An Integrated Framework for Analyzing Risk Influence Factors of Inland Waterway Transport Based on Interpretive Structural Models and Bayesian Networks
AbstractHistorical accident data provides valuable insights into the causes of maritime accidents. To investigate the effect of factors on maritime safety through accident analysis, this study collected 238 accidents that occurred in the mainstream of the Yangtze River from 2016 to 2021. The data fe...
Gespeichert in:
| Veröffentlicht in: | ASCE-ASME journal of risk and uncertainty in engineering systems. Part A, Civil Engineering Civil Engineering, 2024-09, Vol.10 (3) |
|---|---|
| Hauptverfasser: | , , , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | |
|---|---|
| container_issue | 3 |
| container_start_page | |
| container_title | ASCE-ASME journal of risk and uncertainty in engineering systems. Part A, Civil Engineering |
| container_volume | 10 |
| creator | Guo, Tao Xie, Lei Zhang, Jinfen Zhao, Jianwei Zhou, Heyu |
| description | AbstractHistorical accident data provides valuable insights into the causes of maritime accidents. To investigate the effect of factors on maritime safety through accident analysis, this study collected 238 accidents that occurred in the mainstream of the Yangtze River from 2016 to 2021. The data features that reflect the frequency of risk influence factors (RIFs) are identified, and principal component analysis (PCA) is used to reduce the feature dimension of the RIFs. Furthermore, an interpretive structure model is constructed to analyze the relevance and hierarchy of the RIFs. The parameters of the network model are learned using the data set of accident cases, and the conditional probability of each node is obtained, based on these, the Bayesian network model of RIFs can be constructed. The sensitivity analysis reveals that all types of accidents are the location of incident, ship type, ship age, hull condition, and channel environment. Four cases are used to verify the effectiveness of the proposed model. This research provides theoretical support for taking measures to prevent accidents and control risks. |
| doi_str_mv | 10.1061/AJRUA6.RUENG-1282 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_journals_3066180439</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>3066180439</sourcerecordid><originalsourceid>FETCH-LOGICAL-a318t-f6be0dce9a7808a90e043a36093aeb502ad201c495354e158bcae60a74607e413</originalsourceid><addsrcrecordid>eNp1kU1Lw0AQhoMoWNQf4G3Bc-puNtkkx1SsH9QKtcVjmCaTkjZm6-xGiX_Ev2tiBL142mF5n2cYXsc5F3wsuBKXyf1ilajxYnU9v3GFF3kHzsiToXJD5XuHf-Zj58yYLedc-LEng3jkfCY1u6stbggs5mxK8ILvmnas0MSSGqr2o6w3bFGaXZcrqgbrDNkUMqvJMF10nxXUOXvucHqHli0JarPXZNkETGfUg5_2hLZ8Q_ZkqclsQ1CxB51jZViPT6BFU0LN5mj79ebUOSqgMnj28544q-n18urWnT3e3F0lMxekiKxbqDXyPMMYwohHEHPkvgSpeCwB1wH3IPe4yPw4kIGPIojWGaDiEPqKh-gLeeJcDN496dcGjU23uqHubpNKrpSIOl_cpcSQykgbQ1ikeypfgNpU8LSvIB0qSL8rSPsKOmY8MGAy_LX-D3wB4_yLFw</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>3066180439</pqid></control><display><type>article</type><title>An Integrated Framework for Analyzing Risk Influence Factors of Inland Waterway Transport Based on Interpretive Structural Models and Bayesian Networks</title><source>ASCE All titles</source><creator>Guo, Tao ; Xie, Lei ; Zhang, Jinfen ; Zhao, Jianwei ; Zhou, Heyu</creator><creatorcontrib>Guo, Tao ; Xie, Lei ; Zhang, Jinfen ; Zhao, Jianwei ; Zhou, Heyu</creatorcontrib><description>AbstractHistorical accident data provides valuable insights into the causes of maritime accidents. To investigate the effect of factors on maritime safety through accident analysis, this study collected 238 accidents that occurred in the mainstream of the Yangtze River from 2016 to 2021. The data features that reflect the frequency of risk influence factors (RIFs) are identified, and principal component analysis (PCA) is used to reduce the feature dimension of the RIFs. Furthermore, an interpretive structure model is constructed to analyze the relevance and hierarchy of the RIFs. The parameters of the network model are learned using the data set of accident cases, and the conditional probability of each node is obtained, based on these, the Bayesian network model of RIFs can be constructed. The sensitivity analysis reveals that all types of accidents are the location of incident, ship type, ship age, hull condition, and channel environment. Four cases are used to verify the effectiveness of the proposed model. This research provides theoretical support for taking measures to prevent accidents and control risks.