Pub Date : 2025-03-19DOI: 10.1016/j.jnlssr.2024.12.005
Ali Aghazadeh Ardebili , Marco Boscolo , Antonella Longo , Mahdad Pourmadadkar , Antonio Ficarella , Elio Padoano
Critical infrastructures (CIs) embody cyber-physical-social systems (CPSSs) where physical entities are integrated with cyber components, shaping service delivery through end-user behavior. The seamless operation of CIs is vital for society, and the CPSS resilience relies on interdependencies with AI-integrated technologies. The complexity of the system, and the interconnections with other infrastructures, along with the socio-technical transition towards digitization raised the necessity of implementing Resilience Engineering. This motivates exploration of the scientific literature on resilience key performance indicators (R-KPIs) which support strategies for ensuring service continuity. Therefore, this article aims to identify R-KPIs for AI-integrated CIs and prioritize the extracted R-KPIs using a hybrid Multi-Criteria Decision-Making (MCDM) approach. The results show the importance of employing R-KPIs that measure risk probability, energy self-sufficiency level of the system under study, and performance indicators including functionality loss, recovery time, and minimum performance level after disturbance as the most effective R-KPIs in the domain of this study. After identifying and prioritizing the R-KPIs, a general framework is proposed to employ these R-KPIs in modeling the resilience of a CPS. Finally, a case study demonstrates the implementation of the framework and KPIs in a real-life scenario.
{"title":"Resilience in Cyber-Physical Infrastructures: R-KPI prioritization, framework development, and case study insights","authors":"Ali Aghazadeh Ardebili , Marco Boscolo , Antonella Longo , Mahdad Pourmadadkar , Antonio Ficarella , Elio Padoano","doi":"10.1016/j.jnlssr.2024.12.005","DOIUrl":"10.1016/j.jnlssr.2024.12.005","url":null,"abstract":"<div><div>Critical infrastructures (CIs) embody cyber-physical-social systems (CPSSs) where physical entities are integrated with cyber components, shaping service delivery through end-user behavior. The seamless operation of CIs is vital for society, and the CPSS resilience relies on interdependencies with AI-integrated technologies. The complexity of the system, and the interconnections with other infrastructures, along with the socio-technical transition towards digitization raised the necessity of implementing Resilience Engineering. This motivates exploration of the scientific literature on resilience key performance indicators (R-KPIs) which support strategies for ensuring service continuity. Therefore, this article aims to identify R-KPIs for AI-integrated CIs and prioritize the extracted R-KPIs using a hybrid Multi-Criteria Decision-Making (MCDM) approach. The results show the importance of employing R-KPIs that measure risk probability, energy self-sufficiency level of the system under study, and performance indicators including functionality loss, recovery time, and minimum performance level after disturbance as the most effective R-KPIs in the domain of this study. After identifying and prioritizing the R-KPIs, a general framework is proposed to employ these R-KPIs in modeling the resilience of a CPS. Finally, a case study demonstrates the implementation of the framework and KPIs in a real-life scenario.</div></div>","PeriodicalId":62710,"journal":{"name":"安全科学与韧性(英文)","volume":"6 3","pages":"Article 100194"},"PeriodicalIF":3.7,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144491290","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-15DOI: 10.1016/j.jnlssr.2025.01.001
Nuo Yong , Shunjiang Ni , Shifei Shen
This paper explores the challenges of controlling complex metro systems, which are influenced by uncertain and uncontrollable large passenger flow impacts. Traditionally, flow-limiting measures during peak periods have been based on experience rather than scientific theory. To bridge this gap, we introduce a novel network analysis method inspired by control centrality theory. This approach assesses the impact of traffic loads from single or multiple sources on any node within the metro network. Our method provides a scientific basis for operators to develop policies for managing overloaded traffic, enhancing both safety and efficiency in metro system operations.
