Pub Date : 2026-12-01Epub Date: 2026-03-19DOI: 10.1007/s11571-026-10435-1
Zao Li, Xinke Li, Mingyuan Fang, Xia Sun, Wencong Zhang
With the continuous development of the nighttime economy in recent years, urban nocturnal illumination has received widespread attention. The evaluation of night lighting in traditional commercial streets, as a common element of urban history and commerce, is of great importance. In this study, we aimed to conduct a comprehensive investigation of the nighttime illumination of traditional urban streets, exemplified by Tunxi Old Street and Liyang IN Alley, Huangshan City, China, using methods such as electroencephalography(EEG) and the semantic differential technique. Two main results were generated. 1) In the night lighting of traditional commercial streets, reasonable illuminance must be achieved to avoid an incongruous nocturnal atmosphere that substantially affects street quality. 2) Regarding lighting selection, floodlighting produces the best effects, followed by compound lighting, whereas linear lighting yielded the poorest results.
{"title":"Research on the perception of Huizhou traditional street nightscapes: a lab experiment using EEG.","authors":"Zao Li, Xinke Li, Mingyuan Fang, Xia Sun, Wencong Zhang","doi":"10.1007/s11571-026-10435-1","DOIUrl":"https://doi.org/10.1007/s11571-026-10435-1","url":null,"abstract":"<p><p>With the continuous development of the nighttime economy in recent years, urban nocturnal illumination has received widespread attention. The evaluation of night lighting in traditional commercial streets, as a common element of urban history and commerce, is of great importance. In this study, we aimed to conduct a comprehensive investigation of the nighttime illumination of traditional urban streets, exemplified by Tunxi Old Street and Liyang IN Alley, Huangshan City, China, using methods such as electroencephalography(EEG) and the semantic differential technique. Two main results were generated. 1) In the night lighting of traditional commercial streets, reasonable illuminance must be achieved to avoid an incongruous nocturnal atmosphere that substantially affects street quality. 2) Regarding lighting selection, floodlighting produces the best effects, followed by compound lighting, whereas linear lighting yielded the poorest results.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"20 1","pages":"64"},"PeriodicalIF":3.9,"publicationDate":"2026-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13003100/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147497650","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-10-01Epub Date: 2026-02-01DOI: 10.1016/j.ress.2026.112343
Yiqiong Zhang , Fanyuanhang Zhang , Zhiyuan Li , Yuwu Xiao , Hongwei Wang , Min Ouyang
Critical infrastructure systems (CISs) sustain modern societies, yet their interdependencies allow local disruptions to cascade across systems and amplify socio-economic losses. Hazard-specific models represent physical mechanisms but often struggle to capture the full uncertainty and complexity of disruption impacts, while worst-case disruption analysis complements them by identifying upper-bound consequences under the most adverse conditions. However, existing worst-case analyses usually optimize system performance metrics and overlook a logical interdependency created by people who jointly depend on multiple CISs’ services. We propose a people-centric worst-case disruption modelling framework to identify failure scenario that leads to the largest impacts on people under both localized and non-localized disruptions, while capturing the new logical interdependency. Applied to power, gas, water and road-transport systems in a region, results reveal that worst-case impacts and single- versus multi-system outage patterns vary with disruption intensity and interdependency strength. In contrast, traditional performance-centric worst-case analyse identifies different disruption scenarios and underestimates affected populations by up to 114.65 %. Sensitivity analyses on CIS topologies and interdependencies, people-centric objective functions, and correlations in service states across zones further demonstrate how input parameters shape worst-case disruption scenarios. Together, these findings underscore the importance of integrating a people-centric perspective into worst-case disruption analyses to inform disaster risk reduction.
