Reliability Engineering & System Safety最新文献_第6页

Exploring Crowd Behavior during Emergency Evacuations in Subway Incidents 地铁事故紧急疏散人群行为研究

IF 11 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

Reliability Engineering & System Safety

Pub Date : 2026-01-15 DOI: 10.1016/j.ress.2026.112235

Yan Mao , Xuan Wang , Wu He , Gaofeng Pan

Crowded public spaces often present significant safety risks, as pedestrian emotions and behaviors suffered significant changes following emergency incidents. To examine the relevant impacts, this study establishes a large-scale subway simulation platform, modeling panic events, collision scenarios, and stampede incidents to assess the impact of emotional factors, luggage characteristics, crowd density, initial infection proportions, and personality traits on emergency evacuations. The results indicate that emotions must exceed a certain threshold to be triggered, and individuals with different personality traits exhibit varying sensitivities to emotional contagion, with openness (O-type) personalities being the most susceptible. Crowd density was identified as the primary factor determining evacuation efficiency, with high-density conditions significantly increasing evacuation times and exacerbating panic levels. In collision scenarios, pedestrians typically follow curved paths to avoid contact. Notably, increasing luggage weight alone tends to reduce overall evacuation time, whereas larger luggage size at a constant luggage weight prolong evacuation and increase stampede risk. This indicates that luggage size has a greater impact on evacuation outcomes than weight alone. The highest proportions of initial infectors generally raised panic, and this effect was accentuated under high-density conditions. Interestingly, higher initial infector proportions maintain some mitigating influence on panic, but their effectiveness diminishes when crowd density and physical encumbrance reach critical levels. Lastly, emergency evacuation efficiency is effectively improved by incorporating environmental familiarity into the leader-follower model. By illustrating how these factors interact to influence panic spread, collision avoidance, and stampede risk, our study offers a practical foundation for transit authorities to design evacuation drills, allocate resources, and optimize evacuation protocols, thereby enhancing safety and resilience in large-scale urban transit systems.

拥挤的公共空间往往存在重大的安全风险，因为行人的情绪和行为在紧急事件发生后会发生重大变化。为检验其影响，本研究建立了大型地铁仿真平台，模拟恐慌事件、碰撞场景和踩踏事件，评估情绪因素、行李特征、人群密度、初始感染比例和人格特征对应急疏散的影响。结果表明，情绪必须超过一定的阈值才能被触发，不同人格特质的个体对情绪感染的敏感性不同，其中开放性（o型）人格最容易受到感染。人群密度是决定疏散效率的主要因素，高密度的条件显著增加了疏散时间，加剧了恐慌程度。在碰撞场景中，行人通常沿着弯曲的路径行走，以避免接触。值得注意的是，单独增加行李重量往往会缩短总体疏散时间，而在行李重量不变的情况下，更大的行李尺寸会延长疏散时间，增加踩踏风险。这表明行李尺寸比重量本身对疏散结果的影响更大。初始感染者的最高比例通常引起恐慌，这种影响在高密度条件下更加突出。有趣的是，较高的初始感染者比例对恐慌有一定的缓解作用，但当人群密度和身体负担达到临界水平时，其效果就会减弱。最后，将环境熟悉度纳入领导-追随者模型，有效提高了应急疏散效率。通过说明这些因素如何相互作用影响恐慌蔓延、碰撞避免和踩踏风险，我们的研究为交通当局设计疏散演习、分配资源和优化疏散方案提供了实践基础，从而提高了大规模城市交通系统的安全性和弹性。

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引用次数: 0

A novel flexible interval maintenance strategy for multi-unit heterogeneous degrading systems under uncertainty 不确定条件下多单元异构退化系统的柔性间歇维护策略

IF 11 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

Reliability Engineering & System Safety

Pub Date : 2026-01-14 DOI: 10.1016/j.ress.2026.112227

Anqi Shan , Zengqiang Jiang , Qi Li , Hanxiao Zhang , Joaquim Blesa

Coordinated maintenance strategy optimization for multi-unit heterogeneous degrading systems under uncertainty is significantly challenging. Unlike conventional fixed-interval approaches, this study proposes a flexible interval maintenance (FIM) strategy that pursues a unified system-level maintenance policy while allowing flexible execution timing based on remaining useful life information. A two-stage stochastic programming model is developed to support the implementation of the FIM strategy, and the conditional value-at-risk (CVaR) method is incorporated to reflect risk-averse decision preferences. The first stage determines the bounds of the flexible maintenance interval, while the second stage optimizes maintenance timing under various degradation scenarios. A case study on railway freight wagon wheel overhauls demonstrates that the FIM strategy outperforms fixed-interval policies in reducing both cost and the number of threshold overruns. Sensitivity analyses examine the influence of uncertainty and risk preferences on the resulting maintenance strategies. The proposed approach provides a structured and flexible framework for system-level maintenance planning under degradation uncertainty.

