Matches in SemOpenAlex for { <https://semopenalex.org/work/W4386322958> ?p ?o ?g. }
- W4386322958 endingPage "116840" @default.
- W4386322958 startingPage "116840" @default.
- W4386322958 abstract "Fire poses a major risk to the structural integrity of a bridge or progressively to the bridge’s failure. This study uses an Artificial Neural Network (ANN) to investigate the determining factors in bridge fire incidents. Implementation of this powerful model produces an estimation of the damage levels. The basis of the proposed model is determined by critical factors, including bridge location, materials, structural systems, annual average daily traffic (AADT), ignition source, combustible type, and bridge face exposed to fire. Results show that steel I-girder bridges are the most susceptible structural system in a fire. Moreover, a fire involving tankers containing hydrocarbon fuel and trucks with solid combustible cargo is the most dangerous to a bridge. The accuracy of the proposed model is verified, and its outcome can be utilized to determine fire risk based on discrete characteristics. Based on the proposed model, a combination of hazard prevention and protection measures may be utilized to improve structural integrity, human life safety, and durability, and to reduce maintenance costs." @default.
- W4386322958 created "2023-09-01" @default.
- W4386322958 creator A5061088547 @default.
- W4386322958 creator A5066024098 @default.
- W4386322958 creator A5085307241 @default.
- W4386322958 creator A5092819112 @default.
- W4386322958 date "2023-11-01" @default.
- W4386322958 modified "2023-09-27" @default.
- W4386322958 title "Structural damage levels of bridges in vehicular collision fires: Predictions using an artificial neural network (ANN) model" @default.
- W4386322958 cites W1975176661 @default.
- W4386322958 cites W1982665183 @default.
- W4386322958 cites W1999037923 @default.
- W4386322958 cites W2003756933 @default.
- W4386322958 cites W2005201982 @default.
- W4386322958 cites W2015341043 @default.
- W4386322958 cites W2037481461 @default.
- W4386322958 cites W2066565247 @default.
- W4386322958 cites W2090883530 @default.
- W4386322958 cites W2104491887 @default.
- W4386322958 cites W2121908400 @default.
- W4386322958 cites W2124228617 @default.
- W4386322958 cites W2142584836 @default.
- W4386322958 cites W2152874621 @default.
- W4386322958 cites W2155482699 @default.
- W4386322958 cites W2219654286 @default.
- W4386322958 cites W2276964952 @default.
- W4386322958 cites W2552331816 @default.
- W4386322958 cites W2610165939 @default.
- W4386322958 cites W2756449574 @default.
- W4386322958 cites W2767758861 @default.
- W4386322958 cites W2803235424 @default.
- W4386322958 cites W2901312569 @default.
- W4386322958 cites W2904262063 @default.
- W4386322958 cites W2914122721 @default.
- W4386322958 cites W2953458970 @default.
- W4386322958 cites W2972960767 @default.
- W4386322958 cites W3028060678 @default.
- W4386322958 cites W3128350030 @default.
- W4386322958 cites W3135145039 @default.
- W4386322958 cites W3135274872 @default.
- W4386322958 cites W3164670774 @default.
- W4386322958 cites W3173408620 @default.
- W4386322958 cites W3196754102 @default.
- W4386322958 cites W3201895292 @default.
- W4386322958 cites W3202931876 @default.
- W4386322958 cites W3212463249 @default.
- W4386322958 cites W4241270588 @default.
- W4386322958 cites W4280490353 @default.
- W4386322958 cites W4312010854 @default.
- W4386322958 cites W4313889232 @default.
- W4386322958 cites W4319318926 @default.
- W4386322958 cites W4323666557 @default.
- W4386322958 cites W914834056 @default.
- W4386322958 doi "https://doi.org/10.1016/j.engstruct.2023.116840" @default.
- W4386322958 hasPublicationYear "2023" @default.
- W4386322958 type Work @default.
- W4386322958 citedByCount "0" @default.
- W4386322958 crossrefType "journal-article" @default.
- W4386322958 hasAuthorship W4386322958A5061088547 @default.
- W4386322958 hasAuthorship W4386322958A5066024098 @default.
- W4386322958 hasAuthorship W4386322958A5085307241 @default.
- W4386322958 hasAuthorship W4386322958A5092819112 @default.
- W4386322958 hasConcept C100776233 @default.
- W4386322958 hasConcept C104304963 @default.
- W4386322958 hasConcept C121704057 @default.
- W4386322958 hasConcept C126322002 @default.
- W4386322958 hasConcept C127413603 @default.
- W4386322958 hasConcept C154945302 @default.
- W4386322958 hasConcept C171146098 @default.
- W4386322958 hasConcept C38652104 @default.
- W4386322958 hasConcept C39432304 @default.
- W4386322958 hasConcept C41008148 @default.
- W4386322958 hasConcept C50644808 @default.
- W4386322958 hasConcept C52121051 @default.
- W4386322958 hasConcept C66938386 @default.
- W4386322958 hasConcept C71924100 @default.
- W4386322958 hasConcept C77088390 @default.
- W4386322958 hasConcept C77595967 @default.
- W4386322958 hasConceptScore W4386322958C100776233 @default.
- W4386322958 hasConceptScore W4386322958C104304963 @default.
- W4386322958 hasConceptScore W4386322958C121704057 @default.
- W4386322958 hasConceptScore W4386322958C126322002 @default.
- W4386322958 hasConceptScore W4386322958C127413603 @default.
- W4386322958 hasConceptScore W4386322958C154945302 @default.
- W4386322958 hasConceptScore W4386322958C171146098 @default.
- W4386322958 hasConceptScore W4386322958C38652104 @default.
- W4386322958 hasConceptScore W4386322958C39432304 @default.
- W4386322958 hasConceptScore W4386322958C41008148 @default.
- W4386322958 hasConceptScore W4386322958C50644808 @default.
- W4386322958 hasConceptScore W4386322958C52121051 @default.
- W4386322958 hasConceptScore W4386322958C66938386 @default.
- W4386322958 hasConceptScore W4386322958C71924100 @default.
- W4386322958 hasConceptScore W4386322958C77088390 @default.
- W4386322958 hasConceptScore W4386322958C77595967 @default.
- W4386322958 hasFunder F4320334593 @default.
- W4386322958 hasLocation W43863229581 @default.
- W4386322958 hasOpenAccess W4386322958 @default.
- W4386322958 hasPrimaryLocation W43863229581 @default.