Structural Safety最新文献

Accurate variance estimation for subset simulation incorporating intrachain, interchain, and interlevel correlations 包含链内、链间和水平间相关性的子集模拟的准确方差估计

IF 6.3 1区工程技术 Q1 ENGINEERING, CIVIL

Structural Safety

Pub Date : 2026-01-18 DOI: 10.1016/j.strusafe.2026.102690

Qingqing Miao, Ying Min Low

Subset simulation (SS) is widely held as a powerful technique for evaluating small failure probabilities. Variance estimation is integral to assessing the uncertainty of the probability estimate. However, variance estimation for SS is complex as samples are generated by Markov chain Monte Carlo (MCMC), resulting in an intricate web of correlations that fall under three categories: (1) within a chain (intrachain), (2) across separate chains (interchain), and (3) between subset levels (interlevel). To date, hardly any advances have been made on this challenging topic. Most studies using SS adopt the conventional variance estimation method, which considers the intrachain correlation but neglects other correlation types. In a recent study, the authors showed that all three correlation types are important, and developed a method that accounts for the intrachain and interchain correlations. This paper presents a theoretical framework for the interlevel correlations, bridging the final gap and illuminating a long-standing unsolved problem. The method utilizes information available from a single SS run. The equations reveal fascinating insights concerning the mechanism of interlevel correlations, valuable to researchers working on enhancing MCMC algorithms for SS. Among other things, it is mathematically proven that if samples within a level are independent, this level and the next must be independent. The new model is integrated with the prior work to produce a variance estimation method that incorporates all sources of correlations. Case studies with multiple independent SS runs show that the proposed method estimates the variance accurately, providing a vast improvement over the conventional method.

子集模拟（SS）被广泛认为是评估小故障概率的一种强有力的技术。方差估计对于评估概率估计的不确定性是不可或缺的。然而，SS的方差估计是复杂的，因为样本是由马尔可夫链蒙特卡罗（MCMC）生成的，导致了一个复杂的相关性网络，分为三类：(1)链内（链内），(2)跨单独的链（链间），(3)子集水平之间（水平间）。迄今为止，在这个具有挑战性的话题上几乎没有取得任何进展。使用SS的研究大多采用传统的方差估计方法，该方法考虑了链内相关性，而忽略了其他相关类型。在最近的一项研究中，作者表明这三种相关性都很重要，并开发了一种方法来解释链内和链间的相关性。本文提出了一个层次间关联的理论框架，弥合了最后的差距，并阐明了一个长期未解决的问题。该方法利用来自单个SS运行的可用信息。这些方程揭示了关于水平间相关性机制的迷人见解，对致力于增强SS的MCMC算法的研究人员很有价值。除此之外，数学证明，如果一个水平内的样本是独立的，那么这个水平和下一个水平必须是独立的。新模型与先前的工作相结合，产生了包含所有相关性来源的方差估计方法。多个独立SS运行的案例研究表明，所提出的方法可以准确地估计方差，比传统方法有很大的改进。

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引用次数: 0

Encoding of decision trees for life-cycle cost and decision value analysis via optimization 基于优化的生命周期成本和决策价值分析的决策树编码

IF 6.3 1区工程技术 Q1 ENGINEERING, CIVIL

Structural Safety

Pub Date : 2026-01-13 DOI: 10.1016/j.strusafe.2026.102689

Wellison José de Santana Gomes , Sebastian Thöns , André Teófilo Beck

Reliable and cost-effective operation of structural systems over their service life depends on the implementation of Structural Health Monitoring (SHM) and maintenance activities, which influence operational costs, the expected costs of failure and downtime. Decision Value Analysis (DVA) provides a framework to quantify the value of such activities by evaluating their effect on total expected lifecycle costs. While prior studies have focused on isolated decisions or employed heuristic rules to reduce computational demands, an integrated, system-wide, lifetime-based approach is needed to capture interdependencies among components and among decisions, avoiding suboptimal outcomes. This paper addresses the complex problem of optimizing a sequence of SHM and maintenance decisions over the structure’s entire service life, without relying on fixed pre-defined heuristic rules. An approach to encode all decision variables into a single vector of design variables is presented, and an adaptive surrogate modeling strategy is employed to efficiently approximate the total expected cost function, significantly reducing the computational burden. A case study on corrosion in buried steel pipelines is presented, allowing up to nine inspections and the associated repair decisions, resulting in 1533 decision variables and 2¹⁵³³ possible combinations. Results indicate, as expected, that early inspections may be omitted when their cost exceeds the marginal benefit in risk reduction, but also that more frequent inspections can support more effective repair decisions. The proposed approach provides a generalizable and computationally efficient framework for lifecycle DVA, which can be directly applied to more complex problems, and is capable of incorporating multiple inspection and maintenance methods.

