Reliability Engineering & System Safety最新文献_第3页

Bayesian Optimized Deep Ensemble for Uncertainty Quantification of Deep Neural Networks: a System Safety Case Study on Sodium Fast Reactor Thermal Stratification Modeling 深度神经网络不确定性量化的贝叶斯优化深度集成：钠快堆热分层建模的系统安全案例研究

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2025-06-21 DOI: 10.1016/j.ress.2025.111353

Zaid Abulawi , Rui Hu , Prasanna Balaprakash , Yang Liu

{"title":"Bayesian Optimized Deep Ensemble for Uncertainty Quantification of Deep Neural Networks: a System Safety Case Study on Sodium Fast Reactor Thermal Stratification Modeling","authors":"Zaid Abulawi , Rui Hu , Prasanna Balaprakash , Yang Liu","doi":"10.1016/j.ress.2025.111353","DOIUrl":"10.1016/j.ress.2025.111353","url":null,"abstract":"<div><div>Deep neural networks (DNNs) are increasingly important to scientific computing and engineering system simulations. Accurate uncertainty quantification (UQ) for DNNs is critical in safety-sensitive engineering domains. Traditional Deep Ensemble (DE) methods, while easy to implement, frequently suffer from poorly calibrated uncertainty estimates and limited predictive accuracy due to reliance on fixed architectures with varied weight initializations. To address these issues, we introduce a workflow that combines Bayesian Optimization (BO) and DE. The workflow is modular, scalable, and integrates parallel BO initialized with Sobol sequences to individually optimize the hyperparameters of each ensemble member. This method enhances ensemble diversity, improves predictive accuracy, and provides reliable uncertainty estimates.</div><div>We evaluate the proposed BODE approach in a sodium fast reactor thermal stratification modeling case study, where we used a densely connected convolutional neural network to predict turbulent viscosity during the reactor transient with consideration of data noise. We benchmark its performance against several optimization approaches, including baseline deep ensemble, evolutionary algorithm-optimized ensemble, ensemble formed via random search combined with greedy selection, and a BO ensemble using random initialization. Our results demonstrate superior performance of the developed BODE approach. In noise-free scenarios, BODE notably reduces incorrect aleatoric uncertainty and significantly enhances predictive accuracy. Under conditions of 5% and 10% Gaussian noise, BODE adaptively quantifies uncertainty proportional to data noise, achieving up to an 80% reduction in root mean square error compared to baseline methods and producing well-calibrated prediction intervals.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"264 ","pages":"Article 111353"},"PeriodicalIF":9.4,"publicationDate":"2025-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144339016","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A constraint importance measure-based beluga whale optimization algorithm for reliability redundancy allocation problems considering mixed redundancy strategy 基于约束重要性测度的白鲸优化算法用于考虑混合冗余策略的可靠性冗余分配问题

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2025-06-20 DOI: 10.1016/j.ress.2025.111382

Shuai Zhang , Huiqi Du , Zhiqiang Cai , Shubin Si , Jiangbin Zhao

{"title":"A constraint importance measure-based beluga whale optimization algorithm for reliability redundancy allocation problems considering mixed redundancy strategy","authors":"Shuai Zhang , Huiqi Du , Zhiqiang Cai , Shubin Si , Jiangbin Zhao","doi":"10.1016/j.ress.2025.111382","DOIUrl":"10.1016/j.ress.2025.111382","url":null,"abstract":"<div><div>The reliability redundancy allocation problem (RRAP) aims to maximize system reliability by determining the optimal combination of component reliability and subsystem redundancy. RRAP plays an important role in the reliability improvement of complex systems, and it is necessary to determine the cost-effective RRAP solution effectively for complex systems. This paper constructs the constraint importance measure (CIM) to identify the key subsystems by considering the relationship between objective function and constraints. A CIM-based local search rule is designed to improve the system reliability by making the best use of limited resources. CIM-based beluga whale optimization (CIMBWO) is developed by combining the advantages of CIM and BWO. In numerical experiments, compared with GA, PSO, and BWO, the performance of CIMBWO is verified by two benchmarks and 11 different scale hybrid systems. The results show that CIMBWO can get better solutions with fast convergence and high robustness.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"264 ","pages":"Article 111382"},"PeriodicalIF":9.4,"publicationDate":"2025-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144502666","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Response to supply chain network disruption risk through link addition: Resilience enhancement strategies based on ternary closure motifs 通过添加链接响应供应链网络中断风险：基于三元闭合动机的弹性增强策略

