Reliability Engineering & System Safety最新文献_第4页

Active learning Kriging with functional dimension reduction for reliability analysis of stochastic dynamical systems 基于功能降维的主动学习Kriging随机动力系统可靠性分析

IF 11 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

Reliability Engineering & System Safety

Pub Date : 2026-09-01 Epub Date: 2026-02-05 DOI: 10.1016/j.ress.2026.112360

Zhouzhou Song , Chao Dang , Marcos A. Valdebenito , Matthias G.R. Faes

In engineering applications, it is important to evaluate the reliability of dynamical systems under various uncertainties arising from materials, manufacturing processes, and external excitations. Surrogate models are widely employed to enable efficient reliability analysis in complex, computationally intensive engineering problems. However, building high-accuracy surrogate models for estimating dynamic systems’ reliability with limited computational resources remains a significant challenge. This paper presents a novel active learning Kriging method based on functional dimension reduction (AKFDR) for the efficient estimation of first-passage failure probabilities of stochastic dynamical systems. In this approach, Kriging surrogate models are constructed in a latent functional space obtained through functional dimension reduction, enabling probabilistic predictions of dynamic responses. By leveraging the prediction uncertainty and the concept of trajectory misclassification probability (TMP), a new learning function incorporating a weighted correlation criterion is then developed to guide the selection of the best next sample for model enhancement. Furthermore, an error-based stopping criterion is proposed to judge the convergence of the active learning process. The final surrogate model is then used to estimate the first-passage failure probability via Monte Carlo simulation. Through three numerical examples of varying dimensionality and complexity, it is shown that the proposed method is efficient and accurate for first-passage probability evaluation of stochastic dynamical systems.

在工程应用中，评估动力系统在由材料、制造过程和外部激励引起的各种不确定性下的可靠性是很重要的。代理模型被广泛应用于复杂的、计算密集型的工程问题中，以实现高效的可靠性分析。然而，在有限的计算资源下建立高精度的替代模型来估计动态系统的可靠性仍然是一个重大挑战。提出了一种基于泛函降维（AKFDR）的主动学习Kriging方法，用于随机动力系统首路失效概率的有效估计。在这种方法中，Kriging代理模型在通过功能降维获得的潜在功能空间中构建，从而实现动态响应的概率预测。利用预测的不确定性和轨迹误分类概率（TMP）的概念，建立了一个新的学习函数，结合加权相关准则来指导选择最佳的下一个样本进行模型增强。此外，提出了一种基于误差的停止准则来判断主动学习过程的收敛性。通过蒙特卡罗模拟，利用最终的代理模型来估计首通道失效概率。通过三个不同维数和复杂度的数值算例，表明该方法对于随机动力系统的首通概率评估是有效和准确的。

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引用次数: 0

A hybrid neural network-based concrete gravity dam seismic response prediction method quantifying reservoir water level uncertainty 基于混合神经网络的混凝土重力坝地震反应预测方法，量化水库水位不确定性

IF 11 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

Reliability Engineering & System Safety

Pub Date : 2026-09-01 Epub Date: 2026-02-05 DOI: 10.1016/j.ress.2026.112366

Bo Liu , Qiang Xu , Jianying Xing , Jianyun Chen , Mingming Wang , Jing Li , Tianran Zhang

Accurate real-time prediction of the seismic response of gravity dams is critical for their safety assessment, however, the seismic response characteristics of gravity dams are highly variable due to variations in the gravity dam-reservoir-foundation system (e.g., reservoir water level) and seismic characteristics (e.g., impulse characteristics). To address these challenges, this study proposes a hybrid neural network model (SW-HNN) that integrates a new system feature attention mechanism and a unique wavelet decomposition-based impulse identification module to predict the impulse seismic response of gravity dams, considering reservoir water level variability. The model effectively captures the complex interrelations between system characteristics of the gravity dam-reservoir-foundation system, ground motion impulse characteristics, and dam responses. To further enhance model performance, an improved balanced sampling technique is developed for ground motion datasets, which enriches the feature set and mitigates the influence of imbalanced feature distributions. The required datasets for model training and validation are generated through nonlinear time-history analyses of gravity dam-reservoir-foundation systems with varying reservoir water levels. Experimental results confirm the accuracy and robustness of the SW-HNN model. The validity and superiority of the SW-HNN model are further verified by ablation analysis and comparison experiments. Additionally, the SW-HNN is an interpretable deep learning model capable of ranking system features by evaluating changes in its internal parameters.