结构系统在其使用寿命内的可靠和经济有效的运行取决于结构健康监测（SHM）和维护活动的实施，这影响到运行成本、故障和停机的预期成本。决策价值分析（DVA）提供了一个框架，通过评估这些活动对总预期生命周期成本的影响来量化这些活动的价值。虽然先前的研究主要集中在孤立的决策或采用启发式规则来减少计算需求，但需要一种集成的、全系统的、基于生命周期的方法来捕获组件之间和决策之间的相互依赖性，以避免次优结果。本文解决了在结构的整个使用寿命期间优化一系列SHM和维护决策的复杂问题，而不依赖于固定的预定义启发式规则。提出了一种将所有决策变量编码为单个设计变量向量的方法，并采用自适应代理建模策略有效地逼近总期望成本函数，显著减少了计算量。提出了一个地埋钢管道腐蚀的案例研究，允许多达9次检查和相关的维修决策，产生1533个决策变量和21533个可能的组合。结果表明，正如预期的那样，当早期检查的成本超过风险降低的边际效益时，可以忽略早期检查，但更频繁的检查可以支持更有效的维修决策。该方法为生命周期DVA提供了一个通用的、计算效率高的框架，可以直接应用于更复杂的问题，并且能够结合多种检查和维护方法。

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引用次数: 0

On the quantification of robustness and its thresholds 稳健性的量化及其阈值

IF 6.3 1区工程技术 Q1 ENGINEERING, CIVIL

Structural Safety

Pub Date : 2026-01-12 DOI: 10.1016/j.strusafe.2026.102688

Alex Sixie Cao , André T. Beck

Structural systems need to be safe enough against foreseeable loads, but they also need to be robust enough against unforeseeable or abnormal loading. In this paper, a novel entropy-based robustness index for arbitrary perturbations is derived for coherent path-dependent systems, which is consistent with information-theoretic and thermodynamic principles. Using a reliability-based robustness index and the entropy-based robustness index, quantitative robustness thresholds are derived that enable the explicit classification of low, medium, and high robustness based on the sensitivity of the system to arbitrary perturbations. Furthermore, relations between the entropy- and reliability- and risk-based robustness indices are explored, where thresholds for the risk-based robustness indices are provided based on the novel entropy-based robustness index. The use of the various robustness indices and the thresholds are exemplified in three case studies, involving a redundant system subjected to various degrees of damage, damage propagation in frame structures, and a network. For the first time, quantitative thresholds for the robustness of coherent path-dependent systems are provided, which can be applied to structures, networks, and more. This paves the way for providing quantitative guidance on acceptable degrees of robustness in such systems, which may lead to more economic and rational systems with an appropriate degree of robustness.

结构系统需要在可预见的荷载下足够安全，但它们也需要在不可预见或异常荷载下足够坚固。本文根据信息理论和热力学原理，导出了一种新的基于熵的相干路径依赖系统的任意扰动鲁棒性指标。利用基于可靠性的鲁棒性指数和基于熵的鲁棒性指数，导出了定量的鲁棒性阈值，可以根据系统对任意扰动的敏感性对低、中、高鲁棒性进行明确分类。此外，探讨了基于熵、可靠性和风险的鲁棒性指标之间的关系，其中基于新的基于熵的鲁棒性指标提供了基于风险的鲁棒性指标的阈值。在三个案例研究中举例说明了各种鲁棒性指标和阈值的使用，包括遭受不同程度损伤的冗余系统、框架结构中的损伤传播和网络。本文首次提供了相干路径依赖系统鲁棒性的定量阈值，可应用于结构、网络等领域。这为在这些系统中提供可接受的稳健性程度的定量指导铺平了道路，这可能导致具有适当稳健性程度的更经济和合理的系统。

