Pub Date : 2026-09-01Epub Date: 2026-02-07DOI: 10.1016/j.ress.2026.112365
Mengzhu Chen , Chaonan Wang , Yujie Wang , Zhitao Wu
In phased-mission systems (PMSs) exposed to cascading deterministic common cause failures (CDCCFs), a common cause (CC) can result in multiple system components failing simultaneously, and these initial failures may subsequently result in additional components failing through a domino effect. This paper develops two implicit approaches utilizing multi-valued decision diagram for reliability analysis of PMSs affected by cascading effects with no-loop and Hamiltonian loop structures, respectively. Application of the developed approaches extends to arbitrary time-to-failure distributions of components, considering external CCs as well as internal CCs. The correctness of the proposed approaches is verified through Monte Carlo simulation, and their time and space complexities are analyzed as well. A demonstrative case study on a smart home system is carried out to illustrate the applicability and advantages of the approaches.
{"title":"Implicit methods for reliability analysis of phased-mission systems subject to cascading deterministic common cause failures","authors":"Mengzhu Chen , Chaonan Wang , Yujie Wang , Zhitao Wu","doi":"10.1016/j.ress.2026.112365","DOIUrl":"10.1016/j.ress.2026.112365","url":null,"abstract":"<div><div>In phased-mission systems (PMSs) exposed to cascading deterministic common cause failures (CDCCFs), a common cause (CC) can result in multiple system components failing simultaneously, and these initial failures may subsequently result in additional components failing through a domino effect. This paper develops two implicit approaches utilizing multi-valued decision diagram for reliability analysis of PMSs affected by cascading effects with no-loop and Hamiltonian loop structures, respectively. Application of the developed approaches extends to arbitrary time-to-failure distributions of components, considering external CCs as well as internal CCs. The correctness of the proposed approaches is verified through Monte Carlo simulation, and their time and space complexities are analyzed as well. A demonstrative case study on a smart home system is carried out to illustrate the applicability and advantages of the approaches.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"273 ","pages":"Article 112365"},"PeriodicalIF":11.0,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146175023","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}
Pub Date : 2026-09-01Epub Date: 2026-01-31DOI: 10.1016/j.ress.2026.112339
Xiaohong Chen , Daipeng Ma , Jian Guan
Against the backdrop of escalating geopolitical tensions, the global scrap nickel supply network (GSNSN) faces mounting challenges to its systemic reliability. This paper constructs an analytical framework integrating endogenous structural exposure with cascading failure simulations to assess the structural vulnerability mechanisms of the GSNSN. The results indicate that, from the perspective of endogenous structural exposure, the system exhibits significant characteristics of non-linear abrupt transitions, revealing the structural criticality of the network’s transition from a steady state to a collapse. Regarding external shocks, national import/export bans or disruptions in cooperation generally manifest into four risk propagation modes: long-range & large-scale, long-range & small-scale, short-range & large-scale, and short-range & small-scale. Specifically, high-coupling strategic corridors or nodes constitute the core of vulnerability due to rigid supply-demand dependencies (e.g., GBR→USA, DEU↔SWE, CHN, and USA), whereas nodes with high risk tolerance function as physical firewalls through a threshold dissipation mechanism. The findings emphasize that the governance paradigm for resource supply chains must shift from flow monitoring to topological optimization, suggesting that constructing strategic redundancy is critical for enhancing the resilience of the global supply network.
{"title":"Global nickel scrap supply network vulnerability: Endogenous structural exposure and external shocks propagation","authors":"Xiaohong Chen , Daipeng Ma , Jian Guan","doi":"10.1016/j.ress.2026.112339","DOIUrl":"10.1016/j.ress.2026.112339","url":null,"abstract":"<div><div>Against the backdrop of escalating geopolitical tensions, the global scrap nickel supply network (GSNSN) faces mounting challenges to its systemic reliability. This paper constructs an analytical framework integrating endogenous structural exposure with cascading failure simulations to assess the structural vulnerability mechanisms of the GSNSN. The results indicate that, from the perspective of endogenous structural exposure, the system exhibits significant characteristics of non-linear abrupt transitions, revealing the structural criticality of the network’s transition from a steady state to a collapse. Regarding external shocks, national import/export bans or disruptions in cooperation generally manifest into four risk propagation modes: long-range & large-scale, long-range & small-scale, short-range & large-scale, and short-range & small-scale. Specifically, high-coupling strategic corridors or nodes constitute the core of vulnerability due to rigid supply-demand dependencies (e.g., GBR→USA, DEU↔SWE, CHN, and USA), whereas nodes with high risk tolerance function as physical firewalls through a threshold dissipation mechanism. The findings emphasize that the governance paradigm for resource supply chains must shift from flow monitoring to topological optimization, suggesting that constructing strategic redundancy is critical for enhancing the resilience of the global supply network.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"273 ","pages":"Article 112339"},"PeriodicalIF":11.0,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146098525","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}
Pub Date : 2026-09-01Epub Date: 2026-01-29DOI: 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.