{"title":"A people-centric framework for worst-case disruption analysis of interdependent infrastructure systems","authors":"Yiqiong Zhang , Fanyuanhang Zhang , Zhiyuan Li , Yuwu Xiao , Hongwei Wang , Min Ouyang","doi":"10.1016/j.ress.2026.112343","DOIUrl":"10.1016/j.ress.2026.112343","url":null,"abstract":"<div><div>Critical infrastructure systems (CISs) sustain modern societies, yet their interdependencies allow local disruptions to cascade across systems and amplify socio-economic losses. Hazard-specific models represent physical mechanisms but often struggle to capture the full uncertainty and complexity of disruption impacts, while worst-case disruption analysis complements them by identifying upper-bound consequences under the most adverse conditions. However, existing worst-case analyses usually optimize system performance metrics and overlook a logical interdependency created by people who jointly depend on multiple CISs’ services. We propose a people-centric worst-case disruption modelling framework to identify failure scenario that leads to the largest impacts on people under both localized and non-localized disruptions, while capturing the new logical interdependency. Applied to power, gas, water and road-transport systems in a region, results reveal that worst-case impacts and single- versus multi-system outage patterns vary with disruption intensity and interdependency strength. In contrast, traditional performance-centric worst-case analyse identifies different disruption scenarios and underestimates affected populations by up to 114.65 %. Sensitivity analyses on CIS topologies and interdependencies, people-centric objective functions, and correlations in service states across zones further demonstrate how input parameters shape worst-case disruption scenarios. Together, these findings underscore the importance of integrating a people-centric perspective into worst-case disruption analyses to inform disaster risk reduction.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"274 ","pages":"Article 112343"},"PeriodicalIF":11.0,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146192466","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 : 2026-10-01Epub Date: 2026-02-09DOI: 10.1016/j.ress.2026.112393
Chuan-Yao Li , Guanyu Lu , Fan Zhang , Liang Chen
Fire evacuation in multi-storey school dormitories is challenging due to rising occupancy and complex vertical circulation. This study develops an improved social force model that couples pedestrian motion with fire-induced environmental fields (CO, visibility and temperature). To represent adolescent evacuation behaviour, model parameters reflecting students’ interpersonal spacing and following tendencies are calibrated using real-world video observations. Scenario simulations systematically examine how the fire source location within multi-storey dormitories influences evacuation performance, focusing on pedestrian movement characteristics, spatio-temporal density evolution, and total evacuation time. Results indicate that fires located on lower floors or adjacent to stairwells trigger flow breakdowns in the later stages of evacuation, characterised by sharp density peaks and stage-dependent density evolution patterns. Recurrent congestion also emerges at stair and corridor junctions, intensifying bottlenecks and delay risks. When hazards align with evacuation routes, thermal build-up, CO accumulation and visibility loss propagate along paths, triggering route switching, directional differentiation and local congestion. In contrast, upper-floor fires exert weaker network-wide disruptions and may even yield evacuation efficiency gains via heightened risk perception. The findings reveal phase-specific spatio-temporal heterogeneity and the coupling between hazard fields and crowd dynamics, and translate these insights into targeted evacuation management strategies for dormitory buildings.
{"title":"Modelling evacuation dynamics in multi-storey school dormitories under fire conditions","authors":"Chuan-Yao Li , Guanyu Lu , Fan Zhang , Liang Chen","doi":"10.1016/j.ress.2026.112393","DOIUrl":"10.1016/j.ress.2026.112393","url":null,"abstract":"<div><div>Fire evacuation in multi-storey school dormitories is challenging due to rising occupancy and complex vertical circulation. This study develops an improved social force model that couples pedestrian motion with fire-induced environmental fields (CO, visibility and temperature). To represent adolescent evacuation behaviour, model parameters reflecting students’ interpersonal spacing and following tendencies are calibrated using real-world video observations. Scenario simulations systematically examine how the fire source location within multi-storey dormitories influences evacuation performance, focusing on pedestrian movement characteristics, spatio-temporal density evolution, and total evacuation time. Results indicate that fires located on lower floors or adjacent to stairwells trigger flow breakdowns in the later stages of evacuation, characterised by sharp density peaks and stage-dependent density evolution patterns. Recurrent congestion also emerges at stair and corridor junctions, intensifying bottlenecks and delay risks. When hazards align with evacuation routes, thermal build-up, CO accumulation and visibility loss propagate along paths, triggering route switching, directional differentiation and local congestion. In contrast, upper-floor fires exert weaker network-wide disruptions and may even yield evacuation efficiency gains via heightened risk perception. The findings reveal phase-specific spatio-temporal heterogeneity and the coupling between hazard fields and crowd dynamics, and translate these insights into targeted evacuation management strategies for dormitory buildings.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"274 ","pages":"Article 112393"},"PeriodicalIF":11.0,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146192465","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 : 2026-10-01Epub Date: 2026-02-11DOI: 10.1016/j.ress.2026.112390