不确定条件下多单元异构退化系统的协调维护策略优化具有重要的挑战性。与传统的固定时间间隔方法不同，本研究提出了一种灵活的时间间隔维护（FIM）策略，该策略追求统一的系统级维护策略，同时允许基于剩余使用寿命信息的灵活执行时间。建立了一个两阶段随机规划模型来支持FIM策略的实施，并采用条件风险值（CVaR）方法来反映风险规避决策偏好。第一阶段确定灵活维护间隔的边界，第二阶段优化各种退化情景下的维护时间。铁路货车车轮大修的实例研究表明，FIM策略在降低成本和阈值超限次数方面都优于固定间隔策略。敏感性分析考察了不确定性和风险偏好对最终维护策略的影响。该方法为退化不确定性下的系统级维护规划提供了一个结构化的、灵活的框架。

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引用次数: 0

A novel multi-task causal representation learning approach for interpretable maritime collision severity prediction 一种新的多任务因果表示学习方法用于可解释的海上碰撞严重程度预测

IF 11 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

Reliability Engineering & System Safety

Pub Date : 2026-01-14 DOI: 10.1016/j.ress.2026.112236

Miaomiao Wang , Yanfu Wang , Zhicheng Ma , Jin Wang

Accurate prediction and robust interpretation of maritime collision severity are crucial. Prevailing correlation-based methods are non-robust and lack interpretability, struggling with confounding factors, data heterogeneity, and class imbalance. A novel multi-task causal representation learning framework (MCLF) is proposed to address these limitations. Its core is a structured disentanglement mechanism that decomposes effects into direct effects and indirect effects mediated by unsafe factors, reinforced by adversarial training and orthogonality constraints to reduce representation-level confounding induced by observed covariates, thereby improving robustness and interpretability. To address class imbalance, an interactive data synthesis module using the tabular denoising diffusion probabilistic model (TabDDPM) is used, which generates high-quality samples for hard-to-classify cases to enhance model robustness. A dynamic multi-task fusion strategy then adaptively integrates the primary severity prediction with auxiliary tasks (pollution, property loss, and death). This holistic approach achieves superior predictive accuracy and enhances interpretability by providing a structurally-grounded decomposition of effects, advancing towards more transparent decision-support in maritime safety.

海上碰撞严重程度的准确预测和可靠解释至关重要。目前流行的基于相关性的方法不稳健，缺乏可解释性，与混杂因素、数据异质性和类不平衡作斗争。提出了一种新的多任务因果表示学习框架（MCLF）来解决这些限制。其核心是结构化解纠缠机制，该机制将效应分解为不安全因素介导的直接效应和间接效应，并通过对抗性训练和正交性约束进行强化，以减少观测协变量引起的表征水平混淆，从而提高鲁棒性和可解释性。为了解决分类不平衡问题，采用了基于表格去噪扩散概率模型（TabDDPM）的交互式数据综合模块，该模块为难以分类的案例生成高质量的样本，增强了模型的鲁棒性。动态多任务融合策略自适应地将主要严重程度预测与辅助任务（污染、财产损失和死亡）集成在一起。这种整体方法实现了卓越的预测准确性，并通过提供基于结构的影响分解来增强可解释性，从而向更透明的海上安全决策支持迈进。

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引用次数: 0

Probabilistic assessment of dynamic urban evacuation-sheltering functionality under typhoons based on interdependent road-shelter network 基于相互依存道路-遮蔽网络的台风下城市动态疏散-遮蔽功能概率评估