{"title":"A method of characterizing the impact of traffic load on metro system from the control centrality","authors":"Nuo Yong , Shunjiang Ni , Shifei Shen","doi":"10.1016/j.jnlssr.2025.01.001","DOIUrl":"10.1016/j.jnlssr.2025.01.001","url":null,"abstract":"<div><div>This paper explores the challenges of controlling complex metro systems, which are influenced by uncertain and uncontrollable large passenger flow impacts. Traditionally, flow-limiting measures during peak periods have been based on experience rather than scientific theory. To bridge this gap, we introduce a novel network analysis method inspired by control centrality theory. This approach assesses the impact of traffic loads from single or multiple sources on any node within the metro network. Our method provides a scientific basis for operators to develop policies for managing overloaded traffic, enhancing both safety and efficiency in metro system operations.</div></div>","PeriodicalId":62710,"journal":{"name":"安全科学与韧性(英文)","volume":"6 3","pages":"Article 100192"},"PeriodicalIF":3.7,"publicationDate":"2025-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144522276","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-11DOI: 10.1016/j.jnlssr.2024.11.005
Lu Chen , Jianming Zhu , Guoqing Wang
Considering the information interaction in the virtual–real network, this paper introduces a novel three-layer model that explores the integrated influence between virtual and real networks. Existing models often fail to capture the dynamic feedback between these networks and do not effectively simulate integrated decision-making processes. Focusing on the Facility Service Balance Problem, we aim to optimize resource allocation and information diffusion in response to real-world events like natural disasters or large-scale activities. Based on the Linear Threshold model, the Feedback Linear Threshold model, which incorporates feedback mechanisms between virtual and real networks and integrates both original and feedback information in the activation function of nodes, has been proposed to better simulate the information feedback and integrated decision-making process. Then, combined with location-based interpersonal and online social networks, a comprehensive framework that models decision-making processes without direct influence between decision-makers has been provided, focusing on the decision-making of individuals influenced by cumulative information, ultimately maximizing the facility service efficiency. Finally, conduct experiments have been conducted, using two types of data to test the general effectiveness of the feedback mechanism.
{"title":"Facility service balance problem with information feedback mechanism in the virtual–real interaction network","authors":"Lu Chen , Jianming Zhu , Guoqing Wang","doi":"10.1016/j.jnlssr.2024.11.005","DOIUrl":"10.1016/j.jnlssr.2024.11.005","url":null,"abstract":"<div><div>Considering the information interaction in the virtual–real network, this paper introduces a novel three-layer model that explores the integrated influence between virtual and real networks. Existing models often fail to capture the dynamic feedback between these networks and do not effectively simulate integrated decision-making processes. Focusing on the Facility Service Balance Problem, we aim to optimize resource allocation and information diffusion in response to real-world events like natural disasters or large-scale activities. Based on the Linear Threshold model, the Feedback Linear Threshold model, which incorporates feedback mechanisms between virtual and real networks and integrates both original and feedback information in the activation function of nodes, has been proposed to better simulate the information feedback and integrated decision-making process. Then, combined with location-based interpersonal and online social networks, a comprehensive framework that models decision-making processes without direct influence between decision-makers has been provided, focusing on the decision-making of individuals influenced by cumulative information, ultimately maximizing the facility service efficiency. Finally, conduct experiments have been conducted, using two types of data to test the general effectiveness of the feedback mechanism.</div></div>","PeriodicalId":62710,"journal":{"name":"安全科学与韧性(英文)","volume":"6 3","pages":"Article 100191"},"PeriodicalIF":3.7,"publicationDate":"2025-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144306988","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-10DOI: 10.1016/j.jnlssr.2025.01.002
Fengju Shang , Jiaqing Zhang , Xin Liu , Yi Guo , Yunpeng Yang , Lilong Nie , Kaiyuan Li
As the core equipment in power systems, ultra-high voltage (UHV) transformers pose a high fire risk. The compressed-air foam spray nozzle is a novel end-release device that, due to its high efficiency and excellent suppression effect on oil-based fires, has been increasingly applied in UHV substations. This study is based on a self-developed experimental platform for compressed-air foam firefighting systems. To meet practical engineering needs, the longitudinal maximum of the contour line at the threshold of 12 L/(min·m²) was selected as the spray range. The study systematically explored the factors influencing the spray range of the spray nozzle. Experimental results revealed that when the hole elevation angle was 60°, the average spray range increased by 59 % compared to 0°. When the nozzle aperture was 10 mm and the outlet pressure was 0.15 MPa, the spray range improved by 17 %. Additionally, as the outlet pressure increased, the foam spray range grew significantly, with a 35.2 % increase at 0.3 MPa compared to 0.1 MPa, indicating that the outlet pressure had a substantial effect on the spray range. To predict the spray range increase, an empirical model is developed for the outlet pressure versus the spray range. After analyzing the above three influencing factors, all the data of various working conditions were integrated into a single dataset, a prediction model of the spray range was established, and the importance of the factors affecting the range was ranked. These findings provide a theoretical foundation for the optimized design and engineering application of compressed-air foam systems (CAFSs).
