Matches in SemOpenAlex for { <https://semopenalex.org/work/W4280577644> ?p ?o ?g. }
- W4280577644 endingPage "114395" @default.
- W4280577644 startingPage "114395" @default.
- W4280577644 abstract "Reliable post-earthquake modeling of a highway bridge network (HBN) is essential for seismic reliability, risk, and resilience assessment. This requires the accurate modeling of the effects of earthquake-induced bridge damage on traffic congestion. Accordingly, a novel post-earthquake HBN simulation method, wherein each bridge is defined as a link instead of as a part of a highway link, was proposed herein. In addition, the feasibility of implementing machine learning methods for fragility modeling of regular highway bridges was explored as well. The developed artificial neural network (ANN) fragility model can accurately capture the seismic damage of highway bridges at trivial computation costs compared to time-history analysis methods. Finally, the proposed methods were combined with probabilistic seismic hazard analysis, traffic demand assessment and distribution, etc., and applied to an HBN that connects several cities in Arizona and New Mexico to validate their efficacy and scalability. The results revealed that compared to the proposed HBN simulation method, conventional HBN modeling methods may underestimate the seismic resilience of HBNs." @default.
- W4280577644 created "2022-05-22" @default.
- W4280577644 creator A5010208763 @default.
- W4280577644 creator A5031620690 @default.
- W4280577644 creator A5042910096 @default.
- W4280577644 creator A5057107975 @default.
- W4280577644 date "2022-07-01" @default.
- W4280577644 modified "2023-10-14" @default.
- W4280577644 title "Post-earthquake assessment model for highway bridge networks considering traffic congestion due to earthquake-induced bridge damage" @default.
- W4280577644 cites W1178489151 @default.
- W4280577644 cites W1977834849 @default.
- W4280577644 cites W1988980850 @default.
- W4280577644 cites W2002221838 @default.
- W4280577644 cites W2014486108 @default.
- W4280577644 cites W2014530435 @default.
- W4280577644 cites W2017630868 @default.
- W4280577644 cites W2025303890 @default.
- W4280577644 cites W2027591347 @default.
- W4280577644 cites W2042762031 @default.
- W4280577644 cites W2044896258 @default.
- W4280577644 cites W2083916857 @default.
- W4280577644 cites W2084881953 @default.
- W4280577644 cites W2087163087 @default.
- W4280577644 cites W2087993201 @default.
- W4280577644 cites W2090350905 @default.
- W4280577644 cites W2090993956 @default.
- W4280577644 cites W2094685728 @default.
- W4280577644 cites W2106452250 @default.
- W4280577644 cites W2107729861 @default.
- W4280577644 cites W2111437277 @default.
- W4280577644 cites W2117845082 @default.
- W4280577644 cites W2125415054 @default.
- W4280577644 cites W2128865495 @default.
- W4280577644 cites W2129214473 @default.
- W4280577644 cites W2143935347 @default.
- W4280577644 cites W2145720265 @default.
- W4280577644 cites W2157501616 @default.
- W4280577644 cites W2167996923 @default.
- W4280577644 cites W2173448099 @default.
- W4280577644 cites W2405413358 @default.
- W4280577644 cites W2551679322 @default.
- W4280577644 cites W2597311793 @default.
- W4280577644 cites W2744645101 @default.
- W4280577644 cites W2789384841 @default.
- W4280577644 cites W2808760061 @default.
- W4280577644 cites W2888034896 @default.
- W4280577644 cites W2930890426 @default.
- W4280577644 cites W2944221064 @default.
- W4280577644 cites W2946752227 @default.
- W4280577644 cites W2963587403 @default.
- W4280577644 cites W2964135392 @default.
- W4280577644 cites W2967713746 @default.
- W4280577644 cites W2979664067 @default.
- W4280577644 cites W2981416566 @default.
- W4280577644 cites W2981979832 @default.
- W4280577644 cites W3001584767 @default.
- W4280577644 cites W3002707570 @default.
- W4280577644 cites W3006020715 @default.
- W4280577644 cites W3045300204 @default.
- W4280577644 cites W3097413413 @default.
- W4280577644 cites W3109040996 @default.
- W4280577644 cites W3123225657 @default.
- W4280577644 cites W3143641060 @default.
- W4280577644 cites W3158198537 @default.
- W4280577644 cites W3177456145 @default.
- W4280577644 cites W4210795569 @default.
- W4280577644 doi "https://doi.org/10.1016/j.engstruct.2022.114395" @default.
- W4280577644 hasPublicationYear "2022" @default.
- W4280577644 type Work @default.
- W4280577644 citedByCount "2" @default.
- W4280577644 countsByYear W42805776442023 @default.
- W4280577644 crossrefType "journal-article" @default.
- W4280577644 hasAuthorship W4280577644A5010208763 @default.
- W4280577644 hasAuthorship W4280577644A5031620690 @default.
- W4280577644 hasAuthorship W4280577644A5042910096 @default.
- W4280577644 hasAuthorship W4280577644A5057107975 @default.
- W4280577644 hasConcept C100776233 @default.
- W4280577644 hasConcept C121332964 @default.
- W4280577644 hasConcept C126322002 @default.
- W4280577644 hasConcept C127413603 @default.
- W4280577644 hasConcept C147176958 @default.
- W4280577644 hasConcept C147789679 @default.
- W4280577644 hasConcept C163258240 @default.
- W4280577644 hasConcept C182358397 @default.
- W4280577644 hasConcept C185592680 @default.
- W4280577644 hasConcept C2779585090 @default.
- W4280577644 hasConcept C41008148 @default.
- W4280577644 hasConcept C43214815 @default.
- W4280577644 hasConcept C62520636 @default.
- W4280577644 hasConcept C66938386 @default.
- W4280577644 hasConcept C69361100 @default.
- W4280577644 hasConcept C71924100 @default.
- W4280577644 hasConcept C80191262 @default.
- W4280577644 hasConcept C8128475 @default.
- W4280577644 hasConcept C90626213 @default.
- W4280577644 hasConcept C97355855 @default.
- W4280577644 hasConceptScore W4280577644C100776233 @default.
- W4280577644 hasConceptScore W4280577644C121332964 @default.