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

Failure-prediction-activated Packet Rebroadcasting for Packet Loss Mitigation in Unmanned Aerial Vehicle Swarm Networks 基于故障预测激活的无人机群网络丢包重广播

IF 11 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

Reliability Engineering & System Safety

Pub Date : 2026-01-31 DOI: 10.1016/j.ress.2026.112331

Reuben Yaw Hui Lim , Joanne Mun-Yee Lim , Boon Leong Lan , Patrick Wan Chuan Ho , Nee Shen Ho , Thomas Wei Min Ooi

Communication reliability between a UAV swarm and the ground control station (GCS) is crucial for its safe deployment, but unforeseen interference can cause communication failure. There is a lack of proposals on the mitigation of such failure. Here, we propose packet-rebroadcasting by UAV (with packet-loss-count (PLC) based probability) and GCS to mitigate packet loss to maintain safety. This mitigation scheme is activated in waypoint control mode when failure is predicted using a modified throughput as proxy for reliability. The performance of our novel approach is evaluated for a representative UAVs-GCS network across UAV and MANET interference scenarios with low to high interference strengths. Our modified throughput, which correlates far better with reliability than throughput, achieved similar high mean specificity (∼1) for failure prediction, but much lower mean false negative rate overall with concomitant low failure risks. Our proposed mitigation scheme out-performed its variants (with different combinations of rebroadcasting strategy (hybrid/UAV-only) and probability (PLC/fixed/distance-based), and no rebroadcasting), achieving the lowest probability (∼2%) of three consecutive packet loss with the highest rebroadcast efficiency. Our approach enables UAV swarm deployment with a high-level of safety, even if strong interference is encountered.

无人机群与地面控制站（GCS）之间的通信可靠性对其安全部署至关重要，但不可预见的干扰可能导致通信失败。没有关于减轻这种失败的建议。在这里，我们提出了无人机（基于丢包计数（PLC）概率）和GCS的数据包重广播，以减少数据包丢失以保持安全。当使用修改后的吞吐量作为可靠性代理预测故障时，在航路点控制模式下激活此缓解方案。在具有代表性的无人机- gcs网络中，通过低到高干扰强度的无人机和MANET干扰场景，评估了我们的新方法的性能。我们改进的吞吐量与可靠性的相关性远远好于吞吐量，在故障预测方面实现了类似的高平均特异性（~ 1），但总体上的平均假阴性率要低得多，同时伴有低故障风险。我们提出的缓解方案优于其变体（具有转播策略（混合/仅无人机）和概率（PLC/固定/基于距离）的不同组合，并且不转播），以最高的转播效率实现了连续三次丢包的最低概率（~ 2%）。我们的方法使无人机群部署具有高水平的安全性，即使遇到强烈的干扰。

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引用次数: 0

Network recovery for UAV-assisted IoTs after cascading failures with heterogeneous graph neural networks 基于异构图神经网络的无人机辅助物联网级联故障网络恢复

IF 11 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

Reliability Engineering & System Safety

Pub Date : 2026-01-30 DOI: 10.1016/j.ress.2026.112320

Xiaodian Zhuang , Xiuwen Fu , Liudong Xing , Rui Peng

With the growing adoption of unmanned aerial vehicle (UAV)-assisted Internet of Things (IoT), its resilience against cascading failures has garnered significant attention. Cascading failures can severely compromise the topological integrity of such networks, making efficient recovery a significant challenge. To address this challenge, a Network Recovery scheme with Heterogeneous Graph neural network (NRHG) is proposed. The proposed scheme employs a Heterogeneous Graph Neural Network (HGNN), which includes graph perception layers processing local observations from individual UAVs, and graph communication layers enabling information exchange among UAVs. A multi-agent reinforcement learning (MARL) framework is further employed to enable collaborative action decisions for UAVs. Experimental results demonstrate that the proposed NRHG scheme can efficiently schedule surviving UAVs to cover the network blind spots caused by cascading failures. Compared to other schemes, the proposed scheme shows superior performance in both network coverage recovery and system throughput restoration.