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引用次数: 0

A critical review and analysis of the uncertainties involved in fatigue damage assessment and their impact on decision-making 对疲劳损伤评估中的不确定性及其对决策的影响进行了评述和分析

IF 6.3 1区工程技术 Q1 ENGINEERING, CIVIL

Structural Safety

Pub Date : 2026-01-03 DOI: 10.1016/j.strusafe.2026.102687

Somayeh Shojaeikhah , Baran Yeter , Mohamed Soliman , Yordan Garbatov

Fatigue damage is a major driver of interventions in marine and offshore structures. The fluctuating loads applied to these structures can lead to crack initiation and propagation. Accordingly, effective management activities are needed to ensure the safety and reliability of these structures. Predicting the service life under fatigue damage is a crucial step in the effective management of these structures. However, this process is challenged by the presence of significant uncertainties introduced by the natural randomness in sea loading and mechanical behavior. To date, the literature does not present inclusive guidance on quantifying and accounting for these uncertainties in the damage prediction process. To address this need, this paper critically reviews the uncertainties associated with the fatigue damage assessment in ships and offshore structures, including offshore wind turbines, with a specific focus on the reliability and risk as the probabilistic performance indicators. The review covers the S-N approaches and the fracture mechanics-based damage tolerance design and assessment techniques, and discusses the potential discrepancies in their treatment of uncertainties. By systematically evaluating these aspects, this review provides a much-needed insight into existing knowledge gaps and suggests directions for future research on fatigue damage assessment protocols in the marine and offshore engineering domains.

疲劳损伤是海洋和近海结构物干预的主要驱动因素。施加在这些结构上的波动载荷会导致裂纹的萌生和扩展。因此，需要进行有效的管理活动，以确保这些结构的安全和可靠。预测疲劳损伤下的使用寿命是有效管理这些结构的关键步骤。然而，这一过程受到海洋载荷和力学行为的自然随机性所带来的重大不确定性的挑战。到目前为止，文献并没有在损伤预测过程中对这些不确定性进行量化和核算的包容性指导。为了满足这一需求，本文批判性地回顾了与船舶和海上结构（包括海上风力涡轮机）疲劳损伤评估相关的不确定性，并特别关注可靠性和风险作为概率性能指标。本文综述了S-N方法和基于断裂力学的损伤容限设计和评估技术，并讨论了它们在处理不确定性方面的潜在差异。通过系统地评估这些方面，本综述提供了对现有知识空白的急需的见解，并为海洋和海洋工程领域疲劳损伤评估协议的未来研究提出了方向。

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引用次数: 0

Prediction of the flexural behavior of corroded prestressed concrete girders: a probabilistic multi-level approach 腐蚀预应力混凝土梁抗弯性能的预测：一种概率多级方法

IF 6.3 1区工程技术 Q1 ENGINEERING, CIVIL

Structural Safety

Pub Date : 2025-12-30 DOI: 10.1016/j.strusafe.2025.102685

Seungjun Lee , Chi-Ho Jeon , Jaebeom Lee , Young-Joo Lee

This paper introduces a probabilistic multi-level framework for predicting the flexural behavior of corroded prestressed concrete (PSC) girders. The proposed framework employs a hierarchical modeling strategy that progresses from the wire to the girder level and integrates detailed finite element (FE) analysis, surrogate modeling, and Monte Carlo simulations. This computationally efficient framework addresses the challenge of accurately predicting flexural behavior by systematically incorporating the effects of the geometric complexity of the corroded strands and other inherent modeling uncertainties into its probabilistic predictions. The surrogate model constructed from the FE results enables efficient predictions by accounting for material and geometric uncertainties across multiple structural levels. Experimental validation was performed using ten PSC girder specimens, comprising both single- and multi-strand configurations, subjected to controlled corrosion and flexural loading tests. The predicted load–displacement responses, including the 50 %, 95 %, and 99 % prediction ranges, exhibited good agreement with the experimental results, successfully capturing key indicators of structural performance, such as loads and deflections at yield and ultimate. In addition, a global sensitivity analysis identified the dominant sources of uncertainty influencing the variability in the probabilistic predictions. These findings confirm the ability of the proposed framework to accurately model corrosion-induced degradation and reliably quantify the associated uncertainties.

