Structural Safety最新文献_第4页

Estimation of simulation failure set with active learning based on Gaussian Process classifiers and random set theory 基于高斯过程分类器和随机集理论的主动学习仿真故障集估计

IF 5.7 1区工程技术 Q1 ENGINEERING, CIVIL

Structural Safety

Pub Date : 2025-06-18 DOI: 10.1016/j.strusafe.2025.102607

Morgane Menz , Miguel Munoz Zuniga , Delphine Sinoquet

A wide range of industrial applications require numerous time-consuming simulations across various input sets, such as for optimization, calibration, or reliability assessments. In that context, some simulation failures or instabilities can be observed, due for instance, to convergence issues of the numerical scheme of complex partial derivative equations. Most of the time, the set of inputs corresponding to failures is not known a priori and thus may be associated to a hidden constraint. Since the observation of a simulation failure regarding this unknown constraint may be as costly as a feasible expensive simulation, we seek to learn the feasible set of inputs and thus target areas without simulation failure before further analysis. In this classification context, we propose to learn the feasible domain with a new adaptive Gaussian Process Classifier. The proposed methodology is a batch-mode active learning classification strategy that reduces uncertainty step by step, using a random set paradigm and a Gaussian Process Classifiers. The performance of this strategy is demonstrated on several hidden-constrained problems, particularly in the context of a wind turbine simulator-based reliability analysis.

广泛的工业应用需要跨各种输入集进行大量耗时的模拟，例如优化，校准或可靠性评估。在这种情况下，可以观察到一些模拟失败或不稳定性，例如，由于复杂偏导数方程数值格式的收敛问题。大多数情况下，与失败相对应的输入集是未知的，因此可能与隐藏的约束相关联。由于观察关于这个未知约束的模拟失败可能与可行的昂贵模拟一样昂贵，因此在进一步分析之前，我们寻求学习可行的输入集，从而学习没有模拟失败的目标区域。在这种分类背景下，我们提出了一种新的自适应高斯过程分类器来学习可行域。所提出的方法是使用随机集范式和高斯过程分类器逐步减少不确定性的批处理模式主动学习分类策略。该策略的性能在几个隐藏约束问题上得到了验证，特别是在基于风力发电机模拟器的可靠性分析的背景下。

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引用次数: 0

Probabilistic calibration of design resistance models for the anchorage length of prestressing strands considering model uncertainty 考虑模型不确定性的预应力锚固长度设计阻力模型的概率校正

IF 5.7 1区工程技术 Q1 ENGINEERING, CIVIL

Structural Safety

Pub Date : 2025-06-18 DOI: 10.1016/j.strusafe.2025.102631

Sergio Belluco, Flora Faleschini

This study investigates the reliability and the model uncertainty of the anchorage length resistance models proposed in the 2nd generation Eurocode 2 and fib Model Code 2020. First, the two resistance models and their safety format are presented and discussed. Then, the probability distribution of the model uncertainty is estimated comparing the model predictions with a large set of flexural tests collected from the scientific literature. According to the results, the prestress release method and the strand surface conditions are the two variables affecting most the model uncertainty. Furthermore, it is demonstrated that anchorage lengths predicted with fib Model Code 2020 exceed the expected target level of reliability and they could be reduced, particularly for gradual prestress release. Conversely, anchorage lengths calculated according to the 2nd generation Eurocode 2 in case of sudden prestress release need to be increased to guarantee the expected level of reliability. For the same code, no significant changes are necessary in case of gradual prestress release.