</description><identifier>ISSN: 2376-7642</identifier><identifier>EISSN: 2376-7642</identifier><identifier>DOI: 10.1061/AJRUA6.RUENG-1282</identifier><language>eng</language><publisher>Reston: American Society of Civil Engineers</publisher><subject>Accident analysis ; Accident data ; Accident prevention ; Bayesian analysis ; Conditional probability ; Inland waterway transportation ; Principal components analysis ; Risk analysis ; Sensitivity analysis ; Ship hulls ; Structural models ; Technical Papers</subject><ispartof>ASCE-ASME journal of risk and uncertainty in engineering systems. Part A, Civil Engineering, 2024-09, Vol.10 (3)</ispartof><rights>2024 American Society of Civil Engineers</rights><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-a318t-f6be0dce9a7808a90e043a36093aeb502ad201c495354e158bcae60a74607e413</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttp://ascelibrary.org/doi/pdf/10.1061/AJRUA6.RUENG-1282$$EPDF$$P50$$Gasce$$H</linktopdf><linktohtml>$$Uhttp://ascelibrary.org/doi/abs/10.1061/AJRUA6.RUENG-1282$$EHTML$$P50$$Gasce$$H</linktohtml><link.rule.ids>314,776,780,27901,27902,76164,76172</link.rule.ids></links><search><creatorcontrib>Guo, Tao</creatorcontrib><creatorcontrib>Xie, Lei</creatorcontrib><creatorcontrib>Zhang, Jinfen</creatorcontrib><creatorcontrib>Zhao, Jianwei</creatorcontrib><creatorcontrib>Zhou, Heyu</creatorcontrib><title>An Integrated Framework for Analyzing Risk Influence Factors of Inland Waterway Transport Based on Interpretive Structural Models and Bayesian Networks</title><title>ASCE-ASME journal of risk and uncertainty in engineering systems. Part A, Civil Engineering</title><description>AbstractHistorical accident data provides valuable insights into the causes of maritime accidents. To investigate the effect of factors on maritime safety through accident analysis, this study collected 238 accidents that occurred in the mainstream of the Yangtze River from 2016 to 2021. The data features that reflect the frequency of risk influence factors (RIFs) are identified, and principal component analysis (PCA) is used to reduce the feature dimension of the RIFs. Furthermore, an interpretive structure model is constructed to analyze the relevance and hierarchy of the RIFs. The parameters of the network model are learned using the data set of accident cases, and the conditional probability of each node is obtained, based on these, the Bayesian network model of RIFs can be constructed. The sensitivity analysis reveals that all types of accidents are the location of incident, ship type, ship age, hull condition, and channel environment. Four cases are used to verify the effectiveness of the proposed model. This research provides theoretical support for taking measures to prevent accidents and control risks.</description><subject>Accident analysis</subject><subject>Accident data</subject><subject>Accident prevention</subject><subject>Bayesian analysis</subject><subject>Conditional probability</subject><subject>Inland waterway transportation</subject><subject>Principal components analysis</subject><subject>Risk analysis</subject><subject>Sensitivity analysis</subject><subject>Ship hulls</subject><subject>Structural models</subject><subject>Technical Papers</subject><issn>2376-7642</issn><issn>2376-7642</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><recordid>eNp1kU1Lw0AQhoMoWNQf4G3Bc-puNtkkx1SsH9QKtcVjmCaTkjZm6-xGiX_Ev2tiBL142mF5n2cYXsc5F3wsuBKXyf1ilajxYnU9v3GFF3kHzsiToXJD5XuHf-Zj58yYLedc-LEng3jkfCY1u6stbggs5mxK8ILvmnas0MSSGqr2o6w3bFGaXZcrqgbrDNkUMqvJMF10nxXUOXvucHqHli0JarPXZNkETGfUg5_2hLZ8Q_ZkqclsQ1CxB51jZViPT6BFU0LN5mj79ebUOSqgMnj28544q-n18urWnT3e3F0lMxekiKxbqDXyPMMYwohHEHPkvgSpeCwB1wH3IPe4yPw4kIGPIojWGaDiEPqKh-gLeeJcDN496dcGjU23uqHubpNKrpSIOl_cpcSQykgbQ1ikeypfgNpU8LSvIB0qSL8rSPsKOmY8MGAy_LX-D3wB4_yLFw</recordid><startdate>20240901</startdate><enddate>20240901</enddate><creator>Guo, Tao</creator><creator>Xie, Lei</creator><creator>Zhang, Jinfen</creator><creator>Zhao, Jianwei</creator><creator>Zhou, Heyu</creator><general>American Society of Civil Engineers</general><scope>AAYXX</scope><scope>CITATION</scope><scope>8FD</scope><scope>FR3</scope><scope>KR7</scope></search><sort><creationdate>20240901</creationdate><title>An Integrated Framework for Analyzing Risk Influence Factors of Inland Waterway Transport Based on Interpretive Structural