本文介绍了一种预测锈蚀预应力混凝土（PSC）梁抗弯性能的概率多级框架。所提出的框架采用分层建模策略，从钢丝到梁级发展，并集成了详细的有限元（FE）分析，代理建模和蒙特卡罗模拟。这个计算效率高的框架通过系统地将腐蚀链的几何复杂性和其他固有的建模不确定性的影响纳入其概率预测，解决了准确预测弯曲行为的挑战。根据有限元结果构建的代理模型通过考虑跨多个结构水平的材料和几何不确定性，实现了有效的预测。实验验证是使用10个PSC梁试件进行的，包括单股和多股配置，并进行了控制腐蚀和弯曲加载试验。预测的荷载-位移响应，包括50%、95%和99%的预测范围，与实验结果吻合良好，成功捕获了结构性能的关键指标，如屈服和极限荷载和挠度。此外，一项全球敏感性分析确定了影响概率预测变异性的主要不确定性来源。这些发现证实了所提出的框架能够准确地模拟腐蚀引起的退化，并可靠地量化相关的不确定性。

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引用次数: 0

Sequential active learning for estimating small failure probabilities in high-dimensional problems: Application to nonlinear vessel responses 在高维问题中估计小失效概率的序贯主动学习：在非线性容器响应中的应用

IF 6.3 1区工程技术 Q1 ENGINEERING, CIVIL

Structural Safety

Pub Date : 2025-12-29 DOI: 10.1016/j.strusafe.2025.102686

Tomoki Takami , Masaru Kitahara

A new method for high-dimensional structural reliability analysis is proposed, with particular attention to estimating small failure probabilities. The proposed method is built upon an active learning framework, in which an active subspace for supervised dimensionality reduction and a surrogate model for bypassing the performance function are simultaneously updated. Heteroscedastic Gaussian process (hGP) modeling is employed for this purpose. To effectively address rare event problems, the method further incorporates a sequential sampling strategy based on the subset simulation. The resulting Sequential Active Learning with Active Subspace (SALAS) method is first demonstrated using the Sobol function to illustrate its accuracy and computational efficiency. Following this, its application is extended to specific high-dimensional engineering problems involving nonlinear vessel responses in waves. Two subject vessel responses are studied: vertical bending moment and roll motion of a vessel. A nonlinear strip theory and a two-degree-of-freedom roll motion model are used to analyze these responses, respectively. Comprehensive comparisons with crude Monte Carlo simulation, first order reliability method, and the adaptive active subspace-based heteroscedastic Gaussian process (AaS-hGP) method demonstrates the efficiency and accuracy of the proposed SALAS method, even in estimating rare event probabilities in high-dimensional stochastic spaces.

提出了一种高维结构可靠度分析的新方法，特别关注小失效概率的估计。该方法建立在一个主动学习框架之上，其中一个用于监督降维的主动子空间和一个用于绕过性能函数的代理模型同时更新。为此采用了异方差高斯过程（hGP）建模。为了有效地解决罕见事件问题，该方法进一步引入了基于子集模拟的顺序采样策略。本文首先用Sobol函数证明了基于主动子空间的顺序主动学习（SALAS）方法的准确性和计算效率。随后，将其应用扩展到涉及船舶在波浪中的非线性响应的特定高维工程问题。研究了两种被试容器的响应：容器的垂直弯矩和横摇运动。采用非线性条形理论和二自由度横摇运动模型分别对这些响应进行了分析。通过与原始蒙特卡罗模拟、一阶可靠性方法和自适应主动子空间异方差高斯过程（AaS-hGP）方法的综合比较，证明了SALAS方法的有效性和准确性，即使在高维随机空间中估计罕见事件概率也是如此。