本文研究了第二代欧洲规范2和fib模型规范2020中提出的锚固长度阻力模型的可靠性和模型不确定性。首先，提出并讨论了两种阻力模型及其安全格式。然后，将模型预测与从科学文献中收集的大量弯曲试验进行比较，估计模型不确定性的概率分布。结果表明，预应力释放方法和钢绞线表面条件是影响模型不确定性最大的两个变量。此外，还证明了fib模型规范2020预测的锚固长度超过了预期的目标可靠性水平，并且可以减小锚固长度，特别是在逐渐释放预应力的情况下。相反，根据第二代欧洲规范2计算的预应力突然释放情况下的锚固长度需要增加，以保证预期的可靠度水平。对于相同的规范，如果预应力逐渐释放，则不需要进行重大更改。

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引用次数: 0

Spatial variability identification of carbonation depth in concrete using Bayesian networks 利用贝叶斯网络识别混凝土碳化深度的空间变异性

IF 5.7 1区工程技术 Q1 ENGINEERING, CIVIL

Structural Safety

Pub Date : 2025-06-18 DOI: 10.1016/j.strusafe.2025.102632

Thanh-Binh Tran , Emilio Bastidas-Arteaga

Accurate prediction of carbonation depth is crucial for evaluating the durability and service life of reinforced concrete structures. Traditional methods for assessing carbonation depth often involve destructive testing, which is both costly and time-consuming, and yields results with limited accuracy, thus restricting their practical applicability. To address these shortcomings, this research introduces a novel two-step procedure that leverages inspection data on concrete porosity and saturation degree to estimate carbonation depth. By integrating Bayesian networks and considering the influence of spatial variability, the proposed methodology aims to enhance prediction accuracy compared to existing techniques. The study comprehensively investigates the impact of various factors, including the use of individual or combined inspection data, spatial dependence, and inspection distance, on prediction performance. The findings demonstrate the effectiveness of the proposed approach in capturing complex interactions between concrete properties, carbonation depth, and spatial variability. This research contributes to the advancement of non-destructive evaluation methods for concrete structures and provides valuable insights for optimizing inspection strategies.

准确预测碳化深度是评价钢筋混凝土结构耐久性和使用寿命的关键。评估碳酸化深度的传统方法通常涉及破坏性测试，既昂贵又耗时，而且结果精度有限，从而限制了其实际适用性。为了解决这些缺点，本研究引入了一种新的两步程序，该程序利用混凝土孔隙度和饱和度的检测数据来估计碳化深度。通过整合贝叶斯网络并考虑空间变异性的影响，与现有技术相比，该方法旨在提高预测精度。该研究全面考察了各种因素对预测性能的影响，包括单个或组合检测数据的使用、空间依赖性和检测距离。研究结果表明，所提出的方法在捕获混凝土性能、碳化深度和空间变异性之间复杂的相互作用方面是有效的。本研究对混凝土结构无损评价方法的发展具有重要意义，并为优化检测策略提供了有价值的见解。

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引用次数: 0

Reliability analysis for non-deterministic limit-states using stochastic emulators 基于随机仿真器的非确定性极限状态可靠性分析

IF 5.7 1区工程技术 Q1 ENGINEERING, CIVIL

Structural Safety

Pub Date : 2025-06-11 DOI: 10.1016/j.strusafe.2025.102621

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

Reliability analysis is a sub-field of uncertainty quantification that assesses the probability of a system performing as intended under various uncertainties. Traditionally, this analysis relies on deterministic models, where experiments are repeatable, i.e. they produce consistent outputs for a given set of inputs. However, real-world systems often exhibit stochastic behavior, leading to non-repeatable outcomes. These so-called stochastic simulators produce different outputs each time the model is run, even with fixed inputs.

This paper formally introduces reliability analysis for stochastic models and addresses it by using suitable surrogate models to lower its typically high computational cost. Specifically, we focus on the recently introduced generalized lambda models and stochastic polynomial chaos expansions. These emulators are designed to learn the inherent randomness of the simulator’s response and enable efficient uncertainty quantification at a much lower cost than traditional Monte Carlo simulation.

We validate our methodology through three case studies. First, using an analytical function with a closed-form solution, we demonstrate that the emulators converge to the correct solution. Second, we present results obtained from the surrogates using a toy example of a simply supported beam. Finally, we apply the emulators to perform reliability analysis on a realistic wind turbine case study, where only a dataset of simulation results is available.