随着无人机（UAV）辅助物联网（IoT）的日益普及，其抗级联故障的弹性已经引起了人们的广泛关注。级联故障会严重损害此类网络的拓扑完整性，使高效恢复成为一项重大挑战。针对这一挑战，提出了一种基于异构图神经网络（NRHG）的网络恢复方案。该方案采用异构图神经网络（HGNN），其中包括处理单个无人机局部观测的图感知层和实现无人机间信息交换的图通信层。进一步采用多智能体强化学习（MARL）框架实现无人机协同行动决策。实验结果表明，所提出的NRHG方案能够有效地调度幸存的无人机，覆盖由级联故障引起的网络盲点。与其他方案相比，该方案在网络覆盖恢复和系统吞吐量恢复方面都具有较好的性能。

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引用次数: 0

Empirical reliability enhancement in series elastic actuators using fractional-order finite-time robust control 基于分数阶有限时间鲁棒控制的串联弹性执行器可靠性经验增强

IF 11 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

Reliability Engineering & System Safety

Pub Date : 2026-01-30 DOI: 10.1016/j.ress.2026.112330

Seyed Ali Moafi , Ebrahim Abbaszadeh , Seyed Hossein Rouhani , Saleh Mobayen , Farid Najafi

This paper presents a novel fractional-order control strategy aimed at enhancing the reliability, safety, and efficiency of the series elastic actuators in robotic systems, with practical reliability approximated through empirical metrics such as failure probability and mean time to failure. The proposed method optimizes control signal constraints, thereby improving the performance and reliability of series elastic actuator-driven robots in assistive human-robot interaction tasks. Leveraging fractional calculus, the proposed approach provides a more accurate model of dynamic interactions between robots and flexible environments, addressing these complexities more precisely than traditional integer-order models. The proposed strategy accounts for controller effort saturation, offering robust finite-time convergence and ensuring superior resilience to disturbances and uncertainties, critical for mission success in real-world applications. The adaptive capability of the system in low- and high-stiffness environments further enhances its versatility and maintainability in diverse operational scenarios. Various experiments demonstrate the superior performance of the method compared to integer-order terminal sliding mode techniques, particularly in accurately modeling failure dynamics and enhancing system safety. The findings underscore the potential of fractional-order control to substantially improve the reliability of series elastic actuator-driven robots, advancing the development of safe, human-centric robotic systems for deployment in unpredictable and dynamic environments.

本文提出了一种新的分数阶控制策略，旨在提高机器人系统中串联弹性作动器的可靠性、安全性和效率，并通过故障概率和平均故障时间等经验度量来逼近实际可靠性。该方法优化了控制信号约束，从而提高了串联弹性作动器驱动机器人在辅助人机交互任务中的性能和可靠性。利用分数阶微积分，所提出的方法提供了一个更准确的机器人与灵活环境之间动态相互作用的模型，比传统的整阶模型更精确地解决了这些复杂性。所提出的策略考虑了控制器的努力饱和，提供了鲁棒的有限时间收敛性，并确保了对干扰和不确定性的卓越弹性，这对现实应用中的任务成功至关重要。系统在低刚度和高刚度环境下的自适应能力进一步增强了系统在不同作战场景下的通用性和可维护性。各种实验表明，与整阶终端滑模技术相比，该方法具有优越的性能，特别是在准确建模故障动力学和提高系统安全性方面。研究结果强调了分数阶控制的潜力，可以大大提高串联弹性驱动器驱动机器人的可靠性，促进在不可预测和动态环境中部署的安全，以人为中心的机器人系统的发展。

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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 : 2026-01-30 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

A novel integrated dependence assessment method for human reliability analysis under uncertainty and expert disagreement 一种新的不确定和专家分歧下的人的可靠性综合依赖评估方法

IF 11 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

Reliability Engineering & System Safety

Pub Date : 2026-01-30 DOI: 10.1016/j.ress.2026.112299

Fei Gao

Dependence assessment is a critical concern in human reliability analysis (HRA), particularly under uncertain and conflicting expert evaluations. This study operationalizes a structured integration of a consensus-reaching process (CRP), the full consistency method (FUCOM), and Dempster-Shafer evidence theory (DSET) to support dependence assessment and the calculation of conditional human error probability (CHEP) in a THERP-compatible manner. First, expert judgments on dependence levels are represented as basic belief assignments and reconciled through a fidelity-constrained CRP that reduces disagreement under an explicit agreement requirement while remaining close to the original assessments. Subsequently, FUCOM is used to derive internally consistent importance coefficients for dependence-influencing factors, reducing elicitation burden and mitigating incoherent factor prioritization. The consensus-refined belief structures and FUCOM-based coefficients are then aggregated using DSET and mapped to CHEP. A case study demonstrates the feasibility of the proposed scheme and shows that the resulting CHEP is numerically plausible and comparable to those obtained by representative dependence assessment methods, while providing a more transparent and traceable chain from elicited expert inputs to the final conditional probability. Sensitivity analyses further indicate that the results remain stable under reasonable variations of key consensus parameters and factor weights. The proposed framework offers a defensible process for panel-based dependence assessment in safety-critical applications where disagreement management and weighting consistency is required.