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引用次数: 0

Reliability sensitivity with response gradient 响应梯度下的可靠性灵敏度

IF 6.3 1区工程技术 Q1 ENGINEERING, CIVIL

Structural Safety

Pub Date : 2025-12-16 DOI: 10.1016/j.strusafe.2025.102683

Siu-Kui Au , Zi-Jun Cao

Engineering risk is concerned with the likelihood of failure and the scenarios when it occurs. The sensitivity of failure probability to change in system parameters is relevant to risk-informed decision making. Computing sensitivity is at least one level more difficult than the probability itself, which is already challenged by a large number of input random variables, rare events and implicit nonlinear ‘black-box’ response. Finite difference with Monte Carlo probability estimates is spurious, requiring the number of samples to grow with the reciprocal of step size to suppress estimation variance. Many existing works gain efficiency by exploiting a specific class of input variables, sensitivity parameters, or response in its exact or surrogate form. For general systems, this work presents a theory and Monte Carlo strategy for computing sensitivity using response values and gradients with respect to sensitivity parameters. It is shown that the sensitivity at a given response threshold can be expressed via the expectation of response gradient conditional on the threshold. Determining the expectation requires conditioning on the threshold that is a zero-probability event, but it can be resolved by kernel smoothing. The proposed method offers sensitivity estimates for all response thresholds generated in a Monte Carlo run. It is investigated in a number of examples featuring sensitivity parameters of different nature. As response gradient becomes increasingly available, it is hoped that this work can provide the basis for embedding sensitivity calculations with reliability in the same Monte Carlo run.

工程风险是指发生故障的可能性和故障发生时的情景。失效概率对系统参数变化的敏感性与风险知情决策有关。计算灵敏度至少比概率本身困难一个级别，这已经受到大量输入随机变量、罕见事件和隐式非线性“黑箱”响应的挑战。与蒙特卡罗概率估计的有限差分是虚假的，需要样本数量随着步长的倒数而增长以抑制估计方差。许多现有的工作通过利用特定类别的输入变量、灵敏度参数或准确或替代形式的响应来提高效率。对于一般系统，本文提出了一种利用响应值和相对于灵敏度参数的梯度计算灵敏度的理论和蒙特卡罗策略。结果表明，给定响应阈值处的灵敏度可以用以阈值为条件的响应梯度期望来表示。确定期望需要对阈值进行调节，该阈值是零概率事件，但可以通过核平滑来解决。所提出的方法为蒙特卡罗运行中产生的所有响应阈值提供了灵敏度估计。在若干具有不同性质的灵敏度参数的例子中进行了研究。随着响应梯度的日益普及，希望本工作能为在同一蒙特卡罗运行中可靠地嵌入灵敏度计算提供依据。

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引用次数: 0

A transfer learning approach to predict corrosion-induced concrete cracking based on steel weight loss distributions 基于钢筋失重分布预测混凝土腐蚀开裂的迁移学习方法

IF 6.3 1区工程技术 Q1 ENGINEERING, CIVIL

Structural Safety

Pub Date : 2025-12-15 DOI: 10.1016/j.strusafe.2025.102684

Siyi Jia , Mitsuyoshi Akiyama , Dan M. Frangopol

Predictive models of corrosion-induced concrete cracking based on steel weight loss (SWL) distributions are crucial for assessing structural degradation and enabling proactive maintenance of reinforced concrete (RC) structures. Data-driven models offer an efficient alternative to predict corrosion-induced concrete cracking, which facilitate real-time monitoring of corrosion damage while capturing cracking mechanisms amid noisy data. However, developing such data-driven predictive models is constrained by limited experimental samples and domain drift across varying corrosion rates. This paper proposes a transfer learning framework to address these challenges. The proposed method first denoises SWL data and extracts principal components via the Karhunen-Loève (KL) transformation. Transfer component analysis (TCA) then aligns joint probability distributions of SWL and cracking states across different corrosion rates in a learned feature space, where K-nearest neighbors (KNN) serves as the base classifier. The proposed KL-TCA model is validated using an existing experimental dataset and achieves a prediction accuracy of 87.32%, which notably outperforms the baseline methods based on TCA and direct KNN. Then, an ensemble learning model is constructed to integrate KL-TCA base learners with different configurations, which achieves a prediction accuracy of 93.89% on the validation set, surpassing that of any individual KL-TCA base learner. Additionally, a damage indicator, defined as the percentage of cracked sections, is computed using the KL-TCA method. Considering the uncertainties in SWL distributions, a Monte Carlo simulation is performed to establish the relationship between the damage indicator and the average SWL. This relationship allows for a preliminary screening of corrosion severity using readily available cracking observations, supporting timely corrosion assessments and maintenance decisions.