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2025-06-20 DOI: 10.1016/j.ress.2025.111381

Xuguang Wei, Xinlan Cai, Yaxin Tian, Lingyun Guo

{"title":"Response to supply chain network disruption risk through link addition: Resilience enhancement strategies based on ternary closure motifs","authors":"Xuguang Wei, Xinlan Cai, Yaxin Tian, Lingyun Guo","doi":"10.1016/j.ress.2025.111381","DOIUrl":"10.1016/j.ress.2025.111381","url":null,"abstract":"<div><div>In the context of global economic turbulence and unstable supply, supply chain networks (SCNs) are more susceptible to risks that may cause SCNs operation disruption, leading to a broader range of economic losses. How to effectively improve supply chain network resilience (SCNR) to cope with the disruption risks is an urgent problem that scholars and managers need to solve. This paper comprehensively measures SCNR from two aspects, i.e. network connectivity and network communicability, and proposes link addition strategies based on the stability of ternary closure motif structure to enhance SCNR. Meanwhile, considering the community attributes of links, the community-based ternary closure motif link addition strategies are further designed to provide more matching recommendations for SCNs. The results show that: 1) the proposed low degree ternary closure motif link addition strategy (LDM) can more effectively enhance SCNR, compared to the current link addition strategies; 2) the connectivity enhancement effect of community-based link addition strategies on SCNs depends on network size; and 3) for network communicability, intra and inter low degree community ternary closure motif link addition strategy (IAELDM) is more effective. Finally, a case study of Build Your Dreams (BYD) automotive supply chain network further validates the effectiveness of the proposed strategies.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"264 ","pages":"Article 111381"},"PeriodicalIF":9.4,"publicationDate":"2025-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144502665","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A systematic review of engineering resilience: challenges and opportunities in ocean engineering 工程弹性的系统回顾：海洋工程中的挑战与机遇

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2025-06-20 DOI: 10.1016/j.ress.2025.111384

Chenyushu Wang , Baoping Cai , Yiliu Liu , Yixin Zhao , Yanping Zhang , Zhaoyi Pan

{"title":"A systematic review of engineering resilience: challenges and opportunities in ocean engineering","authors":"Chenyushu Wang , Baoping Cai , Yiliu Liu , Yixin Zhao , Yanping Zhang , Zhaoyi Pan","doi":"10.1016/j.ress.2025.111384","DOIUrl":"10.1016/j.ress.2025.111384","url":null,"abstract":"<div><div>Engineering resilience is used to measure the ability of a system to absorb or resist damages and quickly restore its original function after destructive events occur. The increasing probability of unavoidable destructive events, and the serious consequences of damages due to system complexity, have led to the widespread application and development of engineering resilience in the past decade. This article reviews the progress of engineering resilience from 2014 to the present, proposing a general framework for developing resilience metrics. Special attention is paid to the five commonly used resilience modeling methods in resilience evaluation. The application scenarios, advantages, and disadvantages are investigated. Ocean engineering is highly susceptible to complex subsea environments, harsh working conditions, and cascading failures. Destructive events can lead to decrease in oil and gas production, or serious consequences of casualties, property damage, and ocean environmental pollution. The challenges and research directions on resilience in ocean engineering are presented, focusing on the resilience metric of the entire lifecycle, resilience evaluation of giant systems under random multiple risks, resilience maintenance driven by economic capability, resilience modeling with artificial intelligence, and resilience verification with digital twin technology.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"264 ","pages":"Article 111384"},"PeriodicalIF":9.4,"publicationDate":"2025-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144502745","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

DCML-CSAR: A deep cascaded framework with dual-coupled memory learning and orthogonal feature extraction via recursive parameter transfer for SOH-RUL assessment DCML-CSAR：基于递归参数传递的双耦合记忆学习和正交特征提取的深度级联框架