IF 11 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

Reliability Engineering & System Safety

Pub Date : 2026-01-14 DOI: 10.1016/j.ress.2026.112240

Lu Zhang , Shuang Tan , Bo Chen , Qingshan Yang

The functionality of the urban evacuation-sheltering system (UESS) during typhoons depends on the interdependent operational states of both the road network and shelters. Temporal variations in physical damage and traffic demand cause dynamic degradation in UESS functionality. Existing accessibility metrics are limited in capturing this dynamic behavior due to incomplete functional quantification, limited generalizability, lack of standardized outputs, and numerical instability under time-varying traffic demand. To address these issues, this study proposes a holistic functionality metric that captures temporal variations in evacuation timeliness and shelter availability, accompanied by a probabilistic assessment framework for UESS. A case study in Futian District, Shenzhen, reveals a distinct three-stage evolution of UESS functionality during typhoons: an initial sharp decline, a phase of slight recovery and stabilization, and a secondary decline followed by stabilization. The duration and magnitude of UESS functionality changes at each stage vary markedly across typhoon scenarios, with multisource uncertainties further exacerbate spatial disparities in UESS functionality among urban zones. The proposed metric and framework enhance the characterization and uncertainty quantification of dynamic UESS functionality, providing valuable insights for identifying urban areas with weak UESS performance, optimizing evacuation strategies, and strengthening urban resilience to typhoons.

台风期间城市疏散-庇护系统（UESS）的功能取决于道路网络和庇护所的相互依赖的运行状态。物理损坏和交通需求的时间变化导致UESS功能的动态退化。由于功能量化不完整、泛化能力有限、缺乏标准化输出以及时变交通需求下的数值不稳定，现有的可达性指标在捕捉这种动态行为方面受到限制。为了解决这些问题，本研究提出了一个整体的功能指标，该指标可以捕捉疏散及时性和庇护所可用性的时间变化，并伴随着UESS的概率评估框架。以深圳福田区为例，揭示了台风期间UESS功能的明显三个阶段的演变：最初的急剧下降，一个轻微的恢复和稳定阶段，以及第二个下降之后稳定的阶段。不同台风情景下各阶段UESS功能变化的持续时间和幅度存在显著差异，多源不确定性进一步加剧了城市区域间UESS功能的空间差异。所提出的度量和框架增强了动态UESS功能的表征和不确定性量化，为识别UESS性能较弱的城市地区、优化疏散策略和增强城市对台风的抵御能力提供了有价值的见解。

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引用次数: 0

Bound-constrained nonstationary Gaussian process regression for ventilated cavitation prediction 通风空化预测的约束非平稳高斯过程回归

IF 11 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

Reliability Engineering & System Safety

Pub Date : 2026-01-14 DOI: 10.1016/j.ress.2026.112222

Tian Bai , Kuangqi Chen , Dianpeng Wang

This research is motivated by the need to predict cavity length in ventilated cavitation experiments. Given the freestream velocity and ventilation rate, the underlying physical mechanism gives rise to informative bounds and distinct shedding regimes, which in turn induce nonstationarity in the system. In this paper, we propose a novel nonstationary bounded Gaussian process regression model that simultaneously incorporates nonstationarity and bound constraints. We adopt the Gaussian process projection framework to enforce the bound constraints and propose a mixture of bounded Gaussian processes to capture nonstationarity, where each component models a locally stationary behavior consistent with the constraints. The model parameters and mixture components are estimated through a two-stage procedure. Importantly, the proposed model reveals latent physical mechanisms by identifying distinct components, thereby offering deeper scientific insights into the input-output relationship. Numerical results across test functions and the ventilated cavitation experiments validate the superiority of the proposed method. Specifically, the proposed method effectively captures the evolution of ventilated cavity structures, thereby significantly enhancing the adaptability and operational reliability of high-speed underwater vehicles under complex environmental conditions. Codes are available on https://github.com/tbai114/Nonstationary-bounded-Gaussian-process.

本研究的动机是需要预测通风空化实验中的空腔长度。考虑到自由流速度和通风量，潜在的物理机制产生了信息边界和不同的脱落机制，这反过来又导致了系统的非平稳性。本文提出了一种同时包含非平稳约束和有界约束的非平稳有界高斯过程回归模型。我们采用高斯过程投影框架来加强有界约束，并提出了一个有界高斯过程的混合来捕获非平稳性，其中每个组件建模与约束一致的局部平稳行为。模型参数和混合成分通过两阶段估计。重要的是，提出的模型通过识别不同的组成部分揭示了潜在的物理机制，从而为投入产出关系提供了更深入的科学见解。跨测试函数的数值结果和通风空化实验验证了该方法的优越性。具体而言，该方法有效地捕捉了通气腔体结构的演化过程，从而显著提高了高速水下航行器在复杂环境条件下的适应性和运行可靠性。代码可在https://github.com/tbai114/Nonstationary-bounded-Gaussian-process上获得。

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引用次数: 0

A cost-reliability optimization framework for aging water distribution networks with partial demand surges considering time-dependent deterioration effect 考虑时变劣化效应的部分需求激增老化配水网络成本可靠性优化框架

