Structural Safety最新文献_第2页

Nonparametric sector dependence modelling for the directional synthesis of local wind climate and building aerodynamic responses: Adaptive kernel-based approach 局地风气候和建筑空气动力响应定向综合的非参数扇区依赖建模：基于自适应核的方法

IF 6.3 1区工程技术 Q1 ENGINEERING, CIVIL

Structural Safety

Pub Date : 2025-11-24 DOI: 10.1016/j.strusafe.2025.102671

Nahom K. Berile , Matiyas A. Bezabeh , Seifu A. Bekele

Accounting for the directionality of wind is crucial in estimating the response of buildings to wind load. Sector-based directionality techniques are widely used for analyzing directionality effects. In single- and multi-sector methods, directional sectors of the local wind climate and building aerodynamic responses are analyzed separately, while their statistical correlation is assumed to be fully dependent or independent, respectively. The multi-sector method, which is preferred for structural design due to its relative conservatism, requires the use of wide sectors to ensure the statistical independence assumption holds. This, in turn, requires interpolating aerodynamic response parameters, which is prone to errors due to rapid variations with small directional changes. Moreover, performance-based wind design (PBWD) approaches, as outlined in the American Society of Civil Engineers Prestandard for PBWD, require 10-degree or narrower sectors in aerodynamic response representation for detailed directional resolution. Narrow wind sectors often exhibit correlation, necessitating accurate dependence modelling. Parametric copula-based methods have been used to model sector correlations; however, they impose restrictive assumptions on dependence patterns. Therefore, this paper proposes a sector-based directionality technique with nonparametric dependence modelling using adaptive kernel density estimators. To demonstrate the applicability and accuracy of the method, wind responses of a prototype mass-timber building hypothetically located in three cities: i.e., Toronto (Canada), Melbourne (Australia), and Baltimore (USA), were predicted. The predictions were compared with responses empirically computed from historical records. The results demonstrated that the method extends the applicability of sector-based directionality analysis to narrow sectors, making it suitable for PBWD approaches.

考虑风的方向性是估算建筑物对风荷载响应的关键。基于扇区的方向性技术被广泛用于分析方向性效应。在单扇区法和多扇区法中，分别分析了局地风气候和建筑物气动响应的方向扇区，并分别假设它们的统计相关性是完全依赖的或独立的。由于其相对保守性，多部门方法是结构设计的首选方法，它要求使用广泛的部门来确保统计独立性假设成立。反过来，这需要插值空气动力学响应参数，这很容易由于小的方向变化的快速变化而产生误差。此外，基于性能的风设计（PBWD）方法，如美国土木工程师协会PBWD预标准所述，需要10度或更窄的空气动力学响应表示区域，以获得详细的方向分辨率。狭窄的风力部门往往表现出相关性，需要精确的依赖模型。基于参数公式的方法已被用于建模部门相关性；然而，它们对依赖模式施加了限制性假设。因此，本文提出了一种基于扇区的方向性技术，该技术使用自适应核密度估计器进行非参数依赖建模。为了证明该方法的适用性和准确性，对位于三个城市（即多伦多（加拿大）、墨尔本（澳大利亚）和巴尔的摩（美国））的大型木结构原型建筑的风响应进行了预测。这些预测与根据历史记录经验计算的结果进行了比较。结果表明，该方法将基于部门的方向性分析的适用性扩展到狭窄的部门，使其适用于PBWD方法。

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引用次数: 0

Enhancing reliability analysis with limited observations: A statistical framework for system safety margins 用有限的观察增强可靠性分析：系统安全裕度的统计框架

IF 6.3 1区工程技术 Q1 ENGINEERING, CIVIL

Structural Safety

Pub Date : 2025-11-17 DOI: 10.1016/j.strusafe.2025.102670

Guillaume Perrin , Julien Reygner , Vincent Chabridon

The reliability analysis of complex systems is crucial for cost-effective evaluations, particularly when using deterministic black-box models. This study examines system performance under uncertainty, where the input vector

x \in X \subset R^{d}

defines both system and environmental conditions, and failure is characterized by