准确实时预测重力坝的地震反应对其安全性评价至关重要，然而，由于重力坝-水库-基础系统（如水库水位）和地震特征（如冲击特性）的变化，重力坝的地震反应特征变化很大。为了解决这些挑战，本研究提出了一种混合神经网络模型（SW-HNN），该模型集成了一种新的系统特征注意机制和一种独特的基于小波分解的脉冲识别模块，用于在考虑水库水位变化的情况下预测重力坝的脉冲地震响应。该模型有效地捕捉了重力坝-水库-基础系统的系统特性、地震动脉冲特性和大坝响应之间的复杂相互关系。为了进一步提高模型的性能，提出了一种改进的平衡采样技术，丰富了地面运动数据的特征集，减轻了不平衡特征分布的影响。模型训练和验证所需的数据集是通过对具有不同水库水位的重力坝-水库-基础系统的非线性时程分析生成的。实验结果验证了SW-HNN模型的准确性和鲁棒性。通过烧蚀分析和对比实验，进一步验证了SW-HNN模型的有效性和优越性。此外，SW-HNN是一个可解释的深度学习模型，能够通过评估其内部参数的变化来对系统特征进行排名。

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引用次数: 0

Robust decision-based proactive prevention method for renewable distribution systems under typhoon involved with rainstorm events 基于鲁棒决策的台风卷入暴雨条件下可再生配电系统主动预防方法

IF 11 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

Reliability Engineering & System Safety

Pub Date : 2026-09-01 Epub Date: 2026-01-23 DOI: 10.1016/j.ress.2026.112273

Wei Zhang , Cong Zhang , Dapeng Wang , Jiayong Li , Lipeng Zhu , Ke Zhou , Zhikang Shuai

This paper proposes a novel proactive prevention method for renewable distribution systems (RDSs) to address the limited flexibility and economic inefficiency of existing approaches due to insufficient consideration of typhoon rainstorm evolution and component status uncertainties. First, a mechanism-based and data-driven uncertainty model for typhoon rainstorm is established, and a dual-uncertainty scenario generation method for disaster evolution paths and component statuses is further developed to capture their spatiotemporal impacts on the RDSs. Then, a robust decision-based proactive prevention framework for the RDSs is proposed considering the coupling among typhoon rainstorm disasters, system operating statuses, and proactive dispatching. This framework dynamically prioritizes scheduling schemes that maximize the number of out-of-service vulnerable components while meeting the system’s total load demand. Based on this, a multi-objective proactive prevention model is further formulated as a mixed-integer nonlinear programming (MINLP) problem, and a convex solution algorithm is developed to enhance computational efficiency. Finally, the proposed method is tested on the modified IEEE 33-bus system and the 10 kV actual distribution system in Southern China to validate its effectiveness. Numerical results show that the proposed method effectively reduces overall system vulnerability under typhoon rainstorm and improves the proactive prevention capability of the RDSs.