可靠性分析是不确定性量化的一个子领域，用于评估系统在各种不确定性下按预期运行的概率。传统上，这种分析依赖于确定性模型，其中实验是可重复的，即它们对给定的一组输入产生一致的输出。然而，现实世界的系统往往表现出随机行为，导致不可重复的结果。这些所谓的随机模拟器每次运行模型都会产生不同的输出，即使输入是固定的。本文正式介绍了随机模型的可靠性分析，并通过使用合适的替代模型来解决这一问题，以降低其典型的高计算成本。具体来说，我们关注最近引入的广义lambda模型和随机多项式混沌展开式。这些仿真器旨在学习模拟器响应的固有随机性，并以比传统蒙特卡罗仿真低得多的成本实现有效的不确定性量化。我们通过三个案例研究验证了我们的方法。首先，使用具有封闭解的解析函数，我们证明了仿真器收敛到正确的解。其次，我们提出的结果，从代理人使用一个玩具的例子，一个简单的支持梁。最后，我们应用仿真器对一个实际的风力发电机案例进行了可靠性分析，其中只有仿真结果的数据集可用。

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引用次数: 0

Corrigendum to “Evaluating the importance of spatial variability of corrosion initiation parameters for the risk-based maintenance of reinforced concrete marine structures” [Struct. Saf. 114 (2025) 102568] “评估腐蚀起始参数的空间变异性对基于风险的钢筋混凝土海洋结构维修的重要性”的勘误表[结构]。联邦公报114 (2025)102568]

IF 5.7 1区工程技术 Q1 ENGINEERING, CIVIL

Structural Safety

Pub Date : 2025-06-09 DOI: 10.1016/j.strusafe.2025.102618

Romain Clerc , Charbel-Pierre El-Soueidy , Franck Schoefs

引用次数: 0

Reliability-based vulnerability assessment of steel truss bridge components 基于可靠度的钢桁架桥梁构件易损性评估

IF 5.7 1区工程技术 Q1 ENGINEERING, CIVIL

Structural Safety

Pub Date : 2025-06-04 DOI: 10.1016/j.strusafe.2025.102623

Santiago López , Brais Barros , Manuel Buitrago , Jose M. Adam , Belen Riveiro

Bridges are among the most vulnerable and expensive assets of transportation networks. The failure of a bridge component can lead to catastrophic consequences for the entire structure. Therefore, vulnerability assessments have gained prominence to ensure their structural safety. However, as bridges age, performing a reliable assessment becomes increasingly challenging. This paper proposed a framework for the component-based vulnerability assessment of steel truss bridges. An index (SoD) that quantifies the State of Demand of each structural element is proposed. The level of vulnerability of all bridge elements is evaluated through a FEM-based approach that considers the uncertainty of the variables affecting the structural behaviour. The proposed framework has been tested in a real steel truss bridge located in Galicia, Spain. The framework finally integrates finite element modelling, uncertainty quantification and propagation, and probabilistic tools into a systematic approach for evaluating the component-level vulnerability of steel truss bridges. The outputs can be used to optimise inspection routines, reduce costs, and support the decision of authorities regarding bridge safety, monitoring, and maintenance. This work breaks new ground in the practical application of new knowledge, as the methodology could be further automated, simplifying engineering efforts and supporting bridge management entities to improve the bridge’s structural safety.

桥梁是交通网络中最脆弱、最昂贵的资产之一。桥梁构件的失效可能导致整个结构的灾难性后果。因此，对其进行易损性评估以确保其结构安全已成为研究重点。然而，随着桥梁的老化，进行可靠的评估变得越来越具有挑战性。提出了一种基于构件的钢桁架桥梁易损性评估框架。提出了一种量化各结构要素需求状态的指标（SoD）。所有桥梁构件的易损性水平通过基于有限元的方法进行评估，该方法考虑了影响结构行为的变量的不确定性。该框架已经在位于西班牙加利西亚的一座真实钢桁架桥上进行了测试。该框架最后将有限元建模、不确定性量化和传播以及概率工具集成为评估钢桁架桥梁构件级脆弱性的系统方法。其结果可用于优化检查程序，降低成本，并支持有关当局对桥梁安全、监测和维护的决策。这项工作在新知识的实际应用方面开辟了新的领域，因为该方法可以进一步自动化，简化工程工作，并支持桥梁管理实体提高桥梁的结构安全性。