F = (x \in X ∣ y (x) ⩽ s^{⋆})

, with

y

the variable of interest and

s^{⋆} \in R

a given threshold. Since

x

is uncertain, a probabilistic analysis is required to ensure robust safety assessments. Such an analysis typically involves two key steps: first, estimating the system’s probability of failure (noted

p_{f}

), and then, evaluating it against safety standards or expert knowledge. While considerable effort has been invested in proposing efficient methods for estimating

p_{f}

, little attention has been paid to the decision phase, which should take into account the uncertainties. This work focuses on the definition and use of safety margins in system reliability analysis with a final decision making purpose, especially when the knowledge of the input vectors

x

is limited to a finite set of

n

observations. A key distinction is made between cases where

n

is large or small relative to

1 / p_{f}

. The main contributions of the paper focus on scenarios with small

n

and propose two approaches for defining reasonable safety margins. The first estimates the probability distribution of

x

, while the second, based on extreme value theory, directly assesses the tail behavior of the output distribution. The proposed framework is validated through numerical case studies.

复杂系统的可靠性分析对于成本效益评估至关重要，特别是在使用确定性黑盒模型时。本研究考察了不确定性下的系统性能，其中输入向量x∈x≠Rd定义了系统和环境条件，故障的特征为F=x∈x∣y(x)≤s - - -其中y是感兴趣的变量，s - - -∈R是给定的阈值。由于x是不确定的，因此需要进行概率分析以确保可靠的安全评估。这样的分析通常包括两个关键步骤：首先，估计系统的故障概率（注意pf），然后，根据安全标准或专家知识对其进行评估。虽然在提出估算pf的有效方法方面投入了相当大的努力，但很少注意决策阶段，该阶段应考虑到不确定性。这项工作的重点是在系统可靠性分析中安全裕度的定义和使用，以最终决策为目的，特别是当输入向量x的知识仅限于有限的n个观察集时。在n相对于1/pf较大或较小的情况下，有一个关键的区别。本文的主要贡献集中在小n的情况下，并提出了两种定义合理安全边际的方法。前者估计x的概率分布，而后者基于极值理论，直接评估输出分布的尾部行为。通过数值案例研究验证了所提出的框架。

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引用次数: 0

Probabilistic failure mode-dependent reconstruction of the force-deformation response in reinforced concrete shear walls 基于概率破坏模式的钢筋混凝土剪力墙力-变形响应重构

IF 6.3 1区工程技术 Q1 ENGINEERING, CIVIL

Structural Safety

Pub Date : 2025-11-07 DOI: 10.1016/j.strusafe.2025.102669

Pouya Ebrahimi , Amir Hossein Asjodi , Kiarash M. Dolatshahi , Henry V. Burton

This paper aims to probabilistically reconstruct the force–deformation envelope response (or backbone curve) of reinforced concrete shear walls (RCSW) considering the uncertainties in the predictive model accuracy and material properties. A mean backbone curve and dispersion band are used to describe the probability distribution for the complete RCSW backbone curve, based on the mechanical and geometric properties of the wall. A database that includes the mechanical characteristics, cyclic data, and crack patterns for 249 RCSWs subjected to quasi-static cyclic loading is utilized. Based on the observed damage and cyclic behavior, each specimen is classified into one of three failure modes: shear, shear-flexure, or flexural. Then, a clustering algorithm is used to identify the failure mode based on critical points along the backbone curve. Using the similarity of backbone curves within each group, a hybridity index is derived to indicate the contribution of specific failure modes to the ultimate cyclic behavior. The hybridity index, along with wall geometry and mechanical properties, are used to reconstruct the full nonlinear backbone curve. The results show that considering the failure mode significantly improves the accuracy of the reconstructed mean backbone cure. Specifically, the coefficient of determination is increased by up to 0.46 relative to when the failure mode is not considered. The variability in the reconstructed backbone curve due to uncertainties in the material properties and predictive model accuracy are compared.