本文提出了一种新的可再生配电系统（RDSs）主动预防方法，以解决现有方法由于没有充分考虑台风暴雨演变和组件状态不确定性而造成的灵活性有限和经济效率低下的问题。首先，建立了基于机制和数据驱动的台风暴雨不确定性模型，并进一步建立了灾害演化路径和成分状态的双不确定性情景生成方法，以捕捉其对rds的时空影响。在此基础上，考虑台风暴雨灾害、系统运行状态和主动调度三者之间的耦合关系，提出了基于决策的强鲁棒预警框架。该框架在满足系统总负载需求的同时，动态地优先考虑最大限度地减少易受攻击组件的退出服务的调度方案。在此基础上，进一步将多目标主动预防模型表述为混合整数非线性规划（MINLP）问题，并提出了一种凸解算法来提高计算效率。最后，在改进后的IEEE 33总线系统和中国南方10kv实际配电系统上进行了测试，验证了该方法的有效性。数值结果表明，该方法有效地降低了台风暴雨下系统的整体脆弱性，提高了rds的主动防御能力。

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引用次数: 0

Reliability-driven adaptive multi-level pre-optimization control method for reusable launch vehicles under strong stochastic wind disturbances 强随机风扰动下可重复使用运载火箭可靠性驱动自适应多级预优化控制方法

IF 11 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

Reliability Engineering & System Safety

Pub Date : 2026-09-01 Epub Date: 2026-01-30 DOI: 10.1016/j.ress.2026.112327

Dawen Huang

Strong stochastic wind disturbances pose a major threat to the landing reliability of reusable launch vehicles, potentially leading to attitude instability, trajectory deviation, and even structural damage. Traditional control methods, which heavily rely on precise dynamic models and online state observation or prediction, often struggle to ensure reliability under such conditions. To address this challenge, this work proposes a reliability-driven landing pre-optimization method that incorporates regional stochastic wind characteristics, thereby eliminating the need for online observation or prediction. The landing dynamics and stochastic wind field models are established to quantify the destructive impact of winds on landing reliability. An adaptive multi-level control strategy is then introduced, which hierarchically deploys simplified control laws based on real-time altitude and velocity. This design effectively compensates for time-varying wind disturbances without depending on online observers or pre-planned trajectories. Furthermore, a reliability-driven offline optimization framework is developed to tune the control parameters and landing initiation conditions. These key parameters are optimized offline through large-scale Monte Carlo simulations across diverse wind scenarios, thus avoiding the computational burden of online optimization. Finally, the optimal parameters and conditions are pre-optimized to adapt to regional stochastic winds. Results demonstrate that the proposed method achieves a landing reliability of >99.5% and reduces the maximum landing deviation by 99.52%. In comparative studies, the offline pre-optimization method shows superior performance to typical online Model Predictive Control, improving reliability by 14.1%. The proposed strategy offers a robust and practical solution for achieving high-reliability landings under strong stochastic winds.

强随机风扰动对可重复使用运载火箭的着陆可靠性构成重大威胁，可能导致姿态不稳定、轨迹偏离甚至结构损坏。传统的控制方法严重依赖于精确的动态模型和在线状态观测或预测，往往难以保证这种情况下的可靠性。为了应对这一挑战，本研究提出了一种可靠性驱动的着陆预优化方法，该方法结合了区域随机风特征，从而消除了在线观测或预测的需要。为了量化风对着陆可靠性的破坏性影响，建立了着陆动力学和随机风场模型。然后引入了一种自适应多级控制策略，该策略基于实时高度和速度分层部署简化的控制律。这种设计有效地补偿了时变的风干扰，而不依赖于在线观测者或预先规划的轨迹。在此基础上，建立了可靠性驱动的离线优化框架，对控制参数和起降条件进行了优化。这些关键参数通过大规模蒙特卡罗模拟在不同的风场景下进行离线优化，从而避免了在线优化的计算负担。最后，对最优参数和条件进行了预优化，以适应区域随机风。结果表明，该方法的着陆可靠性为99.5%，最大着陆偏差降低了99.52%。在对比研究中，离线预优化方法的性能优于典型的在线模型预测控制，可靠性提高14.1%。所提出的策略为在强随机风条件下实现高可靠性着陆提供了一种鲁棒性和实用性的解决方案。

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引用次数: 0

Stochastic modeling of crack branching under uncertainties: A degradation branching framework 不确定条件下裂纹分支的随机建模：退化分支框架