Ali Peivand, Ehsan Azad-Farsani
Cybersecurity threats such as False Data Injection (FDI) attacks pose significant risks to modern power systems, undermining both operational stability and economic efficiency. To address this challenge, we propose an Intelligent Moving Target Defense (iMTD) framework that enhances grid resilience by dynamically modifying the reactances of selected transmission lines using a Deep Q-Network (DQN). This strategy obscures system parameters from potential attackers while ensuring minimal disruption to power flow and cost. Unlike existing methods, such as Pareto-based Multi-Objective MTD (MO-MTD) and the Smallest Principal Angle (SPA) approach, the iMTD model intelligently identifies and perturbs the most influential lines to maximize attack detectability with minimal operational cost impact. A cost-aware reward structure is designed to balance cybersecurity and system efficiency. The proposed framework is evaluated on the IEEE 118-bus test system under both random and adversarial FDI attack scenarios, including stealthy, topology-aware, economic, sparse, adaptive, and coordinated attacks. Simulation results demonstrate that, under random FDI attacks, the iMTD achieves an average attack detection rate of 91.3 % while maintaining an OPF cost increment below 0.0003 %, outperforming SPA and MO-MTD benchmarks by up to 99 % cost reduction. Under worst-case adversarial attacks, detection performance stabilizes at 52.3 % with virtually zero cost increment, highlighting the robustness of the learned defense policy against intelligent attackers. These results highlight the potential of intelligent reinforcement learning techniques in developing adaptive and cost-effective cybersecurity solutions for cyber-physical power systems.
{"title":"Enhancing power grid cybersecurity against FDI attacks via deep Q-network-based moving target defense","authors":"Ali Peivand, Ehsan Azad-Farsani","doi":"10.1016/j.ress.2026.112390","DOIUrl":"10.1016/j.ress.2026.112390","url":null,"abstract":"<div><div>Cybersecurity threats such as False Data Injection (FDI) attacks pose significant risks to modern power systems, undermining both operational stability and economic efficiency. To address this challenge, we propose an Intelligent Moving Target Defense (iMTD) framework that enhances grid resilience by dynamically modifying the reactances of selected transmission lines using a Deep Q-Network (DQN). This strategy obscures system parameters from potential attackers while ensuring minimal disruption to power flow and cost. Unlike existing methods, such as Pareto-based Multi-Objective MTD (MO-MTD) and the Smallest Principal Angle (SPA) approach, the iMTD model intelligently identifies and perturbs the most influential lines to maximize attack detectability with minimal operational cost impact. A cost-aware reward structure is designed to balance cybersecurity and system efficiency. The proposed framework is evaluated on the IEEE 118-bus test system under both random and adversarial FDI attack scenarios, including stealthy, topology-aware, economic, sparse, adaptive, and coordinated attacks. Simulation results demonstrate that, under random FDI attacks, the iMTD achieves an average attack detection rate of 91.3 % while maintaining an OPF cost increment below 0.0003 %, outperforming SPA and MO-MTD benchmarks by up to 99 % cost reduction. Under worst-case adversarial attacks, detection performance stabilizes at 52.3 % with virtually zero cost increment, highlighting the robustness of the learned defense policy against intelligent attackers. These results highlight the potential of intelligent reinforcement learning techniques in developing adaptive and cost-effective cybersecurity solutions for cyber-physical power systems.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"274 ","pages":"Article 112390"},"PeriodicalIF":11.0,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146192467","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 : 2026-10-01Epub Date: 2026-02-07DOI: 10.1016/j.ress.2026.112374
Linchao Li , Bangxing Li , Liangjian Zhong
Urban mobility flow networks are vital for ensuring the functional efficiency of cities, supporting the movement of people, goods, and services. However, these networks are increasingly vulnerable to disruptions caused by factors such as extreme weather events, traffic accidents, and system failures. This study presents a multi-scale framework to assess the resilience of urban mobility flow networks, focusing on Shenzhen as a case study. By evaluating resilience at the macro, meso, and micro levels, the study investigates the impacts of disruptions and recovery processes across different spatial scales. Key findings reveal that a small subset of high-degree nodes and high-weight edges significantly influences network performance, with their removal causing rapid degradation and swift recovery upon restoration. The analysis also highlights that centrality metrics such as degree, betweenness, and eigenvector centrality are informative for assessing the resilience of urban mobility systems. At the macro scale, degree centrality nodes and weight-based edges exhibit the fastest failure and recovery dynamics, while eigenvector centrality ensures more stable long-term recovery. The meso and micro-scale analyzes underscore the importance of local connectivity and suggest that central districts exhibit stronger resilience compared to peripheral areas. The proposed method assesses urban mobility flow network resilience at multiple scales.