考虑预测模型精度和材料特性的不确定性，对钢筋混凝土剪力墙（RCSW）的力-变形包络响应（或骨干曲线）进行概率重构。基于壁面的力学和几何性质，采用平均骨架曲线和色散带来描述完整RCSW骨架曲线的概率分布。一个数据库，包括力学特性，循环数据和裂纹模式249 rcsw经受准静态循环加载。根据观察到的损伤和循环行为，每个试件被分为三种破坏模式之一：剪切、剪切-弯曲或弯曲。然后，采用基于骨干曲线上的临界点的聚类算法识别故障模式；利用每组骨干曲线的相似性，推导出混合指数来表示特定破坏模式对极限循环行为的贡献。利用杂化指数，结合壁面几何形状和力学性能，重建了完整的非线性骨架曲线。结果表明，考虑失效模式显著提高了重建平均脊柱的精度。具体而言，相对于不考虑失效模式时，决定系数增加了0.46。比较了由于材料性质的不确定性和预测模型的精度所引起的重构骨架曲线的变异性。

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引用次数: 0

Aleatory and epistemic uncertainty in reliability analysis: An engineering perspective 可靠性分析中的不确定性和认知不确定性：工程视角

IF 6.3 1区工程技术 Q1 ENGINEERING, CIVIL

Structural Safety

Pub Date : 2025-10-31 DOI: 10.1016/j.strusafe.2025.102666

Pei-Pei Li , Marcos A. Valdebenito , Chao Dang , Michael Beer , Matthias G.R. Faes

In engineering applications, aleatory and epistemic uncertainties often coexist and interact. Therefore, accurately modeling these two types of uncertainty is critical for reliability analysis and uncertainty-aware decision making. This is for instance the case when quantifying failure probabilities of engineering structures under consideration of incomplete, insufficient, imperfect, or imprecise data or knowledge. Indeed, in such a case, the failure probability can at best be described using set-theoretical or Bayesian descriptors, rather than as a crisp number to explicitly acknowledge this epistemic uncertainty. However, despite this problem being well-described in theory, we observe that there still exists a gap between the theoretical developments on the one hand, and practical engineering applications of the uncertainty modeling approaches on the other. More precisely, even though the treatment of aleatory and epistemic uncertainty is well understood, they are often still mixed implicitly, or even explicitly in engineering calculations. Therefore, this paper provides a practical engineering guide that should help select the appropriate modeling framework, be it p-boxes, fuzzy probability models, or hierarchical probability approaches, when faced with problems that are affected by both aleatory and epistemic uncertainty. By assessing the type and extent of the information and the purpose of the analysis, this work provides specific recommendations for choosing appropriate modeling methods and presents a comprehensive analysis of failure probability. Additionally, this work highlights the importance of sensitivity analysis in identifying the key parameters that most influence the failure probability. This focus enables engineers to prioritize target data collection, thereby reducing epistemic uncertainty and enhancing the credibility of reliability assessment.

在工程应用中，随机不确定性和认知不确定性经常共存并相互作用。因此，准确地对这两类不确定性进行建模对于可靠性分析和不确定性感知决策至关重要。例如，在考虑不完整、不充分、不完美或不精确的数据或知识的情况下，量化工程结构的失效概率。事实上，在这种情况下，失败概率最多只能用集合理论或贝叶斯描述符来描述，而不是作为一个明确承认这种认知不确定性的清晰数字。然而，尽管这一问题在理论上得到了很好的描述，但我们发现，不确定性建模方法的理论发展与实际工程应用之间仍然存在差距。更确切地说，尽管我们很好地理解了对偶然性和认识性不确定性的处理，但在工程计算中，它们仍然经常隐含地、甚至明确地混合在一起。因此，本文提供了一个实用的工程指南，可以帮助选择适当的建模框架，无论是p-box，模糊概率模型，还是分层概率方法，当面临受选择性和认知不确定性影响的问题时。通过评估信息的类型和范围以及分析的目的，本工作为选择适当的建模方法提供了具体建议，并提出了失效概率的综合分析。此外，这项工作强调了灵敏度分析在识别最影响失效概率的关键参数方面的重要性。这种关注使工程师能够优先考虑目标数据收集，从而减少认知的不确定性，提高可靠性评估的可信度。