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2025-06-20 DOI: 10.1016/j.ress.2025.111380

Mengdan Wu , Shunkun Yang , Daoyi Li , Lei Liu , Chong Bian

{"title":"DCML-CSAR: A deep cascaded framework with dual-coupled memory learning and orthogonal feature extraction via recursive parameter transfer for SOH-RUL assessment","authors":"Mengdan Wu , Shunkun Yang , Daoyi Li , Lei Liu , Chong Bian","doi":"10.1016/j.ress.2025.111380","DOIUrl":"10.1016/j.ress.2025.111380","url":null,"abstract":"<div><div>Accurate state of health (SOH) and remaining useful life (RUL) predictions are essential for battery health assessment, early fault detection, and ensuring system safety. However, existing methods struggle to effectively capture multiscale spatiotemporal characteristics, recognize intricate degradation patterns, and achieve synergy between SOH and RUL tasks due to independent architectures and limited information inheritance. To address these challenges, we propose a novel cascaded SOH-RUL assessment framework that integrates recursive hyperparameter transfer to enable deep coupling between SOH and RUL predictions. The framework employs a Triple-Orthogonal-Plane CNN to map battery data onto three orthogonal hyperplanes, extracting and fusing temporal-spatial features via an attention-based adaptive weighting mechanism. Additionally, a Dual-Coupled Memory-Learning LSTM with a novel gating interaction mechanism enhances temporal feature modeling by coupling forget and input gates and introducing peephole connections. Extensive experiments on multiple datasets, including NASA, Oxford, and CALCE, under diverse degradation scenarios, demonstrate significant improvements in prediction accuracy, robustness, and generalization. This framework offers a promising solution for advancing battery health management and system reliability.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"264 ","pages":"Article 111380"},"PeriodicalIF":9.4,"publicationDate":"2025-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144472280","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multi-source-data-driven microgrids reliability analysis via power supply chain using deep learning 基于深度学习的电力供应链多源数据驱动微电网可靠性分析

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2025-06-19 DOI: 10.1016/j.ress.2025.111376

Yulu Zhang , Zhiwei Chen , Xinghui Dong , Hongyan Dui , Min Chang , Junqiang Bai

{"title":"Multi-source-data-driven microgrids reliability analysis via power supply chain using deep learning","authors":"Yulu Zhang , Zhiwei Chen , Xinghui Dong , Hongyan Dui , Min Chang , Junqiang Bai","doi":"10.1016/j.ress.2025.111376","DOIUrl":"10.1016/j.ress.2025.111376","url":null,"abstract":"<div><div>Microgrid reliability is the ability to maintain a stable energy supply in a variable environment. However, such an environment (wind direction, temperature, humidity, pressure, and wind speed) renders the power supply with randomness, intermittency, and volatility. To ensure power stability in variable environments, a data-driven microgrid (DDMG) reliability analysis method is proposed based on the power supply chain (PSC) model, which fully considers the data-dependent output power. Firstly, a convolutional neural network-support vector machine (CNN-SVM) model is developed to effectively fuse multi-source data features. Secondly, a temporal convolutional network-bidirectional gated recurrent unit (TCN-BiGRU) approach is introduced to capture temporal dependencies to predict equipment states. The above two deep learning models provide accurate input values for reliability assessment. Then, a reliability assessment model is established based on the PSC model, complemented by an importance-measure-based reliability improvement strategy. Finally, the feasibility of methodology is validated with a case. The results show that compared with the traditional methods, the classification accuracy of CNN-SVM is up to 98.9747 %, the R<sup>2</sup> of TCN-BiGRU is up to 96.1962 %, and the recovered ranking based on the importance measure markedly and stably improves the reliability, which would guide microgrid reliability design.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"264 ","pages":"Article 111376"},"PeriodicalIF":9.4,"publicationDate":"2025-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144472277","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Frequency-domain analysis and dynamic reliability assessment of random vibration for non-classically damped linear structure under non-Gaussian random excitations 非高斯随机激励下非经典阻尼线性结构随机振动频域分析及动力可靠性评估