{"title":"Resilience assessment of urban mobility flow networks from different scales: A case study in shenzhen","authors":"Linchao Li , Bangxing Li , Liangjian Zhong","doi":"10.1016/j.ress.2026.112374","DOIUrl":"10.1016/j.ress.2026.112374","url":null,"abstract":"<div><div>Urban mobility flow networks are vital for ensuring the functional efficiency of cities, supporting the movement of people, goods, and services. However, these networks are increasingly vulnerable to disruptions caused by factors such as extreme weather events, traffic accidents, and system failures. This study presents a multi-scale framework to assess the resilience of urban mobility flow networks, focusing on Shenzhen as a case study. By evaluating resilience at the macro, meso, and micro levels, the study investigates the impacts of disruptions and recovery processes across different spatial scales. Key findings reveal that a small subset of high-degree nodes and high-weight edges significantly influences network performance, with their removal causing rapid degradation and swift recovery upon restoration. The analysis also highlights that centrality metrics such as degree, betweenness, and eigenvector centrality are informative for assessing the resilience of urban mobility systems. At the macro scale, degree centrality nodes and weight-based edges exhibit the fastest failure and recovery dynamics, while eigenvector centrality ensures more stable long-term recovery. The meso and micro-scale analyzes underscore the importance of local connectivity and suggest that central districts exhibit stronger resilience compared to peripheral areas. The proposed method assesses urban mobility flow network resilience at multiple scales.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"274 ","pages":"Article 112374"},"PeriodicalIF":11.0,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146192468","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 : 2026-10-01Epub Date: 2026-02-09DOI: 10.1016/j.ress.2026.112384
Fatima-Zahra Lahlou , Farhat Mahmood , Ammar M. Khourchid , Bilal M. Ayyub , Sami G. Al-Ghamdi , Tareq Al-Ansari
Water distribution networks provide a critical role in ensuring a reliable water supply. Assessing the resilience of these networks is essential for managing risks and enhancing water security, particularly in evaluating the reliability of water transmission from reservoirs to tanks. However, existing methodologies often focus on a single aspect, such as connectivity or redundancy, without integrating multiple resilience dimensions. This study addresses this gap by developing a resilience assessment framework that evaluates reservoir-to-tank resilience through three key indicators: hydraulic connectivity, supply path diversity, and supply path stability. The hydraulic connectivity indicator couples graph theory with hydraulic characteristics to evaluate the efficiency of water transport from reservoirs to tanks by incorporating real-time head loss calculations. Supply path diversity quantifies the extent to which the network utilizes multiple transmission routes, and supply path stability assesses the persistence of supply paths over time. These indicators are combined into a composite resilience score to provide a holistic assessment of network performance. A sensitivity analysis is conducted to examine the robustness of the resilience rankings under different methodological assumptions. This methodology was applied to the C-Town benchmark network with seven terminal tanks (Tank 1 to Tank 7) over a 7-day simulation period, and revealed that when considering only hydraulic connectivity, Tank 1 consistently ranked as the most resilient tank, while Tank 4 was the least resilient, reflecting their differences in network connectivity and susceptibility to head loss. When integrating all three indicators into the composite resilience score, Tank 1 remained the most resilient, while Tank 4 continued to rank as one of the least resilient tanks, confirming the stability of the assessment and highlighting the influence of both structural and operational factors on overall resilience. The proposed framework provides a structured approach for evaluating reservoir-to-tank resilience and can support decision-makers in prioritizing network reinforcements and developing targeted mitigation strategies to enhance long-term water security.
