Machine Learning–Based Seismic Reliability Assessment of Bridge Networks
AbstractTransportation networks are critical components of lifeline systems. They can experience disruptions due to seismic hazards that could lead to severe emergency response and recovery problems. Finding an efficient and effective method to evaluate the seismic reliability of bridge networks is...
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| Veröffentlicht in: | Journal of structural engineering (New York, N.Y.) N.Y.), 2022-07, Vol.148 (7) |
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| container_title | Journal of structural engineering (New York, N.Y.) |
| container_volume | 148 |
| creator | Chen, Mengdie Mangalathu, Sujith Jeon, Jong-Su |
| description | AbstractTransportation networks are critical components of lifeline systems. They can experience disruptions due to seismic hazards that could lead to severe emergency response and recovery problems. Finding an efficient and effective method to evaluate the seismic reliability of bridge networks is crucial for risk managers. This study proposes a method that can compute the seismic reliability of bridge networks using machine learning techniques. The proposed method is computationally less expensive than existing methods and can be implemented easily in emergency risk management systems. Moreover, it includes information on ranking bridges and prioritizing retrofit plans. |
| doi_str_mv | 10.1061/(ASCE)ST.1943-541X.0003376 |
| format | Article |
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They can experience disruptions due to seismic hazards that could lead to severe emergency response and recovery problems. Finding an efficient and effective method to evaluate the seismic reliability of bridge networks is crucial for risk managers. This study proposes a method that can compute the seismic reliability of bridge networks using machine learning techniques. The proposed method is computationally less expensive than existing methods and can be implemented easily in emergency risk management systems. 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They can experience disruptions due to seismic hazards that could lead to severe emergency response and recovery problems. Finding an efficient and effective method to evaluate the seismic reliability of bridge networks is crucial for risk managers. This study proposes a method that can compute the seismic reliability of bridge networks using machine learning techniques. The proposed method is computationally less expensive than existing methods and can be implemented easily in emergency risk management systems. Moreover, it includes information on ranking bridges and prioritizing retrofit plans.</description><subject>Component reliability</subject><subject>Critical components</subject><subject>Emergency management</subject><subject>Emergency response</subject><subject>Machine learning</subject><subject>Management systems</subject><subject>Network reliability</subject><subject>Reliability analysis</subject><subject>Retrofitting</subject><subject>Risk management</subject><subject>Seismic hazard</subject><subject>Structural engineering</subject><subject>Technical Note</subject><subject>Technical Notes</subject><subject>Transportation networks</subject><issn>0733-9445</issn><issn>1943-541X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><recordid>eNp1kE1OwzAQRi0EEqVwhwg2sEiw49iJ2bVV-VMBiRSJneW44-LSJsVOhbrjDtyQk5CoBVasZjT63szoIXRMcEQwJ-envXwwPMvHEREJDVlCniOMMaUp30Gd39ku6uCU0lAkCdtHB97PmlDKSNZBt3dKv9gSghEoV9py-vXx2VceJkEO1i-sDh5hblVh57ZeBz3vwfsFlHVQmaDv7GQKwT3U75V79Ydoz6i5h6Nt7aKny-F4cB2OHq5uBr1RqGKe1qHKVDoRkKWsKLAxWnHQhuukebRIRCFYwkyRiabNCm60ZtQwZjIlUh5TFnPaRSebvUtXva3A13JWrVzZnJQxZ0Qw2ghoUheblHaV9w6MXDq7UG4tCZatOylbdzIfy9aTbD3JrbsG5htYeQ1_63_I_8Fvip50zg</recordid><startdate>20220701</startdate><enddate>20220701</enddate><creator>Chen, Mengdie</creator><creator>Mangalathu, Sujith</creator><creator>Jeon, Jong-Su</creator><general>American Society of Civil Engineers</general><scope>AAYXX</scope><scope>CITATION</scope><scope>8FD</scope><scope>FR3</scope><scope>KR7</scope><orcidid>https://orcid.org/0000-0001-6657-7265</orcidid></search><sort><creationdate>20220701</creationdate><title>Machine Learning–Based Seismic Reliability Assessment of Bridge Networks</title><author>Chen, Mengdie ; Mangalathu, Sujith ; Jeon, Jong-Su</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a267t-a8a7d9e875bb0ffca6ecf6c4541b49b9545fb8949b8b6fcc53f55f8a976235263</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Component reliability</topic><topic>Critical components</topic><topic>Emergency management</topic><topic>Emergency response</topic><topic>Machine learning</topic><topic>Management systems</topic><topic>Network reliability</topic><topic>Reliability analysis</topic><topic>Retrofitting</topic><topic>Risk management</topic><topic>Seismic hazard</topic><topic>Structural engineering</topic><topic>Technical Note</topic><topic>Technical Notes</topic><topic>Transportation networks</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Chen, Mengdie</creatorcontrib><creatorcontrib>Mangalathu, Sujith</creatorcontrib><creatorcontrib>Jeon, Jong-Su</creatorcontrib><collection>CrossRef</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>Civil Engineering Abstracts</collection><jtitle>Journal of structural engineering (New York, N.Y.)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Chen, Mengdie</au><au>Mangalathu, Sujith</au><au>Jeon, Jong-Su</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Machine Learning–Based Seismic Reliability Assessment of Bridge Networks</atitle><jtitle>Journal of structural engineering (New York, N.Y.)</jtitle><date>2022-07-01</date><risdate>2022</risdate><volume>148</volume><issue>7</issue><issn>0733-9445</issn><eissn>1943-541X</eissn><abstract>AbstractTransportation networks are critical components of lifeline systems. They can experience disruptions due to seismic hazards that could lead to severe emergency response and recovery problems. Finding an efficient and effective method to evaluate the seismic reliability of bridge networks is crucial for risk managers. This study proposes a method that can compute the seismic reliability of bridge networks using machine learning techniques. The proposed method is computationally less expensive than existing methods and can be implemented easily in emergency risk management systems. Moreover, it includes information on ranking bridges and prioritizing retrofit plans.</abstract><cop>New York</cop><pub>American Society of Civil Engineers</pub><doi>10.1061/(ASCE)ST.1943-541X.0003376</doi><orcidid>https://orcid.org/0000-0001-6657-7265</orcidid></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 0733-9445 |
| ispartof | Journal of structural engineering (New York, N.Y.), 2022-07, Vol.148 (7) |
| issn | 0733-9445 1943-541X |
| language | eng |
| recordid | cdi_proquest_journals_2651953033 |
| source | American Society of Civil Engineers:NESLI2:Journals:2014 |
| subjects | Component reliability Critical components Emergency management Emergency response Machine learning Management systems Network reliability Reliability analysis Retrofitting Risk management Seismic hazard Structural engineering Technical Note Technical Notes Transportation networks |
| title | Machine Learning–Based Seismic Reliability Assessment of Bridge Networks |
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