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引用次数: 0

A composition of simplified physics-based model with neural operator for trajectory-level seismic response predictions of structural systems 基于简化物理模型和神经算子的结构体系地震反应轨迹预测

IF 6.3 1区工程技术 Q1 ENGINEERING, CIVIL

Structural Safety

Pub Date : 2025-10-30 DOI: 10.1016/j.strusafe.2025.102668

Jungho Kim , Sang-ri Yi , Ziqi Wang

Accurate prediction of nonlinear structural responses is essential for earthquake risk assessment and management. While high-fidelity nonlinear time history analysis provides the most comprehensive and accurate representation of the responses, it becomes computationally prohibitive for complex structural system models and repeated simulations under varying ground motions. To address this challenge, we propose a composite learning framework that integrates simplified physics-based models with a Fourier neural operator to enable efficient and accurate trajectory-level seismic response prediction. In the proposed architecture, a simplified physics-based model, obtained from techniques such as linearization, modal reduction, or solver relaxation, serves as a preprocessing operator to generate structural response trajectories that capture coarse dynamic characteristics. A neural operator is then trained to correct the discrepancy between these initial approximations and the true nonlinear responses, allowing the composite model to capture hysteretic and path-dependent behaviors. Additionally, a linear regression-based postprocessing scheme is introduced to further refine predictions and quantify associated uncertainty with negligible additional computational effort. The proposed approach is validated on three representative structural systems subjected to synthetic or recorded ground motions. Results show that the proposed approach consistently improves prediction accuracy over baseline models, particularly in data-scarce regimes. These findings demonstrate the potential of physics-guided operator learning for reliable and data-efficient modeling of nonlinear structural seismic responses.

非线性结构响应的准确预测是地震风险评估和管理的关键。虽然高保真非线性时程分析提供了最全面和准确的响应表示，但对于复杂的结构系统模型和不同地震动下的重复模拟，它在计算上变得令人望而却步。为了应对这一挑战，我们提出了一种复合学习框架，该框架将简化的基于物理的模型与傅里叶神经算子相结合，以实现高效、准确的轨迹级地震响应预测。在提出的体系结构中，通过线性化、模态缩减或求解器松弛等技术获得的简化的基于物理的模型作为预处理算子，生成捕获粗动态特性的结构响应轨迹。然后训练一个神经算子来纠正这些初始近似和真实非线性响应之间的差异，允许复合模型捕捉滞后和路径依赖行为。此外，引入了基于线性回归的后处理方案，以进一步细化预测和量化相关的不确定性，而额外的计算工作量可以忽略不计。在三个具有代表性的结构体系上对所提出的方法进行了验证。结果表明，该方法持续提高了基线模型的预测精度，特别是在数据稀缺的情况下。这些发现证明了物理导向的操作人员学习在非线性结构地震反应的可靠和数据高效建模方面的潜力。

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引用次数: 0

Resilience based seismic design of CLT coupled walls and Glulam moment resisting frame system 基于回弹性的CLT墙体-胶合木抗弯矩框架体系抗震设计

