Structural Safety最新文献_第5页

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

Impact of structural information fidelity on reduced-order model development for regional risk assessment 结构信息保真度对区域风险评估降阶模型开发的影响

IF 5.7 1区工程技术 Q1 ENGINEERING, CIVIL

Structural Safety

Pub Date : 2025-04-27 DOI: 10.1016/j.strusafe.2025.102602

Sang-ri Yi , Alexandros A. Taflanidis , Parisa Toofani Movaghar , Carmine Galasso

Reduced-order models (ROMs) are widely used for seismic vulnerability estimation, both for approximating the response of specific structures as well as for modeling a portfolio of buildings within regional risk assessment applications. There are different ROM modeling approaches with different degrees of complexity, and the modeling choice, as well as the accuracy of the estimated response, naturally depends on the fidelity of the available information for developing the ROM. For regional risk assessment applications, the ROM implementation is commonly established using an automated workflow that leverages generic information about basic building characteristics to derive the mechanical parameters of the simulation models. This paper investigates the influence of information fidelity on the downstream risk analysis when utilizing ROMs in such a context, focusing specifically on moment-resisting frames (MRFs). Initially, a framework for establishing multi-degree-of-freedom (MDoF) ROMs with hysteretic nonlinear behavior is presented, establishing rulesets to derive nominal values of ROM parameters from commonly available building descriptions such as number of stories, story height, design specifications, or structural system type and its material(s) (e.g., reinforced concrete or steel). The rulesets place emphasis on explicitly modeling differences across stories instead of relying on simplified approximations that utilize equivalence to inelastic single-degree-of-freedom systems. The fidelity of the information for developing the ROM is quantified by assigning probability distributions over the aforementioned nominal values, with different degrees of uncertainty across the different parameters. Parametric and global sensitivity analyses are then performed to investigate the importance of this information fidelity. A computational workflow leveraging resampling principles is discussed to promote computational efficiency in these analyses. The results provide unique insights into the parameters of critical importance for establishing ROMs for different MDoF archetypes and offer guidance for the type of data that needs to be collected with higher fidelity (degree of confidence) when deploying ROMs in regional scale seismic risk assessment, in order to improve the prediction accuracy.

降阶模型（ROMs）广泛用于地震易损性估计，既可用于近似特定结构的响应，也可用于在区域风险评估应用中对建筑物组合进行建模。不同的ROM建模方法具有不同程度的复杂性，建模选择以及估计响应的准确性自然取决于开发ROM的可用信息的保真度。对于区域风险评估应用，ROM实现通常使用自动化工作流来建立，该工作流利用有关基本建筑特征的通用信息来推导仿真模型的力学参数。本文研究了当在这种情况下使用rom时，信息保真度对下游风险分析的影响，特别关注抗矩框架（mrf）。首先，提出了一个建立具有滞后非线性行为的多自由度（mof） ROM的框架，建立规则集，从常用的建筑描述(如层数、层高、设计规范或结构系统类型及其材料（例如，钢筋混凝土或钢）中导出ROM参数的标称值。规则集强调显式地对故事之间的差异进行建模，而不是依赖于利用等效于非弹性单自由度系统的简化近似。通过在上述标称值上分配概率分布来量化用于开发ROM的信息的保真度，在不同参数上具有不同程度的不确定性。然后进行参数和全局敏感性分析，以调查该信息保真度的重要性。讨论了利用重采样原理的计算工作流，以提高这些分析的计算效率。研究结果为建立不同mof原型ROMs的关键参数提供了独特的见解，并为在区域尺度地震风险评估中部署ROMs时需要收集的数据类型提供了更高的保真度（置信度），以提高预测精度。

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引用次数: 0

Efficient reliability analysis for offshore wind turbines: Leveraging SVM and augmented oversampling technique 海上风力发电机的高效可靠性分析：利用支持向量机和增广过采样技术

IF 5.7 1区工程技术 Q1 ENGINEERING, CIVIL

Structural Safety

Pub Date : 2025-04-27 DOI: 10.1016/j.strusafe.2025.102597

Xukai Zhang, Arash Noshadravan

This study develops an efficient reliability assessment method designed to optimize maintenance strategies for Offshore Wind Turbines (OWT), aiming for significant cost savings through reduced maintenance frequency and enhanced efficiency. Effective cost management requires a robust and accurate approach for reliability-based lifecycle management. Therefore, this paper introduces an improved predictive maintenance method, grounded on the reliability-based failure probability of OWT systems. To augment computational efficiency and diminish computational time, a surrogate model is proposed for the estimation of failure probability. This surrogate model integrates the classification strengths of Support Vector Machine (SVM) with an augmented Synthetic Minority Oversampling Technique (SMOTE), specifically adapted for extremely imbalanced data. The study’s contributions are twofold: firstly, it develops a novel reliability-based predictive maintenance method allowing for the quantitative assessment of OWTs’ current conditions; secondly, it presents a surrogate model adept at managing extreme data imbalance, thereby enhancing prediction accuracy. The effectiveness of the surrogate model is validated through a case study under two distinct weather conditions. The proposed predictive maintenance method serves as an efficient and effective tool for improved maintenance planning for OWTs.