{"title":"Beyond waterlogging: Evaluating the impact of extreme rainfall on the road network","authors":"Jie Liu , Zizhen Xu , Li Wan , Kristen MacAskill","doi":"10.1016/j.ress.2026.112308","DOIUrl":"10.1016/j.ress.2026.112308","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"273 ","pages":"Article 112308"},"PeriodicalIF":11.0,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146098526","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}
Traditional flaw tolerance assessment methods for aeroengine turbine blisks suffer from incomplete spatial coverage and inadequate uncertainty quantification, as hotspot-based approaches focus solely on predetermined high-stress regions while neglecting stochastic flaw distributions across structural surfaces. This study develops a Zone-Collaborative Integrated (ZCI) framework that systematically addresses these limitations through three integrated components: zone-probabilistic decomposition using improved Gaussian Mixture Models with K-means initialization for surface partitioning and Combined Sampling Method for flaw coordinate uncertainty quantification; Genetic Algorithm-enhanced Kriging (GA-Kriging) surrogate modeling integrated with series system theory for multi-zone reliability assessment; and systematic implementation algorithm enabling comprehensive spatial coverage with computational efficiency. Validation through notched plate and turbine blisk case studies demonstrate that GA-Kriging achieves 63.3% improvement in computational efficiency and 31.8%/26.7% enhancement in training/testing precision compared to conventional methods, with normalized RMSE below 0.02. The ZCI framework exhibits 94.95-98.70% accuracy relative to direct simulation while predicting 12-76% higher fatigue life than hotspot method at equivalent reliability levels (720 cycles for hotspot vs. 1518 cycles for two-zone ZCI at R = 0.99 in Case 2). Sensitivity analysis reveals flaw geometry parameters dominate reliability outcomes (flaw radius: -1.75, flaw depth: -1.25), providing quantitative guidance for structural design optimization. The proposed framework transforms computationally prohibitive full-scale reliability problems into manageable zone-based assessments, offering a systematic approach for probabilistic flaw tolerance design of critical aerospace components.
{"title":"Zone-collaborative integrated framework for probabilistic flaw tolerance assessment of aeroengine structure","authors":"Jiong-ran Wen , Bai-yang Zheng , Jian Li , Cheng-wei Fei","doi":"10.1016/j.ress.2026.112338","DOIUrl":"10.1016/j.ress.2026.112338","url":null,"abstract":"<div><div>Traditional flaw tolerance assessment methods for aeroengine turbine blisks suffer from incomplete spatial coverage and inadequate uncertainty quantification, as hotspot-based approaches focus solely on predetermined high-stress regions while neglecting stochastic flaw distributions across structural surfaces. This study develops a Zone-Collaborative Integrated (ZCI) framework that systematically addresses these limitations through three integrated components: zone-probabilistic decomposition using improved Gaussian Mixture Models with K-means initialization for surface partitioning and Combined Sampling Method for flaw coordinate uncertainty quantification; Genetic Algorithm-enhanced Kriging (GA-Kriging) surrogate modeling integrated with series system theory for multi-zone reliability assessment; and systematic implementation algorithm enabling comprehensive spatial coverage with computational efficiency. Validation through notched plate and turbine blisk case studies demonstrate that GA-Kriging achieves 63.3% improvement in computational efficiency and 31.8%/26.7% enhancement in training/testing precision compared to conventional methods, with normalized RMSE below 0.02. The ZCI framework exhibits 94.95-98.70% accuracy relative to direct simulation while predicting 12-76% higher fatigue life than hotspot method at equivalent reliability levels (720 cycles for hotspot vs. 1518 cycles for two-zone ZCI at R = 0.99 in Case 2). Sensitivity analysis reveals flaw geometry parameters dominate reliability outcomes (flaw radius: -1.75, flaw depth: -1.25), providing quantitative guidance for structural design optimization. The proposed framework transforms computationally prohibitive full-scale reliability problems into manageable zone-based assessments, offering a systematic approach for probabilistic flaw tolerance design of critical aerospace components.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"273 ","pages":"Article 112338"},"PeriodicalIF":11.0,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146175039","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}