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引用次数: 0

Analytical solution of the generalized density evolution equation for stochastic systems: Euler-Bernoulli beam under noisy excitations and nonlinear vibration of Kirchhoff plate 随机系统广义密度演化方程的解析解：噪声激励下的Euler-Bernoulli梁和Kirchhoff板的非线性振动

IF 5.7 1区工程技术 Q1 ENGINEERING, CIVIL

Structural Safety

Pub Date : 2025-06-02 DOI: 10.1016/j.strusafe.2025.102619

Yongfeng Zhou , Jie Li

The Generalized Density Evolution Equation (GDEE) describes the evolution of probability densities driven by physical processes. The numerical solution of the GDEE, implemented through a fully developed computational framework, is referred to as the Probability Density Evolution Method (PDEM). However, the absence of analytical solutions presents challenges for error calibration in numerical methods. In this study, analytical solutions of the GDEE are derived, focusing primarily on stochastic dynamic systems. The forced vibration of an Euler-Bernoulli beam subjected to random excitations is first analyzed, yielding analytical solutions for mid-span displacement response. For lower dimensional scenarios, two cases are examined: random harmonic loading and random step loading, both involving uncertainties in structural parameters. Results reveal that the corresponding displacement responses are non-Gaussian and non-stationary random processes. For higher dimensional scenarios, additional noise excitation is considered. By employing the Stochastic Harmonic Function (SHF) representation, noise excitation is effectively approximated as a superposition of finite random harmonic loads. Analytical derivations demonstrate that the SHF representation gradually converges toward the actual noise as the expansion terms increase. Furthermore, to illustrate the versatility of the developed analytical method, a nonlinear free vibration analysis of a Kirchhoff plate without external excitations is presented, showcasing its applicability to broader structural dynamic problems. These analytical solutions provide valuable benchmarks for further in-depth research into the PDEM, especially for the calibration of numerical methods.

广义密度演化方程（GDEE）描述了由物理过程驱动的概率密度演化。GDEE的数值解，通过一个完全发展的计算框架来实现，被称为概率密度演化法（PDEM）。然而，解析解的缺失给数值方法的误差校准带来了挑战。在本研究中，推导了GDEE的解析解，主要关注随机动力系统。首先分析了随机激励下欧拉-伯努利梁的强迫振动，给出了跨中位移响应的解析解。对于低维情况，研究了两种情况：随机谐波加载和随机阶跃加载，两者都涉及结构参数的不确定性。结果表明，相应的位移响应是非高斯非平稳随机过程。对于高维场景，考虑了额外的噪声激励。通过采用随机谐波函数（SHF）表示，噪声激励有效地近似为有限随机谐波负荷的叠加。解析推导表明，随着展开项的增加，SHF表示逐渐收敛于实际噪声。此外，为了说明所开发的分析方法的通用性，给出了无外部激励的基尔霍夫板的非线性自由振动分析，展示了其对更广泛的结构动力问题的适用性。这些解析解为进一步深入研究PDEM，特别是数值方法的标定提供了有价值的基准。

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引用次数: 0

Failure probability estimate of corroded reinforced concrete structures based on sparse representation of steel weight loss distributions 基于钢筋减重分布稀疏表示的锈蚀钢筋混凝土结构失效概率估计