IF 11 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

Reliability Engineering & System Safety

Pub Date : 2026-01-14 DOI: 10.1016/j.ress.2026.112239

Liangcheng Yu , Mingyuan Zhang , Haixing Liu , Yunhu Liu

The synergistic impacts of structural deterioration in aging urban water distribution networks (WDNs) and nonlinear demand surges in partial regions pose dual threats to water supply security. Current research inadequately addresses the dynamic time-dependent effects of aging pipelines on network operational reliability. This study proposes a cost-reliability optimization framework for aging WDNs under partial demand surge scenarios, integrating time-dependent reliability theory and evolutionary algorithms. The framework employs a Gaussian stochastic process-based corrosion power-law model to quantify time-dependent pipe failure probabilities, subsequently transformed into deterioration coefficients. These coefficients are systematically integrated with topological connectivity, junction hydraulic importance, and pipeline uniformity coefficients to formulate a novel Time-dependent Reliability Index (TRI). Comprehensively evaluate pipeline stress strength to establish deterioration pressure boundaries, ensuring safe operating conditions for aged WDNs. Within a Non-dominated Sorting Genetic Algorithm II (NSGA-II) architecture, Pareto frontiers are established for operational decisions on variable-speed pump groups, simultaneously minimizing energy cost and maximizing reliability. Implementing Anytown and a Chinese city's large-scale WDN reveals critical insights: TRI provides a more robust assessment than traditional reliability metrics in quantifying the long-term time-dependent system reliability of WDN, and preventive maintenance based on pressure thresholds effectively mitigates the progressive reliability degradation associated with pipeline deterioration. A more critical finding is that differences in WDN topology and scale cause partial demand surges to slightly enhance the reliability of large-scale aged WDNs, while small-scale WDNs exhibit weakened reliability. This framework offers a scientific WDN reliability enhancement pattern that delivers cost-effective and highly reliable operation solutions in aging WDNs.

老化的城市配水网络结构退化和部分地区的非线性需求激增的协同影响对供水安全构成双重威胁。目前的研究还没有充分解决老化管道对网络运行可靠性的动态时效影响。结合时变可靠性理论和进化算法，提出了部分需求激增情景下老化wdn的成本-可靠性优化框架。该框架采用基于高斯随机过程的腐蚀幂律模型来量化随时间变化的管道失效概率，然后将其转换为劣化系数。将这些系数与拓扑连通性、连接处水力重要性和管道均匀性系数进行系统集成，形成一种新的时间相关可靠性指数（TRI）。综合评价管道应力强度，建立劣化压力边界，确保老化水轮机的安全运行条件。在非支配排序遗传算法II （NSGA-II）架构中，为变速泵组的操作决策建立了帕累托边界，同时最小化能源成本和最大化可靠性。实施Anytown和中国城市的大规模WDN揭示了关键的见解：TRI在量化WDN长期依赖时间的系统可靠性方面提供了比传统可靠性指标更稳健的评估，基于压力阈值的预防性维护有效地减轻了与管道退化相关的渐进式可靠性退化。一个更重要的发现是，WDN拓扑和规模的差异会导致部分需求激增，从而略微提高大规模老化WDN的可靠性，而小规模WDN的可靠性则会减弱。该框架提供了一种科学的WDN可靠性增强模式，为老化的WDN提供了经济高效、高可靠性的运行解决方案。

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引用次数: 0

Multi-scale signal transformer with signal processing-based attention interpretation for fault diagnosis of rotating machinery under variable speed conditions 基于信号处理的多尺度信号变压器在旋转机械变速故障诊断中的应用

IF 11 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

Reliability Engineering & System Safety

Pub Date : 2026-01-14 DOI: 10.1016/j.ress.2026.112243

Sungjong Kim , Seungyun Lee , Jiwon Lee , Minjae Kim , Heonjun Yoon , Byeng D. Youn

Fault diagnosis of rotating machinery remains a critical challenge due to the need for precise localization of fault-related patterns in time-series signals, which are often influenced by variable speed conditions. Moreover, the limited interpretability of deep learning models restricts their deployment in practical industrial applications. To address these issues, this study proposes the Multi-Scale Signal Transformer (MSSiT), a novel deep learning architecture that adaptively captures multi-resolution features using a multi-scale self-attention mechanism. In addition, a Signal Processing-Based Attention Interpretation (SPAI) method is developed to enhance model interpretability by analyzing attention weight in both time and frequency domains through signal processing techniques.