{"title":"Experimental study on enhancing the foam spray range of compressed air foam nozzle","authors":"Fengju Shang , Jiaqing Zhang , Xin Liu , Yi Guo , Yunpeng Yang , Lilong Nie , Kaiyuan Li","doi":"10.1016/j.jnlssr.2025.01.002","DOIUrl":"10.1016/j.jnlssr.2025.01.002","url":null,"abstract":"<div><div>As the core equipment in power systems, ultra-high voltage (UHV) transformers pose a high fire risk. The compressed-air foam spray nozzle is a novel end-release device that, due to its high efficiency and excellent suppression effect on oil-based fires, has been increasingly applied in UHV substations. This study is based on a self-developed experimental platform for compressed-air foam firefighting systems. To meet practical engineering needs, the longitudinal maximum of the contour line at the threshold of 12 L/(min·m²) was selected as the spray range. The study systematically explored the factors influencing the spray range of the spray nozzle. Experimental results revealed that when the hole elevation angle was 60°, the average spray range increased by 59 % compared to 0°. When the nozzle aperture was 10 mm and the outlet pressure was 0.15 MPa, the spray range improved by 17 %. Additionally, as the outlet pressure increased, the foam spray range grew significantly, with a 35.2 % increase at 0.3 MPa compared to 0.1 MPa, indicating that the outlet pressure had a substantial effect on the spray range. To predict the spray range increase, an empirical model is developed for the outlet pressure versus the spray range. After analyzing the above three influencing factors, all the data of various working conditions were integrated into a single dataset, a prediction model of the spray range was established, and the importance of the factors affecting the range was ranked. These findings provide a theoretical foundation for the optimized design and engineering application of compressed-air foam systems (CAFSs).</div></div>","PeriodicalId":62710,"journal":{"name":"安全科学与韧性(英文)","volume":"6 3","pages":"Article 100193"},"PeriodicalIF":3.7,"publicationDate":"2025-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144366812","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-10DOI: 10.1016/j.jnlssr.2025.01.003
Xuhong Jia , Shupei Tang , Quanyi Liu
A numerical simulation study was conducted to analyze flashover in a full-scale aircraft cargo compartment, utilizing FDS (Fire Dynamics Simulator) and PyroSim (visual modeling) software. The study aims to: (i) examine how varying heat release rates (HRR), pressures, and vent sizes influence the hot gas layer temperature during flashover in confined spaces, and (ii) establish a semi-empirical model to predict flashover. Experimental results indicate that when the vent size is 1.86 m × 1.06 m and the pressure is 101 kPa, the minimum hot gas layer temperature required to trigger flashover is approximately 410 °C. When the pressure is reduced to 80 kPa and 60 kPa, the critical temperature increases to approximately 436 °C and 460 °C, respectively. These findings provide critical temperature benchmarks for predicting flashover in aircraft cargo fires. Furthermore, a semi-empirical engineering calculation model was developed to predict the hot gas layer temperature under various conditions during flashover. Validation against experimental data from the literature demonstrated good agreement (deviation of ≈20 %), confirming the model's applicability in diverse scenarios.