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2025-06-19 DOI: 10.1016/j.ress.2025.111363

Xiangqian Sheng , Kuahai Yu , Wenliang Fan , Shihong Xin

{"title":"Frequency-domain analysis and dynamic reliability assessment of random vibration for non-classically damped linear structure under non-Gaussian random excitations","authors":"Xiangqian Sheng , Kuahai Yu , Wenliang Fan , Shihong Xin","doi":"10.1016/j.ress.2025.111363","DOIUrl":"10.1016/j.ress.2025.111363","url":null,"abstract":"<div><div>Frequency domain analysis is the important component in the random vibration analysis. However, frequency domain analysis for the non-classically damped linear structure under non-Gaussian random excitations remains a challenge. Thus, this paper establishes a unified computational framework of higher-order moment spectra of response, and performs reliability assessment based on moment spectra of response. Firstly, the theoretical expressions of the higher-order moment spectra of response are deduced by the complex mode superposition method and the generalized impulse response function. Secondly, the expressions of the higher-order moment spectra of response are reconstructed with the help of responses for the harmonic excitation. Subsequently, the dynamic reliability is estimated based on the approximation joint probability density function which is constructed through the unified Hermite polynomial model and Gaussian Copula function. Finally, two numerical examples are investigated to verify the accuracy and efficiency of the calculation method of response the higher-order moment spectra and the dynamic reliability.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"264 ","pages":"Article 111363"},"PeriodicalIF":9.4,"publicationDate":"2025-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144313179","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Measured displacement data-driven efficient interpretation and real-time risk assessment method for the service performance of arch dams with cracks 实测位移数据驱动的裂隙拱坝使用性能高效解释与实时风险评估方法

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2025-06-18 DOI: 10.1016/j.ress.2025.111377

Bo Xu , Zeyuan Chen , Huaizhi Su , Hu Zhang , Pengcheng Xu , Shaowei Wang

{"title":"Measured displacement data-driven efficient interpretation and real-time risk assessment method for the service performance of arch dams with cracks","authors":"Bo Xu , Zeyuan Chen , Huaizhi Su , Hu Zhang , Pengcheng Xu , Shaowei Wang","doi":"10.1016/j.ress.2025.111377","DOIUrl":"10.1016/j.ress.2025.111377","url":null,"abstract":"<div><div>Current methods for predicting arch dam displacement rarely consider the impact of cracks on displacement and the interpretability of factors, nor do they reasonably assess the risk probability of arch dam. To address these issues, first, clustering partitions are conducted based on the Ward criterion, and the comprehensive displacement is obtained through the Criteria Importance Through Intercriteria Correlation (CRITIC) method. Secondly, considering the impact of cracks, a displacement monitoring model HSCT is constructed, and feature selection for the HSCT model factors is performed using the Max-Relevance and Min-Redundancy (mRMR), while Kernel Principal Component Analysis (KPCA) is utilized for feature extraction of crack factors. Furthermore, to enhance interpretability, an attention mechanism is incorporated into the Convolutional Neural Network with Long Short-Term Memory (CNN-LSTM) model, establishing a CNN-LSTM-Attention model to predict comprehensive displacement and visualize the importance of influencing factors. Finally, Kernel Density Estimation (KDE) is applied to the residuals of the comprehensive displacement, and a multivariate Copula function is used to construct the joint distribution to calculate the overall risk rate. The results indicate that the proposed methods and models are reasonable and feasible, providing scientific basis and technical support for the health diagnosis of hydraulic structures.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"264 ","pages":"Article 111377"},"PeriodicalIF":9.4,"publicationDate":"2025-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144338984","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A novel hybrid deep learning methodology for real-time wellhead pressure forecasting and risk warning during shale gas hydraulic fracturing 一种用于页岩气水力压裂井口压力实时预测和风险预警的新型混合深度学习方法

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2025-06-18 DOI: 10.1016/j.ress.2025.111309