本研究开发了一种有效的可靠性评估方法，旨在优化海上风力涡轮机（OWT）的维护策略，旨在通过减少维护频率和提高效率来显著节省成本。有效的成本管理需要基于可靠性的生命周期管理的稳健和准确的方法。因此，本文以OWT系统基于可靠性的故障概率为基础，提出了一种改进的预测性维修方法。为了提高计算效率和减少计算时间，提出了一种失效概率估计的替代模型。该代理模型将支持向量机（SVM）的分类优势与增强型合成少数过采样技术（SMOTE）相结合，特别适用于极度不平衡的数据。该研究的贡献有两个方面：首先，它开发了一种新的基于可靠性的预测性维护方法，允许对owt的当前状况进行定量评估；其次，提出了一种能够处理极端数据不平衡的代理模型，从而提高了预测精度。通过两种不同天气条件下的案例研究，验证了代理模型的有效性。提出的预测维修方法是改进维修计划的有效工具。

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引用次数: 0

The probabilistic inverse problem and its solving method based on probability density evolution theory and convex optimization algorithms 基于概率密度演化理论和凸优化算法的概率反问题及其求解方法

IF 5.7 1区工程技术 Q1 ENGINEERING, CIVIL

Structural Safety

Pub Date : 2025-04-24 DOI: 10.1016/j.strusafe.2025.102600

Yuhan Zhu, Jie Li

A probabilistic inverse problem-solving method based on the framework of Probability Density Evolution Theory and convex optimization algorithms is proposed. This method reformulates the identification of the random source as a quadratic programming problem with linear constraints, identifying the probability density function of the random source in a physical stochastic system even when the distribution type of the random source is entirely unknown. Through singular value decomposition of the quadratic matrix, an error analysis is performed, revealing that the solvability of the probabilistic inverse problem fundamentally depends on the injectivity of the mapping from the random source space to the response space. Case studies confirm that the proposed method is not sensitive to prior information and does not require any predefined assumptions about the distribution type. Meanwhile, it can preliminarily determine whether the inverse problem is solvable before the computational process begins.

提出了一种基于概率密度演化理论和凸优化算法框架的概率逆问题求解方法。该方法将随机源的识别重新表述为具有线性约束的二次规划问题，即使在随机源的分布类型完全未知的情况下，也能识别物理随机系统中随机源的概率密度函数。通过对二次矩阵的奇异值分解进行误差分析，揭示了概率逆问题的可解性从根本上取决于随机源空间到响应空间的映射的注入性。案例研究证实，所提出的方法对先验信息不敏感，并且不需要对分布类型进行任何预定义的假设。同时，可以在计算过程开始前初步判断逆问题是否可解。

引用次数: 0

Frequency domain method for random vibration analysis of nonlinear systems under time-varying coherent nonstationary excitations 时变相干非平稳激励下非线性系统随机振动分析的频域方法

IF 5.7 1区工程技术 Q1 ENGINEERING, CIVIL

Structural Safety

Pub Date : 2025-04-21 DOI: 10.1016/j.strusafe.2025.102601

Ning Zhao , Xu Wang , Yu Wu , Fengbo Wu , Shaomin Jia

Strong earthquakes, downbursts, and typhoons are extreme events that involve time-varying coherent excitations, which are crucial in accurately analyzing the structural response. However, most current methods for nonstationary random vibration analysis assume time-invariant coherence, which fails to capture the time-varying nature of real-world excitations. To address this gap, this study proposes an effective and efficient frequency domain analysis framework for nonlinear systems under time-varying coherent nonstationary excitations. This framework is grounded in the equivalent linearization technique and an enhanced evolutionary spectral method (EESM). Through the use of the equivalent linearization technique, a series of equivalent linear systems replaces the initial nonlinear system; with EESM, the highly efficient analysis of time-varying coherent nonstationary random vibrations in linear systems can be performed, requiring only a limited number of time history analyses and fast Fourier transform operations. For local nonlinear systems, the efficient frequency domain method is more favorable in terms of efficiency due to the explicit calculation advantages of EESM. The specific applications for Duffing system and hysteretic system are presented to demonstrate the reliable accuracy and exceptional efficiency of this method, thereby showcasing its potential in addressing large-scale nonlinear system problems.

强震、降爆和台风是涉及时变相干激励的极端事件，对准确分析结构响应至关重要。然而，目前大多数非平稳随机振动分析的方法都假设了时不变的相干性，这无法捕捉到现实世界激励的时变性质。为了解决这一差距，本研究提出了一种有效的时变相干非平稳激励下非线性系统的频域分析框架。该框架以等效线性化技术和改进的演化谱方法（EESM）为基础。利用等效线性化技术，用一系列等效线性系统代替初始非线性系统；利用EESM，可以对线性系统中的时变相干非平稳随机振动进行高效分析，只需要有限数量的时程分析和快速傅里叶变换操作。对于局部非线性系统，有效频域法由于EESM的显式计算优势，在效率上更有利。通过对Duffing系统和滞回系统的具体应用，证明了该方法的可靠精度和卓越的效率，从而展示了其在解决大规模非线性系统问题方面的潜力。

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引用次数: 0