{"title":"Numerical study on flashover in aircraft cargo under varying pressure and ventilation conditions","authors":"Xuhong Jia , Shupei Tang , Quanyi Liu","doi":"10.1016/j.jnlssr.2025.01.003","DOIUrl":"10.1016/j.jnlssr.2025.01.003","url":null,"abstract":"<div><div>A numerical simulation study was conducted to analyze flashover in a full-scale aircraft cargo compartment, utilizing FDS (Fire Dynamics Simulator) and PyroSim (visual modeling) software. The study aims to: (i) examine how varying heat release rates (HRR), pressures, and vent sizes influence the hot gas layer temperature during flashover in confined spaces, and (ii) establish a semi-empirical model to predict flashover. Experimental results indicate that when the vent size is 1.86 m × 1.06 m and the pressure is 101 kPa, the minimum hot gas layer temperature required to trigger flashover is approximately 410 °C. When the pressure is reduced to 80 kPa and 60 kPa, the critical temperature increases to approximately 436 °C and 460 °C, respectively. These findings provide critical temperature benchmarks for predicting flashover in aircraft cargo fires. Furthermore, a semi-empirical engineering calculation model was developed to predict the hot gas layer temperature under various conditions during flashover. Validation against experimental data from the literature demonstrated good agreement (deviation of ≈20 %), confirming the model's applicability in diverse scenarios.</div></div>","PeriodicalId":62710,"journal":{"name":"安全科学与韧性(英文)","volume":"6 3","pages":"Article 100195"},"PeriodicalIF":3.7,"publicationDate":"2025-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144517478","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-24DOI: 10.1016/j.jnlssr.2024.10.003
Abroon Qazi
Understanding the relationship between corruption and Sustainable Development Goals (SDGs) is essential for comprehensively addressing sustainable development challenges. Corruption, with its damaging impact on governance, institutions, and public trust, poses a substantial barrier to achieving the SDGs. This study investigates the interconnections between corruption risk at the country level and the risks associated with achieving the SDGs. A Bayesian belief network model is developed using two datasets related to country-level sustainability and corruption performance. The model yields an 86.3 % accuracy in predicting outcomes for the two extreme levels of corruption risk. The findings indicate that the “high risk” state of corruption can significantly hinder progress on the “good health and well-being,” “zero hunger”, and “peace, justice and strong institutions” SDGs. Conversely, the “low risk” state of corruption can significantly enhance performance on the “sustainable cities and communities”, “zero hunger”, and “no poverty” SDGs. This study's exploration of the interconnected relationship between corruption and SDG risks offers valuable insights for policymakers. Its contribution lies in examining the dependencies between corruption and sustainability from a risk science perspective, capturing interactions across all 17 SDGs.