The proposed framework is validated through two case studies involving various and time-varying speed conditions. Experimental results demonstrate that MSSiT outperforms existing fault diagnosis methods under variable speed conditions. The SPAI results reveal that multi-scale attention mechanism focuses on high-frequency components for fault-related features and low-frequency components for speed-dependent trends at each scale. Furthermore, interpretation results are quantitatively validated, confirming that attention weight reliably highlights physically meaningful features. These findings demonstrate the effectiveness and robustness of the proposed framework and its potential applicability in real-world fault diagnosis under complex operational conditions.

由于需要精确定位时间序列信号中与故障相关的模式，旋转机械的故障诊断仍然是一个关键的挑战，而时间序列信号通常受变速条件的影响。此外，深度学习模型有限的可解释性限制了它们在实际工业应用中的部署。为了解决这些问题，本研究提出了多尺度信号变压器（MSSiT），这是一种新型的深度学习架构，可以使用多尺度自注意机制自适应捕获多分辨率特征。此外，提出了一种基于信号处理的注意解释（SPAI）方法，通过信号处理技术在时域和频域分析注意权重，提高模型的可解释性。通过两个涉及各种时变速度条件的案例研究验证了所提出的框架。实验结果表明，该方法在变速条件下优于现有的故障诊断方法。SPAI结果表明，在每个尺度上，故障相关特征的多尺度注意机制主要集中在高频分量上，速度相关趋势的多尺度注意机制主要集中在低频分量上。此外，对解释结果进行了定量验证，证实了注意权重可靠地突出了物理上有意义的特征。这些结果证明了所提出的框架的有效性和鲁棒性，以及它在复杂操作条件下的实际故障诊断中的潜在适用性。

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引用次数: 0

Predictive risk analysis for leakage accidents with dynamic behaviour 动态泄漏事故的预测风险分析

IF 11 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

Reliability Engineering & System Safety

Pub Date : 2026-01-14 DOI: 10.1016/j.ress.2026.112238

Xiangyu Kong , Ruishu Huang , He Li , Jichuan Kang , Yan Dong , C. Guedes Soares , Jin Wang

A predictive risk analysis approach is proposed for modelling leakage accidents with dynamic behaviour based on time-series simulations in order to enhance data foundation of risk analysis tasks. Critical failure items are first identified by the Failure Mode and Effects Analysis model and the occurrence probabilities of which are subsequently computed by the Bayesian and Event Tree Analysis methods. The dynamic behaviour of the accidents is simulated to reveal their time-series failure consequences. With the probabilities and consequences obtained, a predictive risk analysis approach is established as a basis to calculate the risk index of accidents with the consideration of dynamic behaviours. The applicability and superior performance of the proposed approach are illustrated by a leakage risk analysis of offshore hydrogen storage systems. Overall, the proposed approach extends the existing inductive risk analysis concepts to predictive patterns and contributes to leakage accidents analysis and prevention with the situation of data and knowledge scarcities.

为了增强风险分析任务的数据基础，提出了一种基于时间序列模拟的具有动态行为的泄漏事故预测风险分析方法。关键故障项首先由失效模式和影响分析模型确定，然后由贝叶斯和事件树分析方法计算其发生概率。模拟了事故的动态行为，揭示了事故的时间序列破坏后果。在得到概率和后果的基础上，建立了一种考虑动态行为的预测风险分析方法，作为计算事故风险指数的基础。通过海上储氢系统的泄漏风险分析，说明了该方法的适用性和优越性。总体而言，该方法将现有的归纳风险分析概念扩展到预测模式，有助于在数据和知识匮乏的情况下进行泄漏事故分析和预防。

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引用次数: 0

A real-time reliability assessment framework for marine mechanical equipment integrating machine learning and physical knowledge: Toward applications in maritime autonomous surface ships 集成机器学习和物理知识的船舶机械设备实时可靠性评估框架：面向海上自主水面舰艇的应用