IF 11 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

Reliability Engineering & System Safety

Pub Date : 2026-09-01 Epub Date: 2026-01-30 DOI: 10.1016/j.ress.2026.112316

Tong Wu , Ting Wang , Hanyu Li , Changxi Wang , Kang Li

This paper presents a stochastic model for dynamic fracture branching propagation under uncertainties (DFBPU) that addresses key limitations of existing degradation branching models. Existing work often assumes a single branch initiation per event and thereby understates how the initiation of multiple branches in one branching impacts total degradation. DFBPU generalizes this setting by allowing a random number of initial branches, a random number of offspring branches at each branching instant, generation-dependent crack growth rates, and random branching times, yielding a comprehensive framework for dynamic fracture processes. We derive statistical properties of total degradation, including the mean and variance, the expected number of branches, and the system reliability function. The model is validated with experimental crack branching and propagation data and complemented by a single-factor Monte Carlo sensitivity analysis, which shows that the failure threshold, initial branches and generation-dependent growth parameters dominate the variability of system reliability, while other parameters play a comparatively minor role. Finally, because it treats total crack length as the degradation indicator, the framework is directly applicable to systems where leakage risk scales with the cumulative crack extent in length or area, such as underground nuclear/medical waste repository coatings.

本文提出了不确定条件下动态裂缝分支扩展的随机模型（DFBPU），解决了现有退化分支模型的主要局限性。现有的工作通常假设每个事件一个分支的启动，因此低估了在一个分支中多个分支的启动如何影响总体退化。DFBPU通过允许随机数量的初始分支、每个分支时刻随机数量的后代分支、依赖于生成的裂纹增长速率和随机分支时间，对这种设置进行了推广，从而产生了动态断裂过程的综合框架。我们推导了总退化的统计性质，包括均值和方差、期望分支数和系统可靠性函数。利用实验裂纹分支和扩展数据对模型进行了验证，并辅以单因素蒙特卡罗灵敏度分析，结果表明，失效阈值、初始分支和发电相关增长参数对系统可靠性的变异性起主导作用，而其他参数的作用相对较小。最后，由于该框架以总裂缝长度作为退化指标，因此直接适用于泄漏风险以长度或面积累积裂缝程度为尺度的系统，如地下核/医疗废物处置库涂层。

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引用次数: 0

Investigation on new definition and proactive identification method of hazard 新危害定义及主动识别方法探讨

IF 11 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

Reliability Engineering & System Safety

Pub Date : 2026-09-01 Epub Date: 2026-02-04 DOI: 10.1016/j.ress.2026.112361

Qianru Lei, Cunbao Deng, Yansheng Wang, Zihao Zhao, Zhixin Jin

Hazard identification is a crucial task for preventing industrial accidents. However, traditional methods often treat hazards as isolated entities or static deviations, relying on historical data and linear logic, making it difficult to address dynamic interaction risks in complex systems. Although modern system safety theories emphasize systemicity, issues of insufficient practical applicability persist. This investigation holds that interaction between production activities (human, machine, management) and activity environment (environment factors in activity restriction domain) is the cause of safety issues. The safety structure composed of interaction between atomic activity factor (AAF) and atomic environment factor (AEF) is regarded as basic unit for analyzing safety issues. Hazards are redefined as safety structures that may lose stability, and classified into simple, combined, radiation and new hazards. Furthermore, the mechanisms through which hazards trigger single or multiple accidents are elucidated. Three identification criteria are established for different hazards. Its core idea is determining whether there are intersections between state set of the AEF and the environment instability domain. Finally, a hazard identification method is presented and applied to the coal mine production system. The results lay a foundation for precise control of hazards.