IF 6.3 1区工程技术 Q1 ENGINEERING, CIVIL

Structural Safety

Pub Date : 2025-10-23 DOI: 10.1016/j.strusafe.2025.102667

Biniam Tekle Teweldebrhan , Solomon Tesfamariam

Seismic design philosophies have evolved significantly over the past several decades, shifting from life safety focused – prescriptive methods – towards approaches that also consider post-earthquake recovery, economic losses, and social impacts. This transition has led to the emergence of Resilience-Based Seismic Design (RBSD). While RBSD has been explored for concrete- and steel-based structural systems, its application to timber structures remains limited. Accordingly, this study develops a novel RBSD framework for a 20-storey timber building combining Cross-Laminated Timber Coupled Walls (CLTCWs) and a Glulam Moment-Resisting Frame (GMRF) to resist lateral loads. A baseline system is designed and assessed using FEMA P-58 methodology and the TREADS repair time model under multiple seismic intensity levels. Using this baseline, a Multi-Objective Optimization (MOO) framework is formulated with conflicting objectives: minimizing structural strength demands while maximizing its resilience. A dynamic deep learning-based surrogate model is trained to predict seismic performance across varying design parameters. Non-dominated Pareto-optimal solutions are obtained using a genetic algorithm and further evaluated through nonlinear time–history analyses. Results show that the optimized solutions achieve notable improvements in both structural efficiency and resilience performance compared to the baseline system. This research contributes a flexible and data-driven methodology for advancing the design of resilient, high-performance tall timber buildings.

在过去的几十年里，抗震设计理念发生了重大变化，从以生命安全为重点的规范方法，转向同时考虑震后恢复、经济损失和社会影响的方法。这种转变导致了基于弹性的抗震设计（RBSD）的出现。虽然RBSD已经在混凝土和钢基结构体系中进行了探索，但它在木结构中的应用仍然有限。因此，本研究为一座20层的木结构建筑开发了一种新型的RBSD框架，该框架结合了交叉层合木偶联墙（CLTCWs）和胶合木抗弯矩框架（GMRF）来抵抗侧向荷载。使用FEMA P-58方法和TREADS修复时间模型设计和评估了多个地震烈度级别下的基线系统。利用这一基线，一个多目标优化（MOO）框架被制定为相互冲突的目标：最小化结构强度需求，同时最大化其弹性。一个基于动态深度学习的代理模型被训练来预测不同设计参数下的抗震性能。利用遗传算法得到非支配pareto最优解，并通过非线性时程分析进一步评估。结果表明，与基准体系相比，优化方案在结构效率和回弹性能方面均有显著提高。这项研究为推进弹性、高性能高层木结构建筑的设计提供了一种灵活的数据驱动方法。

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引用次数: 0

A probabilistic framework to construct tropical cyclone loss models for building portfolios 为建筑投资组合构建热带气旋损失模型的概率框架

IF 6.3 1区工程技术 Q1 ENGINEERING, CIVIL

Structural Safety

Pub Date : 2025-10-13 DOI: 10.1016/j.strusafe.2025.102665

Yu Liang , Hao Zhang , Cao Wang , Diqi Zeng

Tropical cyclones (TCs) evolve over time and space and can cause substantial damage to building portfolios. Therefore, timely and accurate TC damage assessment is essential for effective risk management. One practical approach is to establish a relationship between hazard intensity (e.g., wind speeds) and regional damage. However, when the study area is large, spatial heterogeneity, such as clustered building distributions, terrain variability, and spatial variations in wind speeds, can hinder accurate modelling of the hazard-damage relationship. To address this challenge, the present study employs a spatial clustering algorithm to divide the entire area into multiple sub-regions with relatively homogeneous characteristics. For each sub-region, a TC loss model is developed as a function of wind speed at the sub-regional centroid and the corresponding building portfolio loss ratio. In practice, losses in all sub-regions are first assessed individually and then aggregated to estimate the total regional loss. This divide-and-aggregate approach significantly improves the accuracy and applicability of TC loss modelling and can be readily applied to various contexts, such as long-term risk management in large-scale communities.

热带气旋（tc）随着时间和空间的变化而演变，并可能对建筑组合造成重大损害。因此，及时准确地评估TC损伤对有效的风险管理至关重要。一种实际的方法是建立灾害强度（如风速）和区域损害之间的关系。然而，当研究区域较大时，空间异质性，如集群建筑分布、地形变异性和风速的空间变化，可能会阻碍对灾害-损害关系的准确建模。为了解决这一挑战，本研究采用空间聚类算法将整个区域划分为多个具有相对均匀特征的子区域。对于每个子区域，建立了以子区域质心风速和相应建筑组合损失率为函数的TC损失模型。在实践中，首先对所有分区域的损失进行单独评估，然后汇总估算区域损失总额。这种划分和汇总方法显著提高了TC损失模型的准确性和适用性，可以很容易地应用于各种情况，例如大规模社区的长期风险管理。