Models and Bayesian Networks</title><author>Guo, Tao ; Xie, Lei ; Zhang, Jinfen ; Zhao, Jianwei ; Zhou, Heyu</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a318t-f6be0dce9a7808a90e043a36093aeb502ad201c495354e158bcae60a74607e413</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Accident analysis</topic><topic>Accident data</topic><topic>Accident prevention</topic><topic>Bayesian analysis</topic><topic>Conditional probability</topic><topic>Inland waterway transportation</topic><topic>Principal components analysis</topic><topic>Risk analysis</topic><topic>Sensitivity analysis</topic><topic>Ship hulls</topic><topic>Structural models</topic><topic>Technical Papers</topic><toplevel>online_resources</toplevel><creatorcontrib>Guo, Tao</creatorcontrib><creatorcontrib>Xie, Lei</creatorcontrib><creatorcontrib>Zhang, Jinfen</creatorcontrib><creatorcontrib>Zhao, Jianwei</creatorcontrib><creatorcontrib>Zhou, Heyu</creatorcontrib><collection>CrossRef</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>Civil Engineering Abstracts</collection><jtitle>ASCE-ASME journal of risk and uncertainty in engineering systems. Part A, Civil Engineering</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Guo, Tao</au><au>Xie, Lei</au><au>Zhang, Jinfen</au><au>Zhao, Jianwei</au><au>Zhou, Heyu</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>An Integrated Framework for Analyzing Risk Influence Factors of Inland Waterway Transport Based on Interpretive Structural Models and Bayesian Networks</atitle><jtitle>ASCE-ASME journal of risk and uncertainty in engineering systems. Part A, Civil Engineering</jtitle><date>2024-09-01</date><risdate>2024</risdate><volume>10</volume><issue>3</issue><issn>2376-7642</issn><eissn>2376-7642</eissn><abstract>AbstractHistorical accident data provides valuable insights into the causes of maritime accidents. To investigate the effect of factors on maritime safety through accident analysis, this study collected 238 accidents that occurred in the mainstream of the Yangtze River from 2016 to 2021. The data features that reflect the frequency of risk influence factors (RIFs) are identified, and principal component analysis (PCA) is used to reduce the feature dimension of the RIFs. Furthermore, an interpretive structure model is constructed to analyze the relevance and hierarchy of the RIFs. The parameters of the network model are learned using the data set of accident cases, and the conditional probability of each node is obtained, based on these, the Bayesian network model of RIFs can be constructed. The sensitivity analysis reveals that all types of accidents are the location of incident, ship type, ship age, hull condition, and channel environment. Four cases are used to verify the effectiveness of the proposed model. This research provides theoretical support for taking measures to prevent accidents and control risks.</abstract><cop>Reston</cop><pub>American Society of Civil Engineers</pub><doi>10.1061/AJRUA6.RUENG-1282</doi></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 2376-7642 |
| ispartof | ASCE-ASME journal of risk and uncertainty in engineering systems. Part A, Civil Engineering, 2024-09, Vol.10 (3) |
| issn | 2376-7642 2376-7642 |
| language | eng |
| recordid | cdi_proquest_journals_3066180439 |
| source | ASCE All titles |
| subjects | Accident analysis Accident data Accident prevention Bayesian analysis Conditional probability Inland waterway transportation Principal components analysis Risk analysis Sensitivity analysis Ship hulls Structural models Technical Papers |
| title | An Integrated Framework for Analyzing Risk Influence Factors of Inland Waterway Transport Based on Interpretive Structural Models and Bayesian Networks |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-13T09%3A31%3A03IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=An%20Integrated%20Framework%20for%20Analyzing%20Risk%20Influence%20Factors%20of%20Inland%20Waterway%20Transport%20Based%20on%20Interpretive%20Structural%20Models%20and%20Bayesian%20Networks&rft.jtitle=ASCE-ASME%20journal%20of%20risk%20and%20uncertainty%20in%20engineering%20systems.%20Part%20A,%20Civil%20Engineering&rft.au=Guo,%20Tao&rft.date=2024-09-01&rft.volume=10&rft.issue=3&rft.issn=2376-7642&rft.eissn=2376-7642&rft_id=info:doi/10.1061/AJRUA6.RUENG-1282&rft_dat=%3Cproquest_cross%3E3066180439%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=3066180439&rft_id=info:pmid/&rfr_iscdi=true |