Matches in SemOpenAlex for { <https://semopenalex.org/work/W3208382054> ?p ?o ?g. }
- W3208382054 endingPage "327" @default.
- W3208382054 startingPage "310" @default.
- W3208382054 abstract "ABSTRACT The incorporation of machine learning (ML) algorithms in earthquake engineering can improve existing methodologies and enable new frameworks to solve complex problems. In the present study, the use of artificial neural networks (ANNs) for the derivation of seismic vulnerability models for building portfolios is explored. Large sets of ground motion records (GMRs) and structural models representing the building stock in the Balkan region were used to train ANNs for the prediction of structural response, damage and economic loss conditioned on a vector of ground shaking intensity measures. The structural responses and loss ratios (LRs) generated using the neural networks were compared with results based on traditional regression models using scalar intensity measures in terms of efficiency, sufficiency, bias and variability. The results indicate a superior performance of the ANN models over traditional approaches, potentially allowing a greater reliability and accuracy in scenario and probabilistic seismic risk assessment." @default.
- W3208382054 created "2021-11-08" @default.
- W3208382054 creator A5036377624 @default.
- W3208382054 creator A5079572183 @default.
- W3208382054 date "2021-10-25" @default.
- W3208382054 modified "2023-10-02" @default.
- W3208382054 title "Seismic vulnerability modelling of building portfolios using artificial neural networks" @default.
- W3208382054 cites W1498436455 @default.
- W3208382054 cites W1964831647 @default.
- W3208382054 cites W1976061167 @default.
- W3208382054 cites W1981912386 @default.
- W3208382054 cites W1984749456 @default.
- W3208382054 cites W2000035517 @default.
- W3208382054 cites W2005553487 @default.
- W3208382054 cites W2014537082 @default.
- W3208382054 cites W2014965310 @default.
- W3208382054 cites W2043951469 @default.
- W3208382054 cites W2044078938 @default.
- W3208382054 cites W2048454736 @default.
- W3208382054 cites W2056918063 @default.
- W3208382054 cites W2070238662 @default.
- W3208382054 cites W2072459226 @default.
- W3208382054 cites W2072598257 @default.
- W3208382054 cites W2077562320 @default.
- W3208382054 cites W2082179468 @default.
- W3208382054 cites W2094685728 @default.
- W3208382054 cites W2096154525 @default.
- W3208382054 cites W2100370856 @default.
- W3208382054 cites W2103496339 @default.
- W3208382054 cites W2110893990 @default.
- W3208382054 cites W2112057493 @default.
- W3208382054 cites W2116861156 @default.
- W3208382054 cites W2129178344 @default.
- W3208382054 cites W2130250210 @default.
- W3208382054 cites W2133485774 @default.
- W3208382054 cites W2137356002 @default.
- W3208382054 cites W2137983211 @default.
- W3208382054 cites W2145195377 @default.
- W3208382054 cites W2162810529 @default.
- W3208382054 cites W2170872460 @default.
- W3208382054 cites W2278122654 @default.
- W3208382054 cites W2372111961 @default.
- W3208382054 cites W2414568040 @default.
- W3208382054 cites W2518569405 @default.
- W3208382054 cites W2533173840 @default.
- W3208382054 cites W2554548125 @default.
- W3208382054 cites W2562296981 @default.
- W3208382054 cites W2588652053 @default.
- W3208382054 cites W2613985117 @default.
- W3208382054 cites W2615749707 @default.
- W3208382054 cites W2744983412 @default.
- W3208382054 cites W2789135478 @default.
- W3208382054 cites W2793734850 @default.
- W3208382054 cites W2898280479 @default.
- W3208382054 cites W2900880673 @default.
- W3208382054 cites W2916578413 @default.
- W3208382054 cites W2919115771 @default.
- W3208382054 cites W2930890426 @default.
- W3208382054 cites W2940450774 @default.
- W3208382054 cites W2941345678 @default.
- W3208382054 cites W2990244813 @default.
- W3208382054 cites W2999473454 @default.
- W3208382054 cites W3003802494 @default.
- W3208382054 cites W3016826380 @default.
- W3208382054 cites W3028130353 @default.
- W3208382054 cites W3033652307 @default.
- W3208382054 cites W3035486359 @default.
- W3208382054 cites W3035732938 @default.
- W3208382054 cites W3090929278 @default.
- W3208382054 cites W3091351793 @default.
- W3208382054 cites W3107696457 @default.
- W3208382054 cites W4239349154 @default.
- W3208382054 doi "https://doi.org/10.1002/eqe.3567" @default.
- W3208382054 hasPublicationYear "2021" @default.
- W3208382054 type Work @default.
- W3208382054 sameAs 3208382054 @default.
- W3208382054 citedByCount "20" @default.
- W3208382054 countsByYear W32083820542022 @default.
- W3208382054 countsByYear W32083820542023 @default.
- W3208382054 crossrefType "journal-article" @default.
- W3208382054 hasAuthorship W3208382054A5036377624 @default.
- W3208382054 hasAuthorship W3208382054A5079572183 @default.
- W3208382054 hasConcept C103711010 @default.
- W3208382054 hasConcept C119857082 @default.
- W3208382054 hasConcept C121332964 @default.
- W3208382054 hasConcept C12267149 @default.
- W3208382054 hasConcept C127413603 @default.
- W3208382054 hasConcept C134512083 @default.
- W3208382054 hasConcept C137176749 @default.
- W