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引用次数: 0

Branch-and-bound algorithm for efficient reliability analysis of general coherent systems 一般相干系统高效可靠性分析的分支定界算法

IF 6.3 1区工程技术 Q1 ENGINEERING, CIVIL

Structural Safety

Pub Date : 2025-09-22 DOI: 10.1016/j.strusafe.2025.102653

Ji-Eun Byun , Hyeuk Ryu , Daniel Straub

Branch-and-bound algorithms, also known as bounding or decomposition algorithms, have been developed for reliability analysis of coherent systems. They can find a computationally efficient representation of a system failure or survival event, which can be re-used when the input probability distributions or reliabilities change, for example with time or when new data is available. Existing branch-and-bound algorithms can handle only a limited set of system performance functions, mostly network connectivity and maximum flow. Furthermore, they run redundant analyses on component vector states whose system state can be inferred from previous analysis results. We address these limitations by proposing the branch-and-bound for reliability analysis of general coherent systems (BRC) algorithm: an algorithm that automatically finds minimal representations of failure/survival events of general coherent systems. Computational efficiency is attained by dynamically inferring importance of component events from hitherto obtained results. We demonstrate advantages of the BRC method as a real-time risk management tool by application to the Eastern Massachusetts highway benchmark network.

分支定界算法，也称为定界或分解算法，已被发展用于相干系统的可靠性分析。它们可以找到系统故障或生存事件的计算效率表示，当输入概率分布或可靠性发生变化时，例如随着时间的推移或当有新数据可用时，可以重用这些表示。现有的分支定界算法只能处理有限的系统性能功能，主要是网络连接和最大流量。此外，他们对组件矢量状态进行冗余分析，系统状态可以从先前的分析结果中推断出来。我们通过提出一般相干系统可靠性分析的分支定界算法（BRC）来解决这些限制：一种自动找到一般相干系统故障/生存事件的最小表示的算法。通过从迄今为止获得的结果动态推断组件事件的重要性来获得计算效率。通过将BRC方法应用于马萨诸塞州东部公路基准网络，证明了BRC方法作为实时风险管理工具的优势。

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引用次数: 0

LSTM-augmented probability-informed neural network-driven evolution estimation for time-dependent reliability analysis 时变可靠性分析的lstm增强概率神经网络驱动进化估计

IF 6.3 1区工程技术 Q1 ENGINEERING, CIVIL

Structural Safety

Pub Date : 2025-09-22 DOI: 10.1016/j.strusafe.2025.102652

Hongyuan Guo , Jiaxin Zhang , You Dong , Dan M. Frangopol

For time-dependent dynamic systems, the inputs include not only random variables but also stochastic processes, posing significant challenges to traditional Time-Dependent Reliability Analysis (TDRA) methods regarding efficiency, accuracy, and generality. To address these challenges, this paper develops a Long Short-Term Memory (LSTM)-Augmented Probability-Informed Neural Network Evolution (LPNE) framework for TDRA of dynamic systems. A set of local performance functions is introduced by selecting representative points for time-independent random variables. Subsequently, an LSTM network is trained to learn the time-dependent behavior of the dynamic system for each local limit state function. Multiple local surrogate LSTM models are then employed to assemble an enhanced dataset accordingly. Based on the enriched dataset, point-evolution estimation is performed with a more ample sample size, integrating Deep Neural Networks (DNN) with the physical equation information of the generalized probability density evolution equation (GDEE). The proposed framework can effectively compensate for the limitations of existing point-evolution approaches that struggle to consider scenarios with stochastic process inputs. The proposed LPNE is validated through four benchmark cases: a simple numerical example, scenarios involving corroded steel beams, corrosion-induced deterioration of steel structures, and the seismic response of multi-story shear frame structure. The results demonstrate that LPNE can accurately and efficiently estimate time-dependent failure probabilities with a limited number of representative points without requiring additional samples.