IF 5.7 1区工程技术 Q1 ENGINEERING, CIVIL

Structural Safety

Pub Date : 2025-06-01 DOI: 10.1016/j.strusafe.2025.102622

Siyi Jia , Mitsuyoshi Akiyama , Dan M. Frangopol , Zhejun Xu

Uncertainties associated with the non-uniform spatial distribution of steel weight loss (SWL) should be considered appropriately when estimating the load-bearing capacity loss of corroded reinforced concrete (RC) structures. Addressing these uncertainties necessitates a probabilistic analysis using high-dimensional SWL data, which can lead to inaccurate condition assessments for corroded RC structures. This paper presents a dimension-reduction approach for SWL distribution based on the K-means singular vector decomposition (K-SVD) algorithm, which enforces a sparse representation of SWL distributions using a combination of non-standard distribution features learned from experimental SWL data. The K-SVD algorithm involves an iterative two-stage supervised learning process. In the dictionary learning stage, K-SVD identifies non-standard distribution features tailored to the localized characteristics of SWL data, based on which the orthogonal matching pursuit (OMP) algorithm is employed in the coding learning stage to derive a sparse representation of SWL distributions. The efficacy of K-SVD is evaluated using 83 experimental samples of SWL distributions. The results reveal that the K-SVD algorithm can derive a sparse representation of SWL distribution while preserving the distribution details of SWL. With just 15 learned non-standard distribution features, K-SVD achieves the same accuracy in reconstructing 168-dimensional SWL distribution data as the baseline Karhunen-Loève OMP (KL-OMP) method, which uses 75 standard features. Subsequently, the sparse representation is used to compute the flexural failure probability of corroded RC beams, for which a Kriging surrogate model is constructed. The results show that the sparse representation significantly enhances the accuracy of the Kriging surrogate model and improves the computational stability of the flexural failure probabilities, which is crucial for accurately assessing the condition of corroded RC structures.

在对钢筋混凝土腐蚀结构的承载能力损失进行评估时，应适当考虑与钢筋重量损失（SWL）空间分布不均匀相关的不确定性。为了解决这些不确定性，需要使用高维SWL数据进行概率分析，这可能导致腐蚀RC结构的状态评估不准确。本文提出了一种基于k均值奇异向量分解（K-SVD）算法的SWL分布降维方法，该方法利用从实验SWL数据中学习到的非标准分布特征的组合来实现SWL分布的稀疏表示。K-SVD算法涉及一个迭代的两阶段监督学习过程。在字典学习阶段，K-SVD识别适合SWL数据局部特征的非标准分布特征，在此基础上，编码学习阶段采用正交匹配追踪（OMP）算法推导SWL分布的稀疏表示。使用83个SWL分布的实验样本对K-SVD的有效性进行了评估。结果表明，K-SVD算法在保留SWL分布细节的同时，可以得到SWL分布的稀疏表示。K-SVD只需要学习到15个非标准分布特征，就可以在重建168维SWL分布数据时达到与使用75个标准特征的基线karhunen - lo -OMP （KL-OMP）方法相同的精度。随后，利用稀疏表示计算腐蚀钢筋混凝土梁的弯曲破坏概率，并建立了Kriging代理模型。结果表明，稀疏表示显著提高了Kriging代理模型的精度，提高了抗弯破坏概率的计算稳定性，这对于准确评估腐蚀RC结构的状态至关重要。

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引用次数: 0

Modeling probabilistic micro-scale wind field for risk forecasts of power transmission systems during tropical cyclones 热带气旋期间输电系统风险预报的概率微尺度风场建模

IF 5.7 1区工程技术 Q1 ENGINEERING, CIVIL

Structural Safety

Pub Date : 2025-05-28 DOI: 10.1016/j.strusafe.2025.102620

Xiubing Huang, Naiyu Wang

Tropical cyclones (TCs) pose significant risks to power transmission systems, causing extensive damage, widespread outages and severe socio-economic impacts. While reliable risk forecasting of these systems during TCs hinges on accurate wind predictions, operational numerical weather prediction (NWP) models struggle to deliver unbiased, high-resolution probabilistic wind-field forecasts necessary for infrastructure risk projections. This study introduces the Probabilistic Micro-Scale Wind-Field model (ProbMicro-WF) designed to enhance real-time hazard modeling for power system risk forecasts during TC evolution. This model improves NWP wind forecast by achieving the following: 1) probabilistic calibration and bias correction for NWP wind forecasts, leveraging historical TC observational data to improve prediction accuracy at high wind speeds; 2) terrain-modified statistical downscaling that translates mesoscale forecasts to micro-scale wind fields, capturing localized wind dynamics critical for tower- and transmission line-specific risk evaluation; and 3) a spatiotemporal stochastic model that preserves wind-field correlation structures, mitigating systemic underestimation of risk variance across geographically dispersed infrastructure during TC evolution. Finally, the ProbMicro-WF model is applied to the power transmission system in Zhejiang Province, China (105,500 km²) during Super Typhoon Lekima in 2019, highlighting its capability to simulate spatially coherent, high-resolution wind fields, enabling robust pre-event mitigation and real-time grid management in TC-prone regions.

