145884512","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

首页上一页

下一页尾页

类型

全部化学•材料生命科学医学物理工程技术环境•农林材料科学地球科学法学管理学化学环境科学与生态学计算机科学教育学经济学农林科学人文科学生物学数学物理与天体物理心理学综合性期刊其他工业工程理学历史学农学文学信息工程

数据库

全部 ACS Publications Elsevier ieeexplore Springer The Royal Society of Chemistry Wiley

期刊

Probabilistic Engineering Mechanics

全部 Acc. Chem. Res. ACS Applied Bio Materials ACS Appl. Electron. Mater. ACS Appl. Energy Mater. ACS Appl. Mater. Interfaces ACS Appl. Nano Mater. ACS Appl. Polym. Mater. ACS BIOMATER-SCI ENG ACS Catal. ACS Cent. Sci. ACS Chem. Biol. ACS Chemical Health & Safety ACS Chem. Neurosci. ACS Comb. Sci. ACS Earth Space Chem. ACS Energy Lett. ACS Infect. Dis. ACS Macro Lett. ACS Mater. Lett. ACS Med. Chem. Lett. ACS Nano ACS Omega ACS Photonics ACS Sens. ACS Sustainable Chem. Eng. ACS Synth. Biol. Anal. Chem. BIOCHEMISTRY-US Bioconjugate Chem. BIOMACROMOLECULES Chem. Res. Toxicol. Chem. Rev. Chem. Mater. CRYST GROWTH DES ENERG FUEL Environ. Sci. Technol. Environ. Sci. Technol. Lett. Eur. J. Inorg. Chem. IND ENG CHEM RES Inorg. Chem. J. Agric. Food. Chem. J. Chem. Eng. Data J. Chem. Educ. J. Chem. Inf. Model. J. Chem. Theory Comput. J. Med. Chem. J. Nat. Prod. J PROTEOME RES J. Am. Chem. Soc. LANGMUIR MACROMOLECULES Mol. Pharmaceutics Nano Lett. Org. Lett. ORG PROCESS RES DEV ORGANOMETALLICS J. Org. Chem. J. Phys. Chem. J. Phys. Chem. A J. Phys. Chem. B J. Phys. Chem. C J. Phys. Chem. Lett. Analyst Anal. Methods Biomater. Sci. Catal. Sci. Technol. Chem. Commun. Chem. Soc. Rev. CHEM EDUC RES PRACT CRYSTENGCOMM Dalton Trans. Energy Environ. Sci. ENVIRON SCI-NANO ENVIRON SCI-PROC IMP ENVIRON SCI-WAT RES Faraday Discuss. Food Funct. Green Chem. Inorg. Chem. Front. Integr. Biol. J. Anal. At. Spectrom. J. Mater. Chem. A J. Mater. Chem. B J. Mater. Chem. C Lab Chip Mater. Chem. Front. Mater. Horiz. MEDCHEMCOMM Metallomics Mol. Biosyst. Mol. Syst. Des. Eng. Nanoscale Nanoscale Horiz. Nat. Prod. Rep. New J. Chem. Org. Biomol. Chem. Org. Chem. Front. PHOTOCH PHOTOBIO SCI PCCP Polym. Chem.

﹀