基于钢重量损失（SWL）分布的腐蚀诱发混凝土开裂预测模型对于评估结构退化和实现钢筋混凝土（RC）结构的主动维护至关重要。数据驱动模型为预测腐蚀引起的混凝土开裂提供了一种有效的替代方案，它有助于实时监测腐蚀损伤，同时在嘈杂的数据中捕捉开裂机制。然而，开发这种数据驱动的预测模型受到有限的实验样品和不同腐蚀速率下的区域漂移的限制。本文提出了一个迁移学习框架来解决这些挑战。该方法首先对SWL数据进行去噪，并通过karhunen - lo变换提取主成分。然后，传递分量分析（TCA）在学习到的特征空间中对不同腐蚀速率下SWL和开裂状态的联合概率分布进行比对，其中k近邻（KNN）作为基本分类器。利用已有的实验数据验证了KL-TCA模型的预测精度，达到87.32%，明显优于基于TCA和直接KNN的基线方法。然后，构建集成学习模型，对不同配置的KL-TCA基学习器进行集成，在验证集上的预测准确率达到93.89%，超过了任何单个KL-TCA基学习器的预测准确率。此外，使用KL-TCA方法计算了一个损伤指标，定义为裂纹截面的百分比。考虑SWL分布的不确定性，通过蒙特卡罗模拟建立了损伤指标与平均SWL之间的关系。这种关系允许使用现成的开裂观察初步筛选腐蚀严重程度，支持及时的腐蚀评估和维护决策。

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引用次数: 0

Fusing experimental and FEM-based knowledge: a transfer learning model for inferring steel corrosion in reinforced concrete structures 融合实验与有限元知识：钢筋混凝土结构中钢筋腐蚀的迁移学习模型

IF 6.3 1区工程技术 Q1 ENGINEERING, CIVIL

Structural Safety

Pub Date : 2025-12-01 DOI: 10.1016/j.strusafe.2025.102672

Siyi Jia , Mitsuyoshi Akiyama , Dan M. Frangopol

Corrosion-induced crack width (CCW) can be readily obtained through visual inspection of reinforced concrete structures and serves as a proxy indicator of steel corrosion based on its relationship with steel weight loss (SWL). Although accelerated corrosion tests and FEM simulations provide a cost-effective data source to derive the relationship between CCW and SWL, the calibrated models often fail to generalize under natural corrosion conditions. This study proposes a transfer learning-based model that fuses knowledge from accelerated corrosion tests and FEM simulations to predict CCW from SWL distribution under natural corrosion conditions. This model is then used as the forward model in a Bayesian inference scheme to estimate SWL distributions. Specifically, the proposed approach first uses an unsupervised transfer learning model that combines the geodesic flow kernel (GFK) and transfer component analysis (TCA) to align the marginal distributions of SWL across different corrosion conditions. This unsupervised model is extended to a semi-supervised approach by introducing FEM-generated pseudo labels for CCW under natural corrosion conditions, which embeds the statistical dependence between SWL and CCW into the TCA projection in a structure-aware manner. Numerical results demonstrate that the proposed transfer learning models effectively transfer the SWL-CCW mapping learned from a combination of accelerated corrosion tests using the galvanostatic method and FEM simulations to natural corrosion conditions represented by the artificial chloride environment (ACE) method. The unsupervised GFK-TCA method improves CCW prediction accuracy under ACE conditions by 115% compared to the non-transfer learning baseline. Furthermore, the semi-supervised GFK-TCA method achieves an additional 18.0% improvement in accuracy over the unsupervised GFK-TCA method. Based on the transfer learning model, Bayesian inference yields a range estimate of SWL distribution that covers nearly 70.0% of the observations in the RC specimens corroded by ACE method.

