Structural Safety最新文献_第8页

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

Improved variance estimation for subset simulation by accounting for the correlation between Markov chains 基于马尔可夫链相关性的子集模拟方差估计改进

IF 5.7 1区工程技术 Q1 ENGINEERING, CIVIL

Structural Safety

Pub Date : 2025-05-01 DOI: 10.1016/j.strusafe.2025.102606

Qingqing Miao, Ying Min Low

Subset simulation (SS) is a popular structural reliability analysis method, especially for problems characterized by low failure probabilities and high-dimensional complexities. Unlike most variance reduction methods, SS obviates the need for prior domain information, making it versatile across diverse applications. Markov chain Monte Carlo (MCMC) algorithms are required for sampling from an unknown conditional distribution, resulting in correlated samples. There is plenty of literature on SS in several aspects, such as the improvement of MCMC algorithms, and combining SS with other techniques. However, one aspect that appears to be neglected concerns the variance estimation crucial for assessing the accuracy of the probability estimate. To date, most studies on SS still rely on the conventional variance estimation method, which only considers the correlation within a Markov chain (intrachain) but neglects the correlation across separate chains (interchain) and different subset levels (interlevel). This study aims to improve understanding of this topic and develop a more accurate variance estimation method for SS. An investigation based on multiple independent SS runs reveal that the intrachain, interchain and interlevel correlations are all important. Subsequently, a new variance estimation method is proposed to account for the intrachain and interchain correlations. The proposed method is easy to apply, has small sampling uncertainty and only utilizes samples from a single SS run. Results indicate a notable improvement in accuracy compared to the conventional method.

子集模拟（SS）是一种流行的结构可靠性分析方法，尤其适用于低失效概率和高维复杂性的问题。与大多数方差减少方法不同，SS消除了对先验域信息的需要，使其在不同的应用中通用。马尔可夫链蒙特卡罗（MCMC）算法需要从一个未知的条件分布中采样，从而得到相关的样本。在MCMC算法的改进、SS与其他技术的结合等几个方面都有大量关于SS的文献。然而，有一个方面似乎被忽略了，那就是对评估概率估计的准确性至关重要的方差估计。迄今为止，大多数关于SS的研究仍然依赖于传统的方差估计方法，该方法只考虑马尔可夫链内（链内）的相关性，而忽略了不同链间（链间）和不同子集水平（水平间）的相关性。本研究旨在提高对这一主题的理解，并开发更准确的SS方差估计方法。基于多个独立SS运行的调查表明，链内、链间和水平间的相关性都很重要。随后，提出了一种新的方差估计方法来考虑链内和链间的相关性。该方法易于应用，采样不确定度小，仅利用单次SS运行的样本。结果表明，与传统方法相比，该方法的准确性有显著提高。

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引用次数: 0

Optimal redundancy allocation and quality control in structural systems 结构系统的最优冗余分配与质量控制

IF 5.7 1区工程技术 Q1 ENGINEERING, CIVIL

Structural Safety

Pub Date : 2025-04-30 DOI: 10.1016/j.strusafe.2025.102603

André T. Beck, Lucas A. Rodrigues da Silva, Luis G.L. Costa, Jochen Köhler

Reliability-Based and Risk-Based design optimization are popular research topics nowadays. Yet, not many studies have addressed the progressive collapse, the optimal robustness nor the optimal redundancy of structural systems. By way of fundamental examples, it is shown herein that redundancy is of little benefit, unless the structural system is exposed to external ‘shocks’. These ‘shocks’ are abnormal loading events; unanticipated failure modes; gross errors in design, construction or operation; operational abuse; and other factors that have historically contributed to observed structural collapses. Shocks may lead to structural damage or complete loss of structural members. The effect of such shocks on system reliability is generically represented by a member damage probability. This is a hazard-imposed damage probability, which is shown to be the key factor justifying the additional spending on structural redundancy. In structural reliability theory, it is understood that quality control should handle gross errors and their impacts; yet, it is shown herein that optimal redundancy is related to the frequency of inspections. The study reveals an intricate interaction between optimal redundancy and optimal quality control by way of inspections, challenging the separation between structural reliability theory and quality control in safety management.

基于可靠性和基于风险的设计优化是当今研究的热点。然而，针对结构体系的渐进崩溃、最优鲁棒性和最优冗余性的研究并不多见。通过基本的例子，本文表明，除非结构系统暴露于外部“冲击”，否则冗余几乎没有好处。这些“冲击”是异常加载事件；意外失效模式；设计、施工、操作出现重大失误的；操作滥用;历史上其他因素导致了观察到的结构崩塌。冲击可能导致结构损坏或结构构件完全丧失。这种冲击对系统可靠性的影响一般用构件损坏概率来表示。这是一个危险造成的损坏概率，这是证明在结构冗余上额外支出的关键因素。在结构可靠性理论中，质量控制应处理大误差及其影响；然而，本文表明，最优冗余与检查频率有关。研究揭示了最优冗余和最优质量控制之间复杂的相互作用，对安全管理中结构可靠性理论与质量控制的分离提出了挑战。

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引用次数: 0

Structural reliability analysis using gradient-enhanced physics-informed neural network and probability density evolution method 基于梯度增强物理信息神经网络和概率密度演化方法的结构可靠性分析