{"title":"Water transmission resilience analytics informed by hydraulics using connectivity, path diversity, and stability","authors":"Fatima-Zahra Lahlou , Farhat Mahmood , Ammar M. Khourchid , Bilal M. Ayyub , Sami G. Al-Ghamdi , Tareq Al-Ansari","doi":"10.1016/j.ress.2026.112384","DOIUrl":"10.1016/j.ress.2026.112384","url":null,"abstract":"<div><div>Water distribution networks provide a critical role in ensuring a reliable water supply. Assessing the resilience of these networks is essential for managing risks and enhancing water security, particularly in evaluating the reliability of water transmission from reservoirs to tanks. However, existing methodologies often focus on a single aspect, such as connectivity or redundancy, without integrating multiple resilience dimensions. This study addresses this gap by developing a resilience assessment framework that evaluates reservoir-to-tank resilience through three key indicators: hydraulic connectivity, supply path diversity, and supply path stability. The hydraulic connectivity indicator couples graph theory with hydraulic characteristics to evaluate the efficiency of water transport from reservoirs to tanks by incorporating real-time head loss calculations. Supply path diversity quantifies the extent to which the network utilizes multiple transmission routes, and supply path stability assesses the persistence of supply paths over time. These indicators are combined into a composite resilience score to provide a holistic assessment of network performance. A sensitivity analysis is conducted to examine the robustness of the resilience rankings under different methodological assumptions. This methodology was applied to the C-Town benchmark network with seven terminal tanks (Tank 1 to Tank 7) over a 7-day simulation period, and revealed that when considering only hydraulic connectivity, Tank 1 consistently ranked as the most resilient tank, while Tank 4 was the least resilient, reflecting their differences in network connectivity and susceptibility to head loss. When integrating all three indicators into the composite resilience score, Tank 1 remained the most resilient, while Tank 4 continued to rank as one of the least resilient tanks, confirming the stability of the assessment and highlighting the influence of both structural and operational factors on overall resilience. The proposed framework provides a structured approach for evaluating reservoir-to-tank resilience and can support decision-makers in prioritizing network reinforcements and developing targeted mitigation strategies to enhance long-term water security.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"274 ","pages":"Article 112384"},"PeriodicalIF":11.0,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146192464","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}
Deficiencies in cross-departmental coordination frequently lead to resource misallocation and critical delays, thereby undermining the overall reliability of earthquake emergency responses. To address the limitations of existing static models in capturing stochastic interagency dynamics, this study develops a formal analytical framework based on stochastic Petri nets. The framework conceptualizes "collaboration reliability" as the system’s capacity to sustain stable resource and information flows under disaster-induced stress. Using the Ms6.2 Jishishan earthquake in China as a validation case, the study reconstructs complex emergency response activities into structured SPN models encompassing four primary collaboration modes. A novel collaborative efficiency index is then introduced to integrate busy place probabilities and transition utilization rates, thereby quantifying the dynamic coupling between resource availability and task execution. Quantitative results reveal significant efficiency disparities across subsystems, identifying material transportation coordination as a critical operational bottleneck. Dynamic optimization further suggests ranges of optimal rates for critical activities: 0.2–0.3 events/hour for transport and rescue; 0.4–0.5 for casualty treatment, and 0.3–0.4 for road accessibility and emergency communications. These ranges ensure that critical tasks are performed at rates conducive to successful outcomes. Overall, the proposed framework offers a mathematically rigorous tool for diagnosing coordination failures and deriving data-driven strategies to enhance collaborative reliability in future seismic events.