Liangjie Gou , Zhaozhong Yang , Chao Min , Duo Yi , Liangping Yi , Xiaogang Li

{"title":"A novel hybrid deep learning methodology for real-time wellhead pressure forecasting and risk warning during shale gas hydraulic fracturing","authors":"Liangjie Gou , Zhaozhong Yang , Chao Min , Duo Yi , Liangping Yi , Xiaogang Li","doi":"10.1016/j.ress.2025.111309","DOIUrl":"10.1016/j.ress.2025.111309","url":null,"abstract":"<div><div>Accurate real-time forecasting of wellhead pressure significantly impacts risk warning and optimization of fracturing parameters. However, the complexity and non-stationary of data limit the accuracy of traditional deep learning (DL). We propose a novel hybrid DL method to enhance risk warning capabilities. The proposed method integrates the complex forecasting process into four modules. Firstly, the VMD-Fuzzy entropy module classifies intrinsic mode functions (IMFs) obtained from variational mode decomposition to significantly reduce feature redundancy. Then the Attention-GNN automatically learns latent features between multiple variables to automatically update the graph structure and incorporate controllable future input features. Additionally, the temporal–spatial feature extraction module captures spatial and temporal correlations to improve accuracy. The uncertainty quantification module employs a backtrack loss function and multi-head attention to enhance the capturing capability for critical data features. The method is verified using fracturing data from a shale gas block in Sichuan, China. The average root mean square error (RMSE), average maximum allowable error (MAE) and average R<span><math><msup><mrow></mrow><mrow><mn>2</mn></mrow></msup></math></span> of the target area are 1.31 (MPa), 1.27 (MPa) and 0.94, respectively, which are significantly better than the traditional DL. In addition, the data of 4 overpressure well stages were used for example verification, and the corresponding traffic light risk warning system was developed. The verification results prove that the proposed method can effectively improve the warning timeliness, and provide an effective technical way to achieve intelligent and efficient hydraulic fracturing.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"264 ","pages":"Article 111309"},"PeriodicalIF":9.4,"publicationDate":"2025-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144329535","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Dynamic vulnerability analysis of multi-modal public transport network using generalized travel costs from a multi-layer perspective 基于多层次广义出行成本的多式联运网络动态脆弱性分析

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2025-06-18 DOI: 10.1016/j.ress.2025.111375

Ziqi Wang , Yulong Pei , Jianhua Zhang , Zhixiang Gao

{"title":"Dynamic vulnerability analysis of multi-modal public transport network using generalized travel costs from a multi-layer perspective","authors":"Ziqi Wang , Yulong Pei , Jianhua Zhang , Zhixiang Gao","doi":"10.1016/j.ress.2025.111375","DOIUrl":"10.1016/j.ress.2025.111375","url":null,"abstract":"<div><div>The proper functioning of urban multi-modal public transport networks (MPTNs) is essential for sustainable urban development. However, as these networks become increasingly complex, their dynamic vulnerability to disturbances also rises. This study proposes a cascading failure model based on localized dynamic flow redistribution, aimed at mitigating and controlling the dynamic vulnerability of MPTNs. Firstly, we construct a multi-layered MPTN weighted by both generalized cost and traffic flow attributes, considering the heterogeneity and interdependence among various routes and modes. Building on this structure, we develop a localized user equilibrium traffic redistribution model that accounts for passenger congestion effects, enabling the analysis of dynamic vulnerability from both structural and functional perspectives. The proposed methodology is applied to a case study of the MPTN in Harbin. Simulation results reveal that the propagation of cascading failures in MPTNs is strongly associated with the geographical locations and importance of stations. Increasing station capacity effectively reduces the scale of cascading failure propagation, thereby alleviating network vulnerability. Moreover, dynamic vulnerability analysis shows that network connectivity and generalized travel efficiency deteriorate nonlinearly over time. Failures at critical stations disproportionately accelerate the dynamic vulnerability evolution, leading to nonlinear and compound degradation of network performance, including connectivity loss, increased travel costs, and service efficiency deterioration. This study provides valuable insights for enhancing the resilience of MPTNs, particularly in complex urban environments.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"264 ","pages":"Article 111375"},"PeriodicalIF":9.4,"publicationDate":"2025-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144339017","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0