依赖性评估是人的可靠性分析（HRA）中的一个关键问题，特别是在不确定和相互冲突的专家评估下。本研究实施了共识达成过程（CRP）、完全一致性方法（FUCOM）和Dempster-Shafer证据理论（DSET）的结构化集成，以一种与therp兼容的方式支持依赖性评估和条件人为错误概率（CHEP）计算。首先，专家对依赖水平的判断被表示为基本信念分配，并通过保真度约束的CRP进行协调，该CRP在明确的协议要求下减少分歧，同时保持接近原始评估。随后，使用FUCOM推导出依赖影响因素的内部一致的重要系数，减少了推导负担，减轻了不一致的因素优先级。然后使用DSET将共识精炼的信念结构和基于fucom的系数聚合并映射到CHEP。一个案例研究证明了所提出方案的可行性，并表明所得到的CHEP在数值上是合理的，与代表性依赖评估方法获得的结果相当，同时提供了一个更透明和可追溯的链，从引出的专家输入到最终的条件概率。敏感性分析进一步表明，在关键共识参数和因子权重合理变化的情况下，结果保持稳定。提出的框架为安全关键应用中基于面板的依赖性评估提供了一个可辩护的过程，其中需要分歧管理和权重一致性。

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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 : 2026-01-30 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

A safety protection method based on trajectory prediction for the operation of virtual coupling trains 基于轨迹预测的虚拟联轴车运行安全保护方法

IF 11 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

Reliability Engineering & System Safety

Pub Date : 2026-01-30 DOI: 10.1016/j.ress.2026.112328

Ying Zhao , Haijun Li , Xiaobing Liu , Yan Huang

This study proposes a safety protection method based on trajectory prediction (SPTP) for the operation of virtual coupling trains. Specifically, a hybrid TLMA model that integrates Temporal Convolutional Networks (TCN), Long Short-Term Memory (LSTM), and Multi-head Self-attention (MATT) was developed to predict the trajectory of the leading train. Based on the prediction results, the SPTP method was introduced, grounded in principles such as space requirements for the following train’s operation, safety requirements when trains are stationary in the station platform, and operation safety requirements under different adverse conditions. Furthermore, a microscopic multi-state train-following model was constructed to validate the effectiveness of the SPTP method. The comparative results of the prediction model demonstrate that the TLMA model outperforms baseline models, achieving high accuracy and demonstrating excellent applicability for train trajectory prediction. Then, the SPTP method was compared with existing safety protection methods. Numerical simulation results showed that the SPTP method effectively reduced the distance interval between trains by 34.6 %, the speed difference between trains by 7.0 %, and the arrival time deviation by 65.0 %. These findings suggest that the SPTP method could effectively improve operation efficiency for urban rail trains and enhance passenger service quality.

提出了一种基于轨迹预测的虚拟联轴车运行安全保护方法。具体而言，建立了一种结合时间卷积网络（TCN）、长短期记忆（LSTM）和多头自注意（MATT）的混合TLMA模型来预测前导列车的运行轨迹。根据预测结果，从后续列车运行的空间要求、列车在站台静止时的安全要求、不同不利条件下的运行安全要求等原则出发，引入了SPTP方法。在此基础上，构建了微观多状态列车跟踪模型，验证了SPTP方法的有效性。预测模型的对比结果表明，TLMA模型优于基线模型，具有较高的预测精度，对列车轨迹预测具有良好的适用性。然后，将SPTP方法与现有的安全保护方法进行了比较。数值模拟结果表明，SPTP方法可有效地将列车间距缩短34.6%，列车间速度差缩短7.0%，到达时间偏差缩短65.0%。研究结果表明，SPTP方法可以有效提高城市轨道交通运行效率，提高客运服务质量。

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引用次数: 0

Multi-stage robust optimization of reliability improvement for power transmission cyber-physical systems under sequential coordinated attacks: an extreme attack scenario search-assisted framework 连续协同攻击下输电网络物理系统可靠性改进的多阶段鲁棒优化：一种极端攻击场景搜索辅助框架