{"title":"Emergency Response Reliability: An SPN-based framework for cross-departmental collaboration efficiency and dynamic optimization","authors":"Zongxi Qu , Yuyue Zhang , Zhifa Wu , Yunzhong Luo , Yongzhong Sha","doi":"10.1016/j.ress.2026.112278","DOIUrl":"10.1016/j.ress.2026.112278","url":null,"abstract":"<div><div>Deficiencies in cross-departmental coordination frequently lead to resource misallocation and critical delays, thereby undermining the overall reliability of earthquake emergency responses. To address the limitations of existing static models in capturing stochastic interagency dynamics, this study develops a formal analytical framework based on stochastic Petri nets. The framework conceptualizes \"collaboration reliability\" as the system’s capacity to sustain stable resource and information flows under disaster-induced stress. Using the Ms6.2 Jishishan earthquake in China as a validation case, the study reconstructs complex emergency response activities into structured SPN models encompassing four primary collaboration modes. A novel collaborative efficiency index is then introduced to integrate busy place probabilities and transition utilization rates, thereby quantifying the dynamic coupling between resource availability and task execution. Quantitative results reveal significant efficiency disparities across subsystems, identifying material transportation coordination as a critical operational bottleneck. Dynamic optimization further suggests ranges of optimal rates for critical activities: 0.2–0.3 events/hour for transport and rescue; 0.4–0.5 for casualty treatment, and 0.3–0.4 for road accessibility and emergency communications. These ranges ensure that critical tasks are performed at rates conducive to successful outcomes. Overall, the proposed framework offers a mathematically rigorous tool for diagnosing coordination failures and deriving data-driven strategies to enhance collaborative reliability in future seismic events.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"273 ","pages":"Article 112278"},"PeriodicalIF":11.0,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146174760","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: 2026-02-04DOI: 10.1016/j.ress.2026.112353
Yaohui Guo , Ying Chen , Yingyi Li
Phased-mission systems (PMSs) consist of multiple sequential phases, where variations in system configurations, environmental conditions, and load levels across phases lead to complex failure coupling effects that challenge accurate reliability assessment. This paper proposes a reliability modelling and assessment framework for PMSs considering failure coupling effects between mission phases based on the Failure-Coupling-based Binary Decision Diagram (FC-BDD). The framework employs logical structure modeling rules to implement hierarchical modeling from the failure mechanism layer to the system layer and further to the mission phase layer, accurately capturing both intra-layer node relationships and inter-layer dependencies. In addition, analytical calculation rules for node associations are defined to enable quantitative reliability assessment of the system. Finally, the proposed method is applied to the ignition electronic control unit (PS-IECU) of a reusable deep-space propulsion system, demonstrating its effectiveness in reliability modeling and assessment. The study also reveals that neglecting coupling effects across mission phases can lead to cumulative errors in reliability assessment and hinder the identification and optimization of system-critical vulnerabilities.
{"title":"Reliability modelling and assessment of PMSs considering failure coupling effect between missions","authors":"Yaohui Guo , Ying Chen , Yingyi Li","doi":"10.1016/j.ress.2026.112353","DOIUrl":"10.1016/j.ress.2026.112353","url":null,"abstract":"<div><div>Phased-mission systems (PMSs) consist of multiple sequential phases, where variations in system configurations, environmental conditions, and load levels across phases lead to complex failure coupling effects that challenge accurate reliability assessment. This paper proposes a reliability modelling and assessment framework for PMSs considering failure coupling effects between mission phases based on the Failure-Coupling-based Binary Decision Diagram (FC-BDD). The framework employs logical structure modeling rules to implement hierarchical modeling from the failure mechanism layer to the system layer and further to the mission phase layer, accurately capturing both intra-layer node relationships and inter-layer dependencies. In addition, analytical calculation rules for node associations are defined to enable quantitative reliability assessment of the system. Finally, the proposed method is applied to the ignition electronic control unit (PS-IECU) of a reusable deep-space propulsion system, demonstrating its effectiveness in reliability modeling and assessment. The study also reveals that neglecting coupling effects across mission phases can lead to cumulative errors in reliability assessment and hinder the identification and optimization of system-critical vulnerabilities.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"273 ","pages":"Article 112353"},"PeriodicalIF":11.0,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146174851","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}
Post-earthquake recovery of electric power networks (EPNs) is critical to community resilience. Traditional recovery processes often rely on prolonged and imprecise manual inspections for damage diagnosis, leading to suboptimal repair prioritization and extended service disruptions. Seismic Structural Health Monitoring (SSHM) offers the potential to expedite post-earthquake recovery by enabling more accurate and timely damage assessment. However, the deployment of SSHM comes with a cost and the quantifiable benefit of SSHM in terms of system-level resilience remains underexplored. This study develops an integrated probabilistic simulation framework to quantify the system-level value of SSHM in enhancing EPN resilience. The framework incorporates damage simulations based on EPN configuration, seismic hazard, fragility function, and damage-functionality mapping models, along with recovery simulations considering repair scheduling, resource constraints, transfer and repair durations. System functionality is evaluated via graph-based island detection and optimal power flow analysis under electrical constraints. Resilience is quantified using the Lack of Resilience (LoR) metric derived from the time-evolution functionality restoration curve. The effect of SSHM is incorporated by altering the quality of damage information used to create repair schedules. Specifically, different monitoring scenarios (e.g., no-SSHM baseline, partial SSHM, and full SSHM with various assessing accuracy levels) are modelled using observation matrices that simulate misclassification of component damage states. The results demonstrate that improved damage awareness enabled by SSHM significantly accelerates recovery and reduces LoR by up to 21%. This study provides a quantitative foundation for evaluating the system-level resilience benefits of SSHM and guiding evidence-based sensor investment decisions for critical infrastructures.