IF 11 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

Reliability Engineering & System Safety

Pub Date : 2026-01-13 DOI: 10.1016/j.ress.2026.112233

Hongqiang Li , Xiangkun Meng , Wenjun Zhang , Xiang-Yu Zhou , Xue Yang

With the rapid development of maritime autonomous surface ships (MASS), the reliability of onboard mechanical equipment has become increasingly critical for safe and efficient operation. Motivated by these emerging requirements, this study proposes a real-time reliability assessment framework for marine mechanical equipment that integrates data-driven models with physical knowledge. By combining physical knowledge with a Wasserstein generative adversarial network (WGAN) to construct a synthetic dataset, the HI is predicted using principal component analysis and a long short-term memory network (PCA-LSTM) model, and the prediction results are optimized using Savitzky-Golay filtering. Finally, real-time reliability quantification is achieved based on the Weibull distribution and maximum likelihood estimation. The case study of a ship propulsion system demonstrates that this method can identify the accelerating trend of reliability reduction after approximately 400 h of operation, and the reliability remains at 99.36% until 720 h. The capability of ML to predict real-time reliability, combined with physical knowledge, reflects real-world conditions. The results provide real-time predictions of the health state and reliability of mechanical equipment, enabling early fault detection and suggesting the formulation of maintenance planning, thereby supporting the reliable operation of MASS.

随着海上自主水面舰艇（MASS）的快速发展，舰载机械设备的可靠性对安全高效运行变得越来越重要。在这些新兴需求的推动下，本研究提出了一种将数据驱动模型与物理知识相结合的船舶机械设备实时可靠性评估框架。将物理知识与Wasserstein生成对抗网络（WGAN）相结合，构建合成数据集，利用主成分分析和长短期记忆网络（PCA-LSTM）模型对HI进行预测，并利用Savitzky-Golay滤波对预测结果进行优化。最后，基于威布尔分布和极大似然估计实现实时可靠性量化。船舶推进系统的实例研究表明，该方法可以识别出运行约400 h后可靠性降低的加速趋势，并且可靠性保持在99.36%，直到720 h。机器学习预测实时可靠性的能力与物理知识相结合，反映了现实情况。研究结果提供了机械设备健康状态和可靠性的实时预测，可以早期发现故障并建议制定维修计划，从而支持MASS的可靠运行。

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引用次数: 0

Variability and probabilistic modeling of external blast loads on cylindrical shells 圆柱壳外爆炸载荷的变异性和概率模型

IF 11 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

Reliability Engineering & System Safety

Pub Date : 2026-01-13 DOI: 10.1016/j.ress.2026.112232

Feng Fan , Fei Yin , Xudong Zhi

Deterministic load models adopted by current blast-resistant design codes struggle to quantify the load variability arising from inherent uncertainties in the explosive source, propagation path, and the models themselves, thus hindering reliability-based blast design. This paper presents a systematic investigation into the variability of external blast loads on cylindrical shells through 20 repeated tests and tests with varying charge orientations, complemented by numerical simulations thoroughly validated against multi-physics experimental data. For the first time, the contributions of repeatability errors and charge orientation errors to the overall variability are decoupled and quantified, revealing that ignoring charge orientation increases the total load variability by over 120%. Statistical tests indicate that peak overpressure and maximum impulse follow a Normal distribution, while positive phase duration follows a Lognormal distribution. This study develops spatial distribution models for the coefficient of variation, showing that charge orientation deviation and structural obstruction effects amplify blast load variability in the near-field and far-field, respectively. Through extensive parametric numerical analyses, a deterministic load model considering charge size, distance, and orientation is established. Furthermore, a probabilistic load model guided by the 95% fractile value is proposed. This model can be directly integrated with the partial factor system of current structural reliability design codes, providing a practical tool for the probabilistic blast-resistant design and safety assessment of cylindrical shell structures.

现有防爆设计规范采用的确定性荷载模型难以量化爆炸源、传播路径和模型本身固有的不确定性所引起的荷载变异性，从而阻碍了基于可靠性的爆炸设计。本文通过20次重复试验和不同装药方向的试验，系统地研究了圆柱壳上外爆炸载荷的变异性，并辅以针对多物理场实验数据进行了彻底验证的数值模拟。该研究首次对可重复性误差和电荷取向误差对总体变异性的贡献进行了解耦和量化，表明忽略电荷取向使总负载变异性增加了120%以上。统计检验表明，峰值超压和最大冲量服从正态分布，正相持续时间服从对数正态分布。本研究建立了变异系数的空间分布模型，表明装药取向偏差和结构障碍效应分别放大了近场和远场爆炸载荷的变异。通过广泛的参数数值分析，建立了考虑电荷大小、距离和方向的确定性载荷模型。在此基础上，提出了以95%分形值为指导的概率荷载模型。该模型可直接与现行结构可靠性设计规范的部分因子体系相结合，为圆柱壳结构的概率抗爆设计和安全评价提供实用工具。

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引用次数: 0