IF 11 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

Reliability Engineering & System Safety

Pub Date : 2026-01-29 DOI: 10.1016/j.ress.2026.112319

Chuangzhi Li, Tianlei Zang, Buxiang Zhou

The increasingly deployed information components render the power transmission cyber-physical systems (PTCPS) more vulnerable to cyber-physical attacks (CPAs). Attackers often target critical devices in the communication network, disrupting their operation, with such attacks increasingly exhibiting sequential and coordinated patterns. To address this, we develop a multi-stage robust optimization framework with explicit restoration duration for PTCPS under sequential coordinated CPAs. The framework comprises a defender that hardens components, an attacker that orchestrates coordinated actions, and an operator that executes power redistribution and system restoration. Protection relay intrusions are modeled jointly with deliberate transmission-line outages, and a virtual fault flow representation captures fault propagation across lines, relays, and buses. A key ingredient is an extreme attack scenario search that identifies critical scenarios via objective-free optimization based on fault propagation. The discovered scenarios and constraints are embedded into the master problem, allowing faster convergence of the column-and-constraint generation algorithm and improving the overall efficiency of the multi-stage robust optimization. Case studies on the standard IEEE RTS-96 system demonstrate that the extreme scenario search framework can reduce the overall computational time by approximately 44.8%. Moreover, adding Euclidean distance selection achieves a total reduction of approximately 49.0% in computational time while ensuring stable convergence under coordinated CPAs.

越来越多的信息组件部署使得输电网络物理系统（PTCPS）更容易受到网络物理攻击。攻击者经常以通信网络中的关键设备为目标，破坏其运行，这种攻击越来越多地呈现出顺序和协调的模式。为了解决这个问题，我们开发了一个多阶段鲁棒优化框架，明确了顺序协调cpa下PTCPS的恢复时间。该框架包括一个加固组件的防御者、一个协调行动的攻击者和一个执行权力重新分配和系统恢复的操作员。保护继电器入侵与故意的输电在线中断联合建模，虚拟故障流表示捕获跨线路、继电器和总线的故障传播。一个关键因素是极端攻击场景搜索，通过基于故障传播的无目标优化来识别关键场景。将发现的场景和约束嵌入到主问题中，加快了列约束生成算法的收敛速度，提高了多阶段鲁棒优化的整体效率。在标准IEEE RTS-96系统上的实例研究表明，极端场景搜索框架可以将总体计算时间减少约44.8%。此外，加入欧几里得距离选择后，在保证协调cpa下稳定收敛的同时，计算时间减少了约49.0%。

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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 : 2026-01-29 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

Beyond waterlogging: Evaluating the impact of extreme rainfall on the road network 超越内涝：评估极端降雨对道路网络的影响

IF 11 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

Reliability Engineering & System Safety

Pub Date : 2026-01-29 DOI: 10.1016/j.ress.2026.112308

Jie Liu , Zizhen Xu , Li Wan , Kristen MacAskill

Existing research of extreme rainfall impact on transport networks primarily examines the effect of waterlogging. Although the other two main factors—reduced visibility and traffic-signal power outages—have been shown to significantly affect road operation, their contributions at the network scale remain underexplored. Taking a macroscopic approach, this study gauges the impacts of these three factors on the road network connectivity and efficiency during extreme rainfall through a case study of 26 Local Government Areas in and around Greater London. The result shows that focusing solely on waterlogging while disregarding reduced visibility and traffic signal power failures overestimates road capacities by 15–30% and underestimates network efficiency impacts by 1–23% under different rainfall scenarios. Particularly, the largest impact underestimation is observed for 1-in-30-year rainfall risk, where waterlogging is less dominant, while poor visibility considerably contributes to the impacts. The analysis also suggests that signal power failures during rainfall have limited, localised effects at the network level.

现有的极端降雨对交通网络影响的研究主要考察了内涝的影响。尽管其他两个主要因素——能见度降低和交通信号停电——已被证明会显著影响道路运行，但它们在网络规模上的作用仍未得到充分探讨。本研究采用宏观方法，通过对大伦敦及其周边26个地方政府区域的案例研究，衡量了这三个因素对极端降雨期间道路网络连通性和效率的影响。结果表明，在不同降雨情景下，仅关注内涝而忽视能见度降低和交通信号故障对道路通行能力的高估幅度为15-30%，对路网效率影响的低估幅度为1-23%。特别是，对30年一遇的降雨风险的影响低估最大，其中内涝不太占主导地位，而能见度低在很大程度上助长了影响。该分析还表明，降雨期间的信号电源故障在网络层面上具有有限的、局部的影响。

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引用次数: 0