对于时变动态系统，输入不仅包括随机变量，还包括随机过程，这对传统的时变可靠性分析方法在效率、准确性和通用性方面提出了重大挑战。为了解决这些挑战，本文开发了一个用于动态系统TDRA的长短期记忆(LSTM)-增强概率-通知神经网络进化（LPNE）框架。通过选取时间无关随机变量的代表点，引入一组局部性能函数。然后，训练LSTM网络学习动态系统的每个局部极限状态函数的时变行为。然后使用多个本地代理LSTM模型相应地组装增强的数据集。在此基础上，将深度神经网络（DNN）与广义概率密度进化方程（GDEE）的物理方程信息相结合，以更大的样本量进行点进化估计。所提出的框架可以有效地弥补现有的点进化方法的局限性，这些方法难以考虑具有随机过程输入的场景。提出的LPNE通过四个基准案例进行验证：一个简单的数值例子，涉及腐蚀钢梁的场景，钢结构腐蚀引起的劣化，以及多层剪力框架结构的地震反应。结果表明，LPNE可以在不需要额外样本的情况下，以有限的代表性点准确有效地估计随时间变化的失效概率。

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引用次数: 0

Seismic risk assessment methodology for large-span CFST arch bridges in near-fault areas based on fragility analysis 基于易损性分析的近断裂带大跨度钢管混凝土拱桥地震风险评价方法

IF 6.3 1区工程技术 Q1 ENGINEERING, CIVIL

Structural Safety

Pub Date : 2025-09-18 DOI: 10.1016/j.strusafe.2025.102656

Lihan Xu , Lueqin Xu , Dong Xie , Jianting Zhou

Large-span concrete-filled steel tube (CFST) arch bridges are widely built in high-seismicity mountainous areas in China due to their low maintenance costs and high adaptability to the challenging construction environments. The dynamic response of such bridges under seismic loading is highly complex, and their seismic performance is a major concern for multiple stakeholders. This study proposes a seismic risk assessment method for large-span CFST arch bridges from a risk perspective, based on seismic fragility analysis. The method begins with seismic hazard analysis of the bridge site, followed by seismic risk scenario identification of the bridge through fragility analysis, then quantifies the seismic risk scenarios from the perspective of economic losses, and finally evaluates the quantified results of discrete risk scenarios based on tolerance theory. A CFST arch bridge located in a near-fault area is analyzed as a case study, with two design schemes and five annual earthquake frequencies considered to validate the feasibility of the proposed method. The research results show that the seismic risk assessment method effectively identifies risk scenarios and their characteristics across different design schemes and seismic frequencies. Additionally, as the method presents results through macro risk tolerance zone divisions, it offers more intuitive and stakeholder-friendly outputs compared to traditional engineering-technology-based assessments (e.g., seismic fragility curves). Overall, the proposed method serves as a robust decision-making tool for the design, operation, and maintenance of large-span CFST arch bridges and similar structures with complex seismic responses.

大跨度钢管混凝土拱桥由于其维护成本低、对恶劣施工环境适应性强等优点，在中国的高震山区得到了广泛的应用。此类桥梁在地震荷载作用下的动力响应非常复杂，其抗震性能是各方关注的焦点。基于地震易损性分析，提出了一种基于风险视角的大跨度钢管混凝土拱桥地震风险评估方法。该方法首先对桥梁场地进行地震危险性分析，然后通过易损性分析对桥梁进行地震风险情景识别，然后从经济损失角度对地震风险情景进行量化，最后基于容差理论对离散风险情景量化结果进行评价。以近断裂带的钢管混凝土拱桥为例，考虑了两种设计方案和5个年地震频率，验证了所提方法的可行性。研究结果表明，该方法能有效识别不同设计方案和不同地震频率下的地震风险情景及其特征。此外，由于该方法通过宏观风险容忍区划分来呈现结果，与传统的基于工程技术的评估（例如地震易损性曲线）相比，它提供了更直观、更有利于利益相关者的输出。总体而言，该方法可为大跨度钢管混凝土拱桥及类似复杂地震响应结构的设计、运行和维护提供可靠的决策工具。

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