危害识别是预防工业事故的一项重要工作。然而，传统方法往往将风险视为孤立的实体或静态偏差，依赖于历史数据和线性逻辑，难以解决复杂系统中动态交互的风险。现代系统安全理论虽然强调系统性，但实际适用性不足的问题依然存在。本研究认为，生产活动（人、机器、管理）与活动环境（活动限制域的环境因素）之间的相互作用是造成安全问题的原因。将原子活度因子与原子环境因子相互作用构成的安全结构作为分析安全问题的基本单位。危害被重新定义为可能失去稳定性的安全结构，并分为简单危害、综合危害、辐射危害和新危害。此外，阐明了危险触发单一或多个事故的机制。针对不同的危害建立了三种识别标准。其核心思想是确定AEF的状态集与环境不稳定域之间是否存在交集。最后，提出了一种危害识别方法，并将其应用于煤矿生产系统。研究结果为灾害的精确控制奠定了基础。

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引用次数: 0

An interpretable transfer bayesian method for remaining useful life prediction 剩余使用寿命预测的可解释迁移贝叶斯方法

IF 11 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

Reliability Engineering & System Safety

Pub Date : 2026-09-01 Epub Date: 2026-01-25 DOI: 10.1016/j.ress.2026.112283

Pengcheng Xu , Naipeng Li , Yaguo Lei , Xiang Li , Lei Song , Hao Sun

Accurate and interpretable remaining useful life (RUL) prediction under domain shifts with streaming multi-sensor data remains challenging for field equipment. To address this challenge, an interpretable transfer Bayesian method is developed, integrating three key components: dynamic pseudo-domain generation (DPG), online Bayesian updating, and a pseudo-domain-based ensemble strategy. First, the DPG algorithm transforms source domains into pseudo-domain units whose degradation trajectories closely match those of the target domain. Second, a dual-scale distance is proposed to identify the cumulatively selected optimal pseudo-domain units, which are then utilized in the Bayesian updating of the degradation model. Third, adaptive weights are assigned to multi-sensor features based on the selected optimal pseudo-domain units, thereby improving RUL prediction accuracy and robustness. Finally, simulation studies and experimental validation on two real-world Stirling cryocooler datasets demonstrate that the proposed method outperforms existing methods in terms of both accuracy and robustness.

对于现场设备来说，在多传感器数据流的域变换下，准确和可解释的剩余使用寿命（RUL）预测仍然是一个挑战。为了解决这一挑战，开发了一种可解释的迁移贝叶斯方法，该方法集成了三个关键组件：动态伪域生成（DPG）、在线贝叶斯更新和基于伪域的集成策略。首先，DPG算法将源域转换为伪域单元，伪域单元的退化轨迹与目标域的退化轨迹密切匹配。其次，提出双尺度距离来识别累积选择的最优伪域单元，并将其用于退化模型的贝叶斯更新；第三，根据选取的最优伪域单元对多传感器特征赋予自适应权值，提高RUL预测精度和鲁棒性。最后，在两个真实的斯特林制冷机数据集上进行了仿真研究和实验验证，结果表明该方法在精度和鲁棒性方面都优于现有方法。

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引用次数: 0

Harnessing the integrated statistical machine learning for traffic crash injury-severity modeling 利用综合统计机器学习进行交通碰撞伤害严重程度建模

IF 11 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

Reliability Engineering & System Safety

Pub Date : 2026-09-01 Epub Date: 2026-01-29 DOI: 10.1016/j.ress.2026.112321

Pengfei Cui , Chenzhu Wang , Mohamed Abdel-Aty , Xiaobao Yang , Xingchen Zhang , Lishan Sun