腐蚀裂缝宽度（CCW）可以通过钢筋混凝土结构的目测得到，并根据其与钢的失重（SWL）的关系作为钢腐蚀的代理指标。虽然加速腐蚀试验和有限元模拟提供了一种具有成本效益的数据源，以得出CCW和SWL之间的关系，但校准的模型往往不能推广到自然腐蚀条件下。本研究提出了一种基于迁移学习的模型，该模型融合了加速腐蚀试验和FEM模拟的知识，以预测自然腐蚀条件下SWL分布的CCW。然后将该模型用作贝叶斯推理方案中的前向模型来估计SWL分布。具体来说，所提出的方法首先使用无监督迁移学习模型，该模型结合了测地线流核（GFK）和传递成分分析（TCA），以对齐不同腐蚀条件下SWL的边际分布。通过引入fem生成的自然腐蚀条件下CCW的伪标签，该无监督模型扩展为半监督方法，该方法以结构感知的方式将SWL和CCW之间的统计依赖性嵌入到TCA投影中。数值结果表明，所提出的迁移学习模型能有效地将恒流法加速腐蚀试验和有限元模拟相结合获得的SWL-CCW映射转换到以人工氯化物环境（ACE）方法为代表的自然腐蚀条件下。与非迁移学习基线相比，无监督GFK-TCA方法在ACE条件下将CCW预测精度提高了115%。此外，与无监督GFK-TCA方法相比，半监督GFK-TCA方法的准确率提高了18.0%。基于迁移学习模型，贝叶斯推理得到的SWL分布范围估计覆盖了ACE腐蚀RC试件中近70.0%的观测值。

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引用次数: 0

Adaptive machine learning-driven multi-fidelity stratified sampling for failure analysis of nonlinear stochastic systems 非线性随机系统失效分析的自适应机器学习驱动多保真分层抽样

IF 6.3 1区工程技术 Q1 ENGINEERING, CIVIL

Structural Safety

Pub Date : 2025-11-29 DOI: 10.1016/j.strusafe.2025.102673

Liuyun Xu, Seymour M.J. Spence

Existing variance reduction techniques used in stochastic simulations for rare event analysis still require a substantial number of model evaluations to estimate small failure probabilities. In the context of complex, nonlinear finite element modeling environments, this can become computationally challenging, particularly for systems subjected to stochastic excitation. To address this challenge, a multi-fidelity stratified sampling scheme with adaptive machine learning metamodels is introduced for efficiently propagating uncertainties and estimating small failure probabilities. In this approach, a high-fidelity dataset generated through stratified sampling is used to train a deep learning-based metamodel, which then serves as a cost-effective and highly correlated low-fidelity model. An adaptive training scheme is proposed to balance the trade-off between approximation quality and computational demand associated with the development of the low-fidelity model. By integrating the low-fidelity outputs with additional high-fidelity results, an unbiased estimate of the strata-wise failure probabilities is obtained using a multi-fidelity Monte Carlo framework. The overall probability of failure is then computed using the total probability theorem. Application to a full-scale high-rise steel building subjected to stochastic wind excitation demonstrates that the proposed scheme can accurately estimate exceedance probability curves for nonlinear responses of interest while achieving significant computational savings compared to single-fidelity variance reduction approaches.

现有的用于罕见事件分析的随机模拟的方差减少技术仍然需要大量的模型评估来估计小的失效概率。在复杂、非线性有限元建模环境的背景下，这可能成为计算上的挑战，特别是对于受随机激励的系统。为了解决这一挑战，引入了一种具有自适应机器学习元模型的多保真度分层抽样方案，以有效地传播不确定性并估计小故障概率。在这种方法中，通过分层抽样生成的高保真数据集用于训练基于深度学习的元模型，然后作为成本效益高且高度相关的低保真模型。提出了一种自适应训练方案，以平衡低保真度模型的逼近质量和计算需求之间的权衡。通过将低保真输出与额外的高保真结果相结合，使用多保真蒙特卡罗框架获得了分层失效概率的无偏估计。然后使用总概率定理计算故障的总概率。对随机风激励下的全尺寸高层钢结构建筑的应用表明，与单保真度方差缩减方法相比，该方法可以准确地估计非线性响应的超越概率曲线，同时大大节省了计算量。

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引用次数: 0