Pub Date : 2026-09-01Epub Date: 2026-01-29DOI: 10.1016/j.ress.2026.112323
Wuyin Lin , Songming Yu , Xinran Yu , Yuxing Li , Cuiwei Liu
Integrating hydrogen into urban gas pipeline networks is a pivotal technology for energy transition yet poses critical safety threats, thus necessitating comprehensive risk assessment of hydrogen-blended natural gas pipelines. This study performs full quantitative risk assessment of leakage failure and accident evolution by proposing a novel framework that integrates causal inference (Bow-Tie analysis) with probabilistic machine learning (Bayesian networks), enabling systematic failure factor identification and dynamic accident progression simulation. Key findings indicate human factors and pipeline material degradation as primary triggers. The studied pipeline exhibits a low baseline failure probability, with dispersion emerging as the most likely consequence of leakage. Higher hydrogen blending ratios significantly elevate jet fire risk due to hydrogen’s low ignition energy, while hydrogen’s inherent buoyancy and high diffusivity notably mitigate the likelihood of flash fire and vapor cloud explosion. The case study verifies the model’s practicability, and macro-micro analyses provide holistic insights, offering a reliable method to guide pipeline safety and reliability improvement amid energy transition.
{"title":"Analysis of urban hydrogen-blended natural gas pipeline leak failure and accident evolution based on the combination of causal inference and probabilistic machine learning","authors":"Wuyin Lin , Songming Yu , Xinran Yu , Yuxing Li , Cuiwei Liu","doi":"10.1016/j.ress.2026.112323","DOIUrl":"10.1016/j.ress.2026.112323","url":null,"abstract":"<div><div>Integrating hydrogen into urban gas pipeline networks is a pivotal technology for energy transition yet poses critical safety threats, thus necessitating comprehensive risk assessment of hydrogen-blended natural gas pipelines. This study performs full quantitative risk assessment of leakage failure and accident evolution by proposing a novel framework that integrates causal inference (Bow-Tie analysis) with probabilistic machine learning (Bayesian networks), enabling systematic failure factor identification and dynamic accident progression simulation. Key findings indicate human factors and pipeline material degradation as primary triggers. The studied pipeline exhibits a low baseline failure probability, with dispersion emerging as the most likely consequence of leakage. Higher hydrogen blending ratios significantly elevate jet fire risk due to hydrogen’s low ignition energy, while hydrogen’s inherent buoyancy and high diffusivity notably mitigate the likelihood of flash fire and vapor cloud explosion. The case study verifies the model’s practicability, and macro-micro analyses provide holistic insights, offering a reliable method to guide pipeline safety and reliability improvement amid energy transition.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"273 ","pages":"Article 112323"},"PeriodicalIF":11.0,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146098522","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}
Pub Date : 2026-09-01Epub Date: 2026-01-29DOI: 10.1016/j.ress.2026.112317
Wenbin Jiang , Wenkai Hu , Yupeng Li , Weihua Cao
As an effective alarm monitoring strategy, alarm event prediction helps mitigate the impact of alarm floods and the risk of industrial accidents by providing early warnings of potential future alarms, thereby allowing operators more time to take corrective action. However, in continuous industrial processes, varying operating conditions and abnormal states cause real-time fluctuations in alarm rates, posing challenges for existing methods to achieve satisfactory prediction performance. In view of such issues, this paper proposes a new alarm event prediction method adapting to variable alarm rates over long-term consecutive alarm monitoring periods using multi-dimensional sequence embedding and improved Informer. The contributions are threefold: 1) An adaptive alarm sequence segmentation strategy is designed to generate input alarm sequences adapting to alarm rates; 2) a multi-dimensional sequence embedding method based on both the alarm tags and time intervals is proposed to convert the textual alarm messages into numerical vectors; and 3) an Informer based alarm event prediction model is developed for precise and early alarm event prediction under alarm flood and non-flood periods. A case study based on the Vinyl Acetate Monomer public model is given to prove the effectiveness of the proposed method.
{"title":"Multi-dimensional sequence embedding and improved Informer for prediction of industrial alarm events","authors":"Wenbin Jiang , Wenkai Hu , Yupeng Li , Weihua Cao","doi":"10.1016/j.ress.2026.112317","DOIUrl":"10.1016/j.ress.2026.112317","url":null,"abstract":"<div><div>As an effective alarm monitoring strategy, alarm event prediction helps mitigate the impact of alarm floods and the risk of industrial accidents by providing early warnings of potential future alarms, thereby allowing operators more time to take corrective action. However, in continuous industrial processes, varying operating conditions and abnormal states cause real-time fluctuations in alarm rates, posing challenges for existing methods to achieve satisfactory prediction performance. In view of such issues, this paper proposes a new alarm event prediction method adapting to variable alarm rates over long-term consecutive alarm monitoring periods using multi-dimensional sequence embedding and improved Informer. The contributions are threefold: 1) An adaptive alarm sequence segmentation strategy is designed to generate input alarm sequences adapting to alarm rates; 2) a multi-dimensional sequence embedding method based on both the alarm tags and time intervals is proposed to convert the textual alarm messages into numerical vectors; and 3) an Informer based alarm event prediction model is developed for precise and early alarm event prediction under alarm flood and non-flood periods. A case study based on the Vinyl Acetate Monomer public model is given to prove the effectiveness of the proposed method.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"273 ","pages":"Article 112317"},"PeriodicalIF":11.0,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146098524","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}
Pub Date : 2026-09-01Epub Date: 2026-01-24DOI: 10.1016/j.ress.2026.112284
July B. Macedo , Plínio M.S. Ramos , Caio B.S. Maior , Márcio J.C. Moura , Isis D. Lins
Aviation accidents are a significant concern due to the potential loss of life, and human factors have emerged as the primary underlying cause. The aviation industry maintains extensive records, including accident investigation reports, which offer valuable insights for decision-making. This research explores the application of Natural Language Processing (NLP) as a solution for analyzing these documents and conducting risk assessments, empowering experts to manage accidents better and develop effective preventive measures. We propose a novel methodology that leverages a Bidirectional Encoder Representations from Transformers (BERT)-based classifier, combined with topic modeling techniques, to automate the labeling of accident datasets and identify key pilot failures contributing to aviation accidents. This automated labeling process is a critical step in efficiently creating a high-quality dataset essential for training a classifier capable of accurately detecting specific failure types. By applying the methodology to accident reports from the National Transportation Safety Board (NTSB), we successfully trained a classifier that identifies pilot failures, such as skill-based errors, routine violations, and perceptual errors. This study contributes to the field by introducing an innovative integration of contextual embeddings and topic modeling, significantly reducing manual efforts in data preparation while enhancing the precision and efficiency of analyzing aviation accident data. The findings demonstrate the potential of NLP to streamline the analysis of accident reports, assisting experts in developing targeted training programs, procedural improvements, and risk mitigation strategies to address pilot-related errors effectively.
{"title":"Automated identification of pilot failures in aviation accidents using a BERT-based classifier and topic modeling","authors":"July B. Macedo , Plínio M.S. Ramos , Caio B.S. Maior , Márcio J.C. Moura , Isis D. Lins","doi":"10.1016/j.ress.2026.112284","DOIUrl":"10.1016/j.ress.2026.112284","url":null,"abstract":"<div><div>Aviation accidents are a significant concern due to the potential loss of life, and human factors have emerged as the primary underlying cause. The aviation industry maintains extensive records, including accident investigation reports, which offer valuable insights for decision-making. This research explores the application of Natural Language Processing (NLP) as a solution for analyzing these documents and conducting risk assessments, empowering experts to manage accidents better and develop effective preventive measures. We propose a novel methodology that leverages a Bidirectional Encoder Representations from Transformers (BERT)-based classifier, combined with topic modeling techniques, to automate the labeling of accident datasets and identify key pilot failures contributing to aviation accidents. This automated labeling process is a critical step in efficiently creating a high-quality dataset essential for training a classifier capable of accurately detecting specific failure types. By applying the methodology to accident reports from the National Transportation Safety Board (NTSB), we successfully trained a classifier that identifies pilot failures, such as skill-based errors, routine violations, and perceptual errors. This study contributes to the field by introducing an innovative integration of contextual embeddings and topic modeling, significantly reducing manual efforts in data preparation while enhancing the precision and efficiency of analyzing aviation accident data. The findings demonstrate the potential of NLP to streamline the analysis of accident reports, assisting experts in developing targeted training programs, procedural improvements, and risk mitigation strategies to address pilot-related errors effectively.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"273 ","pages":"Article 112284"},"PeriodicalIF":11.0,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146174836","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}
Pub Date : 2026-09-01Epub Date: 2026-02-01DOI: 10.1016/j.ress.2026.112348
Zhihong Huang , Gang Ma , Zhitao Ai , Jiawei Wang , Xiaolin Chang , Wei Zhou
Accurate deformation analysis is crucial for the safety assessment and risk management of high earth-rock dams. While conventional surrogate-assisted optimization improves prediction accuracy, the neglect of intrinsic physical parameter correlations often leads to non-unique solutions, limited accuracy gains, and numerical divergence. This study proposes a physics-constrained digital twin (DT) framework that enables high-fidelity virtual-physical synchronization. The key innovation is a physical constraint mechanism utilizing a β-variational autoencoder (β-VAE) to extract parameter correlations from global experimental datasets as prior knowledge. By integrating this mechanism with multi-objective optimization and an elite archiving strategy, the framework ensures stable and physically consistent model evolution. Validated on the 303 m high LHK dam, the results demonstrate a 28 % improvement in prediction accuracy and a transition to near real-time computational performance. This framework provides a more reliable and physically consistent modeling approach for intelligent dam operation and lifecycle risk assessment.
{"title":"Physics-constrained digital twin framework for deformation analysis and safety assessment of high earth-rock dams","authors":"Zhihong Huang , Gang Ma , Zhitao Ai , Jiawei Wang , Xiaolin Chang , Wei Zhou","doi":"10.1016/j.ress.2026.112348","DOIUrl":"10.1016/j.ress.2026.112348","url":null,"abstract":"<div><div>Accurate deformation analysis is crucial for the safety assessment and risk management of high earth-rock dams. While conventional surrogate-assisted optimization improves prediction accuracy, the neglect of intrinsic physical parameter correlations often leads to non-unique solutions, limited accuracy gains, and numerical divergence. This study proposes a physics-constrained digital twin (DT) framework that enables high-fidelity virtual-physical synchronization. The key innovation is a physical constraint mechanism utilizing a <em>β</em>-variational autoencoder (<em>β</em>-VAE) to extract parameter correlations from global experimental datasets as prior knowledge. By integrating this mechanism with multi-objective optimization and an elite archiving strategy, the framework ensures stable and physically consistent model evolution. Validated on the 303 m high LHK dam, the results demonstrate a 28 % improvement in prediction accuracy and a transition to near real-time computational performance. This framework provides a more reliable and physically consistent modeling approach for intelligent dam operation and lifecycle risk assessment.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"273 ","pages":"Article 112348"},"PeriodicalIF":11.0,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146174837","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}
Pub Date : 2026-09-01Epub Date: 2026-02-07DOI: 10.1016/j.ress.2026.112372
Chong Gao , Hui Jiang , Mang Li
Disaster response systems (DRS) operate in an uncertain environment and are continuously tested by external perturbations and internal failures. The ability to withstand disruptions and mitigate the risk of structural disintegration is a defining aspect of a resilient DRS. The current research on the resilience of DRS aims to reveal its robustness performance using metrics from social network analysis. However, the correspondence between these metrics and the underlying ability to maintain structural integrity is neither direct nor well-established. In this paper, we first construct faithful representations of DRS using hypergraph modelling. We demonstrate that hypergraph-based representations provide a principled basis for robustness analysis, revealing local structures key to structural integrity. We establish comprehensive robustness metrics and introduce a principled analysis framework to formalise these key local structures. We also perform a stability decomposition and obtain stability indicators that are used to identify the unstable local structures with weak internal coherence and fragile external embedding. Then we formulate the robustness-enhancing problem and develop fortification methods aiming at maximising the total robustness gain. Extensive simulations demonstrate that our approach substantially outperforms alternative strategies, including coreness-based and graph-based methods, across a wide range of fortification budgets. In addition, the proposed method has an efficient numerical implementation, and we validate it in large-scale synthetic hypergraphs.
{"title":"Robustness of disaster response systems: Hypergraph modelling, key local structures, and fortification methods","authors":"Chong Gao , Hui Jiang , Mang Li","doi":"10.1016/j.ress.2026.112372","DOIUrl":"10.1016/j.ress.2026.112372","url":null,"abstract":"<div><div>Disaster response systems (DRS) operate in an uncertain environment and are continuously tested by external perturbations and internal failures. The ability to withstand disruptions and mitigate the risk of structural disintegration is a defining aspect of a resilient DRS. The current research on the resilience of DRS aims to reveal its robustness performance using metrics from social network analysis. However, the correspondence between these metrics and the underlying ability to maintain structural integrity is neither direct nor well-established. In this paper, we first construct faithful representations of DRS using hypergraph modelling. We demonstrate that hypergraph-based representations provide a principled basis for robustness analysis, revealing local structures key to structural integrity. We establish comprehensive robustness metrics and introduce a principled analysis framework to formalise these key local structures. We also perform a stability decomposition and obtain stability indicators that are used to identify the unstable local structures with weak internal coherence and fragile external embedding. Then we formulate the robustness-enhancing problem and develop fortification methods aiming at maximising the total robustness gain. Extensive simulations demonstrate that our approach substantially outperforms alternative strategies, including coreness-based and graph-based methods, across a wide range of fortification budgets. In addition, the proposed method has an efficient numerical implementation, and we validate it in large-scale synthetic hypergraphs.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"273 ","pages":"Article 112372"},"PeriodicalIF":11.0,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146174921","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}
Pub Date : 2026-09-01Epub Date: 2026-02-09DOI: 10.1016/j.ress.2026.112387
Xiaoli Liu , Mingyuan Zhang , Haixing Liu
This study proposes a framework to enhance the resilience of water distribution systems (WDSs) through optimized isolation valve operation and identification of critical components. First, the WDS topology is converted into a segment-valve model to efficiently identify the isolation valves for isolating failed pipes. Second, quantitative metrics are established from both hydraulic and water quality perspectives to assess service performance losses caused by isolation valve closures (IVCs). Subsequently, an optimization model is developed to determine the IVC scheme that minimizes cumulative performance loss (CPL) during failures. Finally, a criticality assessment framework is introduced to accurately identify segments and isolation valves that significantly impact system resilience. The proposed framework was validated on two real-world WDSs in China with distinct topological configurations. The results indicate that the optimized IVC scheme reduces average CPL by approximately 15% and 26.63%, respectively, compared to the minimum isolation time scheme and its reverse scheme. The distribution characteristics of critical components vary across WDSs with different topologies. Furthermore, implementing N-valve and N-1 valve configurations reduces the average criticality of segments by 71.02% and 64.69%, respectively, thereby enhancing system resilience. This study provides decision support for developing efficient isolation valve operation schemes and precise component maintenance strategies.
{"title":"Optimization of isolation valve operation and identification of critical components for enhancing the resilience of water distribution systems","authors":"Xiaoli Liu , Mingyuan Zhang , Haixing Liu","doi":"10.1016/j.ress.2026.112387","DOIUrl":"10.1016/j.ress.2026.112387","url":null,"abstract":"<div><div>This study proposes a framework to enhance the resilience of water distribution systems (WDSs) through optimized isolation valve operation and identification of critical components. First, the WDS topology is converted into a segment-valve model to efficiently identify the isolation valves for isolating failed pipes. Second, quantitative metrics are established from both hydraulic and water quality perspectives to assess service performance losses caused by isolation valve closures (IVCs). Subsequently, an optimization model is developed to determine the IVC scheme that minimizes cumulative performance loss (CPL) during failures. Finally, a criticality assessment framework is introduced to accurately identify segments and isolation valves that significantly impact system resilience. The proposed framework was validated on two real-world WDSs in China with distinct topological configurations. The results indicate that the optimized IVC scheme reduces average CPL by approximately 15% and 26.63%, respectively, compared to the minimum isolation time scheme and its reverse scheme. The distribution characteristics of critical components vary across WDSs with different topologies. Furthermore, implementing <em>N</em>-valve and <em>N</em>-1 valve configurations reduces the average criticality of segments by 71.02% and 64.69%, respectively, thereby enhancing system resilience. This study provides decision support for developing efficient isolation valve operation schemes and precise component maintenance strategies.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"273 ","pages":"Article 112387"},"PeriodicalIF":11.0,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146174924","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}
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