Modeling the severity of traffic crash remains challenging due to the complexity, uncertainty, and heterogeneity inherent in crash datasets. Traditional statistical models often overlook interactions and structural dependencies, while machine learning methods, though effective with large datasets, struggle to capture spatial and temporal dynamics. To address these gaps, we propose the Latent Gaussian Process with Tree-Boosting Model (LGPBoost), which integrates tree-based machine learning with Gaussian process mixed effects models. This framework accounts for spatial, temporal, and grouped dependencies while capturing nonlinear feature–outcome relationships. To demonstrate the superiority of LGPBoost, we conducted a well-designed simulation experiment focused on datasets characterized by complex feature relationships and latent grouped random effects, as well as spatial and temporal variabilities. Applying the method to Florida motorcycle crashes (2014–2023) revealed that rural and less urbanized areas face significantly higher severe and fatal crash risks, underscoring the need for targeted enforcement and infrastructure improvements. Temporal instability analysis further showed evolving crash risks across regions, particularly in non-urban regions. By unifying spatial heterogeneity and temporal variability, LGPBoost provides a rigorous benchmark for reliability-oriented crash severity modeling, offering a comprehensive framework to identify risk factors, quantify non-linear effects, and capture intrinsic spatial-temporal dynamics.

由于交通事故数据集的复杂性、不确定性和异质性，对交通事故严重程度的建模仍然具有挑战性。传统的统计模型往往忽略了相互作用和结构依赖性，而机器学习方法虽然对大型数据集有效，但很难捕捉空间和时间动态。为了解决这些差距，我们提出了隐高斯过程与树增强模型（LGPBoost），它将基于树的机器学习与高斯过程混合效应模型相结合。该框架在捕获非线性特征-结果关系的同时考虑了空间、时间和分组依赖关系。为了证明LGPBoost的优势，我们针对具有复杂特征关系和潜在分组随机效应以及时空变异的数据集进行了精心设计的模拟实验。将该方法应用于佛罗里达州的摩托车事故（2014-2023）显示，农村和城市化程度较低的地区面临着明显更高的严重和致命事故风险，强调了有针对性的执法和基础设施改善的必要性。时间不稳定性分析进一步显示了不同地区，特别是非城市地区不断变化的坠机风险。通过统一空间异质性和时间变异性，LGPBoost为面向可靠性的碰撞严重性建模提供了严格的基准，提供了一个全面的框架来识别风险因素，量化非线性影响，并捕捉内在的时空动态。

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引用次数: 0

Nonlinear random walks on hypergraphs characterized by higher-order interactions 以高阶相互作用为特征的超图上的非线性随机行走

IF 11 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

Reliability Engineering & System Safety

Pub Date : 2026-09-01 Epub Date: 2026-01-31 DOI: 10.1016/j.ress.2026.112307

Meng Li , Xin Lu , Shuiling Shi , Leyang Xue , Zengru Di

Random walks, as one of the classical dynamics on networks, are capable of extracting information on the structure of interacting systems. While existing studies have extended classical network random walks to higher-order networks, prevailing models primarily assume linear relationships between transition probabilities and node hyperdegrees or hyperedge sizes, which inadequately represent the nonlinear properties of higher-order interactions. To address this, we propose a nonlinear random walk on hypergraphs that explicitly considers higher-order collaborative structures and nonlinear dynamics. By introducing a nonlinear mapping between transition probabilities and node hyperdegrees, we go beyond the linear assumption constraints of traditional random walks. Specifically, the probability of a node selecting a hyperedge is inversely proportional to a power function of its hyperdegree, where the power exponent is determined by the interaction order. We first conduct qualitative analysis of the model on star-clique structures, comparing it with classical random walk and linear random walk to reveal the mechanism by which higher-order interactions influence node importance rankings. Subsequently, we conducted node removal experiments on three large-scale datasets to validate the effectiveness of the model by comparing three structural integrity metrics. The results indicate that the proposed model consistently outperforms both Classical and Linear models across all datasets and metrics. These findings confirm that accounting for the nonlinear characteristics of higher-order interactions is essential for both accurately identifying critical nodes and understanding system robustness.