{"title":"Risk forecasting for shortfalls in achieving sustainable development goals: A corruption perspective","authors":"Abroon Qazi","doi":"10.1016/j.jnlssr.2024.10.003","DOIUrl":"10.1016/j.jnlssr.2024.10.003","url":null,"abstract":"<div><div>Understanding the relationship between corruption and Sustainable Development Goals (SDGs) is essential for comprehensively addressing sustainable development challenges. Corruption, with its damaging impact on governance, institutions, and public trust, poses a substantial barrier to achieving the SDGs. This study investigates the interconnections between corruption risk at the country level and the risks associated with achieving the SDGs. A Bayesian belief network model is developed using two datasets related to country-level sustainability and corruption performance. The model yields an 86.3 % accuracy in predicting outcomes for the two extreme levels of corruption risk. The findings indicate that the “high risk” state of corruption can significantly hinder progress on the “good health and well-being,” “zero hunger”, and “peace, justice and strong institutions” SDGs. Conversely, the “low risk” state of corruption can significantly enhance performance on the “sustainable cities and communities”, “zero hunger”, and “no poverty” SDGs. This study's exploration of the interconnected relationship between corruption and SDG risks offers valuable insights for policymakers. Its contribution lies in examining the dependencies between corruption and sustainability from a risk science perspective, capturing interactions across all 17 SDGs.</div></div>","PeriodicalId":62710,"journal":{"name":"安全科学与韧性(英文)","volume":"6 2","pages":"Pages 237-249"},"PeriodicalIF":3.7,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143852016","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-17DOI: 10.1016/j.jnlssr.2024.12.004
Funmilayo Ebun Rotimi, Roohollah Kalatehjari, Taofeeq Durojaye Moshood, George Dokyi
Climate change has become a pressing concern, with an alarming increase in flooding events posing significant risks to residential areas worldwide. As land and infrastructure development rapidly evolve, it is crucial to systematically analyze the bibliometric patterns and methodological trends in flood mitigation research, with a specific focus on residential building flood mitigation. This study presents a comprehensive comparative analysis of the bibliometric patterns and methodological trends in flood mitigation research over the past two decades, identifies prevailing gaps, and proposes future research directions to enhance the effectiveness of flood mitigation strategies. Using data from the Scopus database, 441 publications were objectively selected and subjected to metadata analysis. The study identifies top authors, contributing institutions, nations, and the distribution of contributions across different fields and methodologies. The findings emphasize the need for an integrated and interdisciplinary approach to flood reduction research, considering the complex interplay of social, ecological, and physical dimensions in flood risk management. The study reveals the predominance of modeling and simulation approaches, geographic information systems (GIS) remote sensing approaches, and statistical and data-driven approaches as the most widely employed methodologies. Furthermore, it highlights the growing diversity of approaches, with increasing interest in machine learning algorithms and combined methods. Also, this study provides valuable recommendations for future research, emphasizing the importance of developing effective flood-mitigating strategies to enhance community resilience. It advocates for a multidisciplinary and integrated approach, leveraging geospatial technologies, machine learning algorithms, and collaborative methodologies to advance flood mitigation research and practice. Future research should consider exploring additional databases, including Web of Science, EBSCO, IEEE, and Google Scholar, to conduct a more comprehensive review of the available literature. There is need for future studies to conduct in-depth comparative analyses of flood mitigation methodologies, particularly in the context of residential buildings.
