{"title":"基于故障树和多态模糊贝叶斯网络的水下浮动隧道安全风险评估","authors":"","doi":"10.1016/j.ocecoaman.2024.107355","DOIUrl":null,"url":null,"abstract":"<div><p>To assess the global security risks of the submerged floating tunnel (SFT) in marine environments during operation and provide a basis for risk control, a security risk assessment method using a multistate fuzzy Bayesian network (MFBN) considering complex disaster-inducing factors is proposed. A fault tree model of SFT security risk is established to analyze the causal relationships between global risk and influence factors such as structural components and environmental loads. For root nodes, fuzzy probabilities for each state are obtained through expert knowledge. An improved similarity aggregation method is proposed to integrate expert opinions, mitigating the impact of significant option discrepancies. For non-root nodes, the Leaky Noisy-Max model is used to calculate complex conditional probabilities within the SFT. The probabilities of various security risk states and key risk factors could be determined through reasoning by MFBN. Additionally, a risk prediction method that incorporates domain expert opinions and leverages the BN's ability of updating node probabilities with new information was developed to forecast the security risks of the SFT under wave and current loads.</p></div>","PeriodicalId":54698,"journal":{"name":"Ocean & Coastal Management","volume":null,"pages":null},"PeriodicalIF":4.8000,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Security risk assessment of submerged floating tunnel based on fault tree and multistate fuzzy Bayesian network\",\"authors\":\"\",\"doi\":\"10.1016/j.ocecoaman.2024.107355\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>To assess the global security risks of the submerged floating tunnel (SFT) in marine environments during operation and provide a basis for risk control, a security risk assessment method using a multistate fuzzy Bayesian network (MFBN) considering complex disaster-inducing factors is proposed. A fault tree model of SFT security risk is established to analyze the causal relationships between global risk and influence factors such as structural components and environmental loads. For root nodes, fuzzy probabilities for each state are obtained through expert knowledge. An improved similarity aggregation method is proposed to integrate expert opinions, mitigating the impact of significant option discrepancies. For non-root nodes, the Leaky Noisy-Max model is used to calculate complex conditional probabilities within the SFT. The probabilities of various security risk states and key risk factors could be determined through reasoning by MFBN. Additionally, a risk prediction method that incorporates domain expert opinions and leverages the BN's ability of updating node probabilities with new information was developed to forecast the security risks of the SFT under wave and current loads.</p></div>\",\"PeriodicalId\":54698,\"journal\":{\"name\":\"Ocean & Coastal Management\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":4.8000,\"publicationDate\":\"2024-09-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Ocean & Coastal Management\",\"FirstCategoryId\":\"93\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0964569124003405\",\"RegionNum\":2,\"RegionCategory\":\"环境科学与生态学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"OCEANOGRAPHY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Ocean & Coastal Management","FirstCategoryId":"93","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0964569124003405","RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"OCEANOGRAPHY","Score":null,"Total":0}
Security risk assessment of submerged floating tunnel based on fault tree and multistate fuzzy Bayesian network
To assess the global security risks of the submerged floating tunnel (SFT) in marine environments during operation and provide a basis for risk control, a security risk assessment method using a multistate fuzzy Bayesian network (MFBN) considering complex disaster-inducing factors is proposed. A fault tree model of SFT security risk is established to analyze the causal relationships between global risk and influence factors such as structural components and environmental loads. For root nodes, fuzzy probabilities for each state are obtained through expert knowledge. An improved similarity aggregation method is proposed to integrate expert opinions, mitigating the impact of significant option discrepancies. For non-root nodes, the Leaky Noisy-Max model is used to calculate complex conditional probabilities within the SFT. The probabilities of various security risk states and key risk factors could be determined through reasoning by MFBN. Additionally, a risk prediction method that incorporates domain expert opinions and leverages the BN's ability of updating node probabilities with new information was developed to forecast the security risks of the SFT under wave and current loads.
期刊介绍:
Ocean & Coastal Management is the leading international journal dedicated to the study of all aspects of ocean and coastal management from the global to local levels.
We publish rigorously peer-reviewed manuscripts from all disciplines, and inter-/trans-disciplinary and co-designed research, but all submissions must make clear the relevance to management and/or governance issues relevant to the sustainable development and conservation of oceans and coasts.
Comparative studies (from sub-national to trans-national cases, and other management / policy arenas) are encouraged, as are studies that critically assess current management practices and governance approaches. Submissions involving robust analysis, development of theory, and improvement of management practice are especially welcome.