热带气旋（tc）对输电系统构成重大风险，造成广泛破坏、大范围停电和严重的社会经济影响。虽然这些系统在tc期间的可靠风险预测取决于准确的风力预测，但操作性数值天气预报（NWP）模型难以提供基础设施风险预测所需的无偏、高分辨率概率风场预测。本文介绍了概率微尺度风场模型（ProbMicro-WF），该模型旨在增强电力系统在TC演变过程中风险预测的实时风险建模。该模型通过实现以下几点改进了NWP风预报：1)NWP风预报的概率校正和偏置校正，利用历史TC观测数据提高了高风速下的预报精度；2)地形修正统计降尺度，将中尺度预报转化为微尺度风场，捕捉局部风动力学，对塔和输电线路特定风险评估至关重要；3)一个时空随机模型，该模型保留了风场相关结构，减轻了在TC演化过程中地理分散的基础设施风险方差的系统性低估。最后，将ProbMicro-WF模型应用于2019年超级台风“利基马”期间中国浙江省（105,500平方公里）的输电系统，突出了其模拟空间相干、高分辨率风场的能力，从而在tc易发地区实现了强大的事件前缓解和实时电网管理。

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引用次数: 0

Optimizing uncertainty estimation in Enhanced Monte Carlo methods 改进蒙特卡罗方法中不确定性估计的优化

IF 5.7 1区工程技术 Q1 ENGINEERING, CIVIL

Structural Safety

Pub Date : 2025-05-22 DOI: 10.1016/j.strusafe.2025.102617

Konstantinos N. Anyfantis

The probability of failure serves as a key metric in a structural reliability analysis, but its accurate estimation remains computationally demanding, particularly for low-probability failure events. The Enhanced Monte Carlo (EMC) method has been developed in order to alleviate from inefficiencies due to the high number of required simulations. Recent advancements integrate Machine Learning techniques with the EMC to further accelerate the estimation process. However, a critical limitation of EMC lies in its fitted confidence interval (CI) estimation, which tends to overestimate uncertainty, leading to unnecessary computational overhead. This study proposes a new prescriptive CI formulation constructed from the method’s hyperparameters, offering a more accurate and computationally efficient approach to uncertainty quantification. The method is general and can be applied to any reliability problem that can be described by a probability curve. The effectiveness of the proposed method is demonstrated through a benchmark reliability problem and a real-world marine structural application. The results indicate significant improvements in efficiency without compromising accuracy, paving the way for enhanced structural reliability assessments.

失效概率是结构可靠性分析中的一个关键指标，但其准确估计仍然是计算上的要求，特别是对于低概率失效事件。增强型蒙特卡罗（EMC）方法是为了解决由于需要大量仿真而导致的效率低下的问题而开发的。最近的进展将机器学习技术与EMC结合起来，进一步加快了估计过程。然而，电磁兼容的一个关键限制在于其拟合置信区间（CI）估计，它往往高估不确定性，导致不必要的计算开销。本研究提出了一种由该方法的超参数构建的新的规定性CI公式，为不确定性量化提供了一种更准确和计算效率更高的方法。该方法具有通用性，适用于任何可用概率曲线描述的可靠性问题。通过一个基准可靠性问题和实际船舶结构应用验证了该方法的有效性。结果表明，在不影响精度的情况下，效率有了显著提高，为增强结构可靠性评估铺平了道路。

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引用次数: 0