随机漫步作为经典的网络动力学之一，能够提取相互作用系统的结构信息。虽然已有研究将经典网络随机漫步扩展到高阶网络，但主流模型主要假设转移概率与节点超度或超边大小之间存在线性关系，这不足以代表高阶相互作用的非线性性质。为了解决这个问题，我们提出了一种明确考虑高阶协作结构和非线性动力学的超图上的非线性随机漫步。通过引入转移概率与节点超度之间的非线性映射，突破了传统随机漫步的线性假设约束。具体而言，节点选择超边缘的概率与其超度的幂函数成反比，其中幂指数由交互顺序决定。我们首先对星团结构模型进行定性分析，并将其与经典随机漫步和线性随机漫步进行比较，揭示高阶相互作用影响节点重要性排序的机制。随后，我们在三个大规模数据集上进行了节点去除实验，通过比较三个结构完整性指标来验证模型的有效性。结果表明，所提出的模型在所有数据集和指标上始终优于经典模型和线性模型。这些发现证实，考虑高阶相互作用的非线性特征对于准确识别关键节点和理解系统鲁棒性至关重要。

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引用次数: 0

Structural fragility curves for brick-concrete buildings subjected to debris flow loading in northern China using momentum flux approach 基于动量通量法的华北地区砖混结构在泥石流荷载作用下的易损性曲线

IF 11 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

Reliability Engineering & System Safety

Pub Date : 2026-09-01 Epub Date: 2026-02-01 DOI: 10.1016/j.ress.2026.112344

Xinren Zhang , Ying Wang , Mengxia Zhao , Jing Qi , Hao Chang , Yu Chen

Debris flows in northern China are increasing in frequency and intensity, highlighting the need for reliable vulnerability assessment frameworks. This study develops fragility curves for brick-concrete buildings by integrating momentum flux with structural lateral displacement response. Following Typhoon Doksuri in 2023, continuous rainfall triggered multiple debris flows in the Beijing–Tianjin–Hebei region. Field surveys of 583 buildings provided data supporting momentum flux-based fragility curves. Identified thresholds for complete, extensive, and moderate damage are 31.01, 22.64, and 13.74 m³/s². The model parameter β, empirically estimated from regional building damage data, enhances the predictive accuracy of vulnerability curves across hazard intensities and more realistically captures the variability among building populations than theoretical estimates. Results indicate that building stability increases with the number of floors, reducing damage probability—for instance, two-storey buildings show 5% lower damage probability than single-storey ones under moderate damage conditions. Below 80 m³/s², infilled-frame buildings (C3L) in northern China have up to 30% higher damage probability than U.S. bare-frame buildings (C1L), which neglect infill walls; above this, hazard intensity dominates and probabilities converge. Newer buildings show 15% lower damage probability, reflecting improved resilience. The proposed fragility curves serve as a physics-based probabilistic tool for risk assessment, supporting debris flow disaster prevention and mitigation in mountainous areas of northern China.

中国北方泥石流的频率和强度都在增加，这凸显了建立可靠的脆弱性评估框架的必要性。将动量通量与结构侧向位移响应相结合，建立了砖混结构的易损性曲线。2023年的台风“独瑞”之后，持续降雨引发了京津冀地区的多次泥石流。对583座建筑物的实地调查提供了支持基于动量通量的脆弱性曲线的数据。确定的完全、广泛和中度损害阈值分别为31.01、22.64和13.74 m³/s²。模型参数β根据区域建筑损伤数据进行实证估计，提高了不同灾害强度脆弱性曲线的预测精度，比理论估计更真实地反映了建筑人群之间的变化。结果表明，随着楼层数的增加，建筑物的稳定性增加，破坏概率降低，例如，在中等破坏条件下，两层建筑的破坏概率比单层建筑低5%。在80 m³/s²以下，中国北方填充框架建筑（C3L）的破坏概率比美国忽略填充墙的裸框架建筑（C1L）高30%；在此之上，灾害强度占主导地位，概率趋于收敛。较新的建筑显示15%的低伤害概率，反映了改善的弹性。提出的脆弱性曲线可作为基于物理的风险评估概率工具，支持中国北方山区的泥石流灾害预防和缓解。

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引用次数: 0