气候变化已经成为一个紧迫的问题，洪水事件的惊人增加给世界各地的居民区带来了重大风险。随着土地和基础设施的快速发展，系统分析防洪研究的文献计量模式和方法趋势至关重要，特别是对住宅建筑的防洪研究。本文对近二十年来洪水减灾研究的文献计量模式和方法趋势进行了全面的比较分析，指出了主要的差距，并提出了未来的研究方向，以提高洪水减灾战略的有效性。利用Scopus数据库中的数据，客观选择441篇出版物进行元数据分析。该研究确定了顶级作者、有贡献的机构、国家，以及不同领域和方法的贡献分布。研究结果强调，考虑到洪水风险管理中社会、生态和物理维度的复杂相互作用，需要一种综合和跨学科的方法来减少洪水的研究。研究表明，建模和模拟方法、地理信息系统（GIS）遥感方法、统计和数据驱动方法是最广泛使用的方法。此外，它还强调了方法的多样性，对机器学习算法和组合方法的兴趣越来越大。同时，本研究为未来的研究提供了有价值的建议，强调了制定有效的防洪策略以增强社区抵御能力的重要性。它倡导采用多学科综合方法，利用地理空间技术、机器学习算法和协作方法来推进防洪研究和实践。未来的研究应考虑探索其他数据库，包括Web of Science、EBSCO、IEEE和b谷歌Scholar，以对现有文献进行更全面的综述。未来的研究需要对减轻洪水的方法进行深入的比较分析，特别是在住宅建筑的背景下。
{"title":"A comprehensive review of bibliometric and methodological approaches in flood mitigation studies: Current trends and future directions","authors":"Funmilayo Ebun Rotimi, Roohollah Kalatehjari, Taofeeq Durojaye Moshood, George Dokyi","doi":"10.1016/j.jnlssr.2024.12.004","DOIUrl":"10.1016/j.jnlssr.2024.12.004","url":null,"abstract":"<div><div>Climate change has become a pressing concern, with an alarming increase in flooding events posing significant risks to residential areas worldwide. As land and infrastructure development rapidly evolve, it is crucial to systematically analyze the bibliometric patterns and methodological trends in flood mitigation research, with a specific focus on residential building flood mitigation. This study presents a comprehensive comparative analysis of the bibliometric patterns and methodological trends in flood mitigation research over the past two decades, identifies prevailing gaps, and proposes future research directions to enhance the effectiveness of flood mitigation strategies. Using data from the Scopus database, 441 publications were objectively selected and subjected to metadata analysis. The study identifies top authors, contributing institutions, nations, and the distribution of contributions across different fields and methodologies. The findings emphasize the need for an integrated and interdisciplinary approach to flood reduction research, considering the complex interplay of social, ecological, and physical dimensions in flood risk management. The study reveals the predominance of modeling and simulation approaches, geographic information systems (GIS) remote sensing approaches, and statistical and data-driven approaches as the most widely employed methodologies. Furthermore, it highlights the growing diversity of approaches, with increasing interest in machine learning algorithms and combined methods. Also, this study provides valuable recommendations for future research, emphasizing the importance of developing effective flood-mitigating strategies to enhance community resilience. It advocates for a multidisciplinary and integrated approach, leveraging geospatial technologies, machine learning algorithms, and collaborative methodologies to advance flood mitigation research and practice. Future research should consider exploring additional databases, including Web of Science, EBSCO, IEEE, and Google Scholar, to conduct a more comprehensive review of the available literature. There is need for future studies to conduct in-depth comparative analyses of flood mitigation methodologies, particularly in the context of residential buildings.</div></div>","PeriodicalId":62710,"journal":{"name":"安全科学与韧性(英文)","volume":"6 2","pages":"Pages 280-296"},"PeriodicalIF":3.7,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143873675","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-15DOI: 10.1016/j.jnlssr.2024.12.003
M. Zitouni , M.R.T. Arruda , P. Cantor , F. Branco
This paper presents an extensive experimental investigation campaign concerning the thermal fire reaction of firebrands, as they accumulate on the exterior walls of dwellings, a common occurrence in southern Europe. Three types of wall core layers were studied: bricks, designed according to the Exterior Thermal Insulation Composite Systems (ETICS) methodology, cross-laminated timber (CLT) and normal wood (NW), both utilizing the sandwich methodology. The wall specimens are made of a combination of materials such as three types of mortar (Tria, Sika, and Weber), and various thermal insulation materials, such as agglomerates of composite cork, impermeable membranes, rigid rock wool, fireproof paint, and extruded polystyrene rigid foam (XPS), which are recommended for their good performance against fire and high temperatures. Firebrands are then deposited on the localized surfaces of the wall specimens, and the temperature is recorded in each layer. This study aims to precisely verify the firebrand reaction to fire, including the type of ignition, smoke and droplet production. The insulation capabilities of each insulation and wall system will also be analyzed.
{"title":"Heat penetration and thermal response due to firebrand accumulation on the exterior walls of dwellings","authors":"M. Zitouni , M.R.T. Arruda , P. Cantor , F. Branco","doi":"10.1016/j.jnlssr.2024.12.003","DOIUrl":"10.1016/j.jnlssr.2024.12.003","url":null,"abstract":"<div><div>This paper presents an extensive experimental investigation campaign concerning the thermal fire reaction of firebrands, as they accumulate on the exterior walls of dwellings, a common occurrence in southern Europe. Three types of wall core layers were studied: bricks, designed according to the Exterior Thermal Insulation Composite Systems (ETICS) methodology, cross-laminated timber (CLT) and normal wood (NW), both utilizing the sandwich methodology. The wall specimens are made of a combination of materials such as three types of mortar (Tria, Sika, and Weber), and various thermal insulation materials, such as agglomerates of composite cork, impermeable membranes, rigid rock wool, fireproof paint, and extruded polystyrene rigid foam (XPS), which are recommended for their good performance against fire and high temperatures. Firebrands are then deposited on the localized surfaces of the wall specimens, and the temperature is recorded in each layer. This study aims to precisely verify the firebrand reaction to fire, including the type of ignition, smoke and droplet production. The insulation capabilities of each insulation and wall system will also be analyzed.</div></div>","PeriodicalId":62710,"journal":{"name":"安全科学与韧性(英文)","volume":"6 3","pages":"Article 100189"},"PeriodicalIF":3.7,"publicationDate":"2025-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144517477","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-15DOI: 10.1016/j.jnlssr.2024.12.002
Yulong Zhu, Wei Tian, Xuhong Jia, Quanyi Liu
Aviation transport infrastructure is key to international commerce and cultural exchange, with any emergency potentially causing major impacts on contemporary society. With advancements in technology and growing societal needs, modern airports are evolving toward smarter and more integrated systems. While transportation engineers and planners aim to bolster resilience in subsystems and transport management with new technologies and diverse strategies for effective risk management, the growing complexity of disaster-inducing factors and fire dynamics in airport systems somewhat diminishes the accuracy of existing risk vulnerability analyses. It drives the demand for enhanced firefighting resilience. This study discusses assessment and improvement strategies for enhancing the resilience of airport firefighting systems in the context of smart airports. Specifically, we analyze the resilience characteristics of airport firefighting systems, which can be subdivided into four dimensions: stability capability, resistance capability, recovery capability, and adaptability capability. Furthermore, by integrating human, mechanical, environmental, and managerial elements, a comprehensive resilience evaluation indicator system is constructed. We propose a modified composite weight calculation framework that innovatively introduces genetic algorithm (GA) in the weight combination process to simplify the model into a constrained minimization problem from a mathematical perspective, thus making the ranking results reflect ordinal and intensity information. The findings highlight the significance of technological advancements, emergency response capabilities, expertise in fire management, cross-departmental collaborative responses, personnel psychological quality, and accident investigation skills in enhancing the resilience of airport firefighting systems. Although the comprehensive evaluation model based on expert knowledge still focuses primarily on resistance, the criterion of adaptation capability has a more pronounced increasing trend in weight under correction, highlighting its significant role and potential in future airport firefighting resilience indicators. This research aims to offer guidance to the aviation sector and managers for devising appropriate protection strategies, thereby improving public safety at airports.
{"title":"Optimizing firefighting resilience in airports through genetic algorithms and decision-making frameworks","authors":"Yulong Zhu, Wei Tian, Xuhong Jia, Quanyi Liu","doi":"10.1016/j.jnlssr.2024.12.002","DOIUrl":"10.1016/j.jnlssr.2024.12.002","url":null,"abstract":"<div><div>Aviation transport infrastructure is key to international commerce and cultural exchange, with any emergency potentially causing major impacts on contemporary society. With advancements in technology and growing societal needs, modern airports are evolving toward smarter and more integrated systems. While transportation engineers and planners aim to bolster resilience in subsystems and transport management with new technologies and diverse strategies for effective risk management, the growing complexity of disaster-inducing factors and fire dynamics in airport systems somewhat diminishes the accuracy of existing risk vulnerability analyses. It drives the demand for enhanced firefighting resilience. This study discusses assessment and improvement strategies for enhancing the resilience of airport firefighting systems in the context of smart airports. Specifically, we analyze the resilience characteristics of airport firefighting systems, which can be subdivided into four dimensions: stability capability, resistance capability, recovery capability, and adaptability capability. Furthermore, by integrating human, mechanical, environmental, and managerial elements, a comprehensive resilience evaluation indicator system is constructed. We propose a modified composite weight calculation framework that innovatively introduces genetic algorithm (GA) in the weight combination process to simplify the model into a constrained minimization problem from a mathematical perspective, thus making the ranking results reflect ordinal and intensity information. The findings highlight the significance of technological advancements, emergency response capabilities, expertise in fire management, cross-departmental collaborative responses, personnel psychological quality, and accident investigation skills in enhancing the resilience of airport firefighting systems. Although the comprehensive evaluation model based on expert knowledge still focuses primarily on resistance, the criterion of adaptation capability has a more pronounced increasing trend in weight under correction, highlighting its significant role and potential in future airport firefighting resilience indicators. This research aims to offer guidance to the aviation sector and managers for devising appropriate protection strategies, thereby improving public safety at airports.</div></div>","PeriodicalId":62710,"journal":{"name":"安全科学与韧性(英文)","volume":"6 2","pages":"Pages 212-225"},"PeriodicalIF":3.7,"publicationDate":"2025-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143838398","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Accurate crime prediction is crucial for the proactive allocation of law enforcement resources and ensuring urban safety. A major challenge in achieving accurate predictions lies in identifying generalized patterns of criminal behavior from spatiotemporal features in crime data. Additionally, the inherent randomness and volatility of crime data at the spatiotemporal level introduce noise, which can mislead prediction models. While many effective spatiotemporal crime prediction methods have been proposed, most overlook this issue, reducing their ability to generalize. In this paper, we introduce a novel deep learning-based model, adaptive-GCNLSTM (Ada-GCNLSTM). Specifically, in the spatial feature extraction module, we enhance the model's ability to capture crime spatial distributions by leveraging graph convolutional networks to model spatial dependencies in conjunction with the maximum mean discrepancy to extract the universal features of crime data. We then incorporate a memory network based on long short-term memory network to capture the underlying relationships between temporal features. Through extensive experiments, our model demonstrates an average improvement of 11.7% in mean absolute error and 2.7% in root mean squared error across the three datasets, outperforming the best baseline model. These results underscore the effectiveness of our approach in enhancing crime prediction accuracy.
{"title":"Ada-GCNLSTM: An adaptive urban crime spatiotemporal prediction model","authors":"Miaoxuan Shan , Chunlin Ye , Peng Chen , Shufan Peng","doi":"10.1016/j.jnlssr.2024.11.003","DOIUrl":"10.1016/j.jnlssr.2024.11.003","url":null,"abstract":"<div><div>Accurate crime prediction is crucial for the proactive allocation of law enforcement resources and ensuring urban safety. A major challenge in achieving accurate predictions lies in identifying generalized patterns of criminal behavior from spatiotemporal features in crime data. Additionally, the inherent randomness and volatility of crime data at the spatiotemporal level introduce noise, which can mislead prediction models. While many effective spatiotemporal crime prediction methods have been proposed, most overlook this issue, reducing their ability to generalize. In this paper, we introduce a novel deep learning-based model, adaptive-GCNLSTM (Ada-GCNLSTM). Specifically, in the spatial feature extraction module, we enhance the model's ability to capture crime spatial distributions by leveraging graph convolutional networks to model spatial dependencies in conjunction with the maximum mean discrepancy to extract the universal features of crime data. We then incorporate a memory network based on long short-term memory network to capture the underlying relationships between temporal features. Through extensive experiments, our model demonstrates an average improvement of 11.7% in mean absolute error and 2.7% in root mean squared error across the three datasets, outperforming the best baseline model. These results underscore the effectiveness of our approach in enhancing crime prediction accuracy.</div></div>","PeriodicalId":62710,"journal":{"name":"安全科学与韧性(英文)","volume":"6 2","pages":"Pages 226-236"},"PeriodicalIF":3.7,"publicationDate":"2025-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143852015","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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