IF 5.7 1区工程技术 Q1 ENGINEERING, CIVIL

Structural Safety

Pub Date : 2025-04-30 DOI: 10.1016/j.strusafe.2025.102604

Zidong Xu, Hao Wang, Kaiyong Zhao, Han Zhang

In past decade, probability density evolution method (PDEM) has become one of the most popular approaches to conduct overall structural reliability analysis (SRA). The main procedure of the PDEM-based SRA lies in solving the generalized probability density evolution equation (GDEE) related to virtual stochastic process (VSP). Common methods for GDEE solving are highly sensitive to the choice of solving parameters, which may affect the accuracy, efficiency and stability of the solution. Recently, physics-informed neural network (PINN) and its extended form have successfully utilized to solve differential equations in different fields. With this in view, the gradient-enhanced PINN (gPINN) are utilized to solve the GDEE of the VSP for SRA, which leads to an improved approach, termed as evolutionary probability density (EPD)-gPINN model. Specifically, the normalized GDEE and the additional gradient residual equations are derived as the physical loss. Meanwhile, to offer sufficient supervised training data, an easy-to-operate data augmentation procedure is established. Numerical examples are posed for validating the validity of the proposed framework. Parametric analysis is conducted to investigate the influence of the network parameters to the predictive performance. Results indicate that using proper weight of the gradient loss, the proposed framework can efficiently conduct the SRA, whose predictive performance outperforms PINN.

近十年来，概率密度演化法（PDEM）已成为进行结构整体可靠度分析（SRA）的最常用方法之一。基于pdem的SRA的主要过程是求解与虚拟随机过程相关的广义概率密度演化方程（GDEE）。常用的GDEE求解方法对求解参数的选择高度敏感，可能影响求解的精度、效率和稳定性。近年来，物理信息神经网络（PINN）及其扩展形式已成功地用于求解不同领域的微分方程。鉴于此，利用梯度增强的PINN （gPINN）来求解SRA的VSP的GDEE，从而得到一种改进的进化概率密度(EPD)-gPINN模型。具体来说，将归一化GDEE方程和附加梯度残差方程导出为物理损失。同时，为了提供足够的有监督的训练数据，建立了易于操作的数据增强程序。通过数值算例验证了所提框架的有效性。通过参数分析研究了网络参数对预测性能的影响。结果表明，采用适当的梯度损失权重，该框架可以有效地进行SRA，其预测性能优于PINN。

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引用次数: 0

Modeling the spatial corrosion of strand and FE-based Monte Carlo simulation for structural performance assessment of deteriorated PC beams 钢绞线空间腐蚀建模及基于fe的预应力混凝土劣化梁结构性能评估蒙特卡罗模拟

IF 5.7 1区工程技术 Q1 ENGINEERING, CIVIL

Structural Safety

Pub Date : 2025-04-30 DOI: 10.1016/j.strusafe.2025.102605

Taotao Wu , Mitsuyoshi Akiyama , De-Cheng Feng , Sopokhem Lim , Dan M. Frangopol , Zhejun Xu

Structural performance assessments of corroded prestressed concrete (PC) beams using numerical models that account for spatial corrosion distribution and are validated against experimental results remain limited compared to those of reinforced concrete (RC) beams. This study proposes a probabilistic analysis method to evaluate the structural performance of corroded PC beams, incorporating the spatial corrosion distribution of strands and wires. The method is further applied to compare the structural performance of corroded PC and RC beams. Three finite element (FE) models are developed and compared for their accuracy in predicting the structural behavior of PC beams: (a) using the mean steel weight loss of the strand, (b) incorporating the spatial corrosion distribution of the strand, and (c) considering the spatial corrosion distribution of the six outer wires. The model incorporating the spatial corrosion distribution of the six outer wires achieves the highest accuracy, as it effectively simulates the first wire breakage that governs the flexural load-bearing and deflection ductility capacities of PC beams. The probabilistic distribution parameters representing the spatial variability of corrosion are derived from experimental data. Using this distribution, Monte Carlo simulation-based spatial corrosion samples are integrated into the most accurate FE model to obtain the probability density functions (PDFs) of corroded PC beams. The results indicate that PC beams with the same total steel weight loss can exhibit significantly different flexural load-bearing and deflection ductility capacities due to spatial variability, underscoring the importance of a probabilistic assessment. Furthermore, the PDFs of PC members are shifted to the left compared to those of RC members with the same degree of corrosion. Notably, early wire breakage results in lower mean values and standard deviations for the deflection ductility of corroded PC beams compared to RC beams.

与钢筋混凝土（RC）梁相比，使用考虑空间腐蚀分布的数值模型对腐蚀预应力混凝土（PC）梁的结构性能进行评估并根据实验结果进行验证仍然有限。本研究提出了一种概率分析方法来评估腐蚀PC梁的结构性能，该方法考虑了钢绞线的空间腐蚀分布。将该方法进一步应用于腐蚀PC和RC梁的结构性能比较。开发了三种有限元（FE）模型，并比较了它们预测PC梁结构行为的准确性：(a)使用钢绞线的平均钢重量损失，(b)结合钢绞线的空间腐蚀分布，以及(c)考虑六条外钢丝的空间腐蚀分布。该模型结合了六根外部钢丝的空间腐蚀分布，达到了最高的精度，因为它有效地模拟了控制PC梁的弯曲承载和挠曲延性能力的第一次钢丝断裂。根据实验数据推导出表征腐蚀空间变异性的概率分布参数。利用这一分布，将基于蒙特卡罗模拟的空间腐蚀样本整合到最精确的有限元模型中，得到PC梁腐蚀的概率密度函数（pdf）。结果表明，相同钢总重量损失的PC梁由于空间变异性而表现出明显不同的弯曲承载和挠曲延性能力，强调了概率评估的重要性。此外，在腐蚀程度相同的情况下，混凝土构件的pdf比混凝土构件的pdf更左移。值得注意的是，与钢筋混凝土梁相比，早期断线导致腐蚀PC梁挠度延性的平均值和标准差更低。

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引用次数: 0