{"title":"Quantifying the value of seismic structural health monitoring for post-earthquake recovery of electric power system in terms of resilience enhancement","authors":"Huangbin Liang , Beatriz Moya , Francisco Chinesta , Eleni Chatzi","doi":"10.1016/j.ress.2026.112292","DOIUrl":"10.1016/j.ress.2026.112292","url":null,"abstract":"<div><div>Post-earthquake recovery of electric power networks (EPNs) is critical to community resilience. Traditional recovery processes often rely on prolonged and imprecise manual inspections for damage diagnosis, leading to suboptimal repair prioritization and extended service disruptions. Seismic Structural Health Monitoring (SSHM) offers the potential to expedite post-earthquake recovery by enabling more accurate and timely damage assessment. However, the deployment of SSHM comes with a cost and the quantifiable benefit of SSHM in terms of system-level resilience remains underexplored. This study develops an integrated probabilistic simulation framework to quantify the system-level value of SSHM in enhancing EPN resilience. The framework incorporates damage simulations based on EPN configuration, seismic hazard, fragility function, and damage-functionality mapping models, along with recovery simulations considering repair scheduling, resource constraints, transfer and repair durations. System functionality is evaluated via graph-based island detection and optimal power flow analysis under electrical constraints. Resilience is quantified using the Lack of Resilience (LoR) metric derived from the time-evolution functionality restoration curve. The effect of SSHM is incorporated by altering the quality of damage information used to create repair schedules. Specifically, different monitoring scenarios (e.g., no-SSHM baseline, partial SSHM, and full SSHM with various assessing accuracy levels) are modelled using observation matrices that simulate misclassification of component damage states. The results demonstrate that improved damage awareness enabled by SSHM significantly accelerates recovery and reduces LoR by up to 21%. This study provides a quantitative foundation for evaluating the system-level resilience benefits of SSHM and guiding evidence-based sensor investment decisions for critical infrastructures.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"273 ","pages":"Article 112292"},"PeriodicalIF":11.0,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146174757","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.2026.112337
M. Omair Nawaz , M. Qamar Zia , Taimur Ali Shams
Human error remains a major source of reliability and safety risk in aviation maintenance, particularly in military operations where task complexity and operational pressure are unavoidable. Despite continued advancements in technical reliability, the mechanisms through which working conditions and cognitive demands translate into maintenance error, remain insufficiently understood. In particular, the combined influence of systemic factors, cognitive workload, and individual differences has received limited empirical attention. This study examines the effect of Performance Shaping Factors (PSFs) on human error in military aviation maintenance, considering cognitive workload as a mediating mechanism and Error Orientation (EO) as a moderating factor.
Survey data from 282 military aviation maintenance personnel were analyzed using structural equation modeling. The results show that adverse PSFs significantly increase both cognitive workload and the likelihood of maintenance error. Cognitive workload partially mediates this relationship, indicating that increased mental demand is a key pathway through which unfavorable system conditions degrade maintenance reliability. Error Orientation moderates both direct and indirect effects. Personnel with lower EO are more susceptible to workload-related error.
These findings extend human reliability analysis by explaining when and why maintenance errors are most likely to occur. The results support integrated safety management strategies that combine system design improvements, workload control, and targeted personnel development to enhance reliability in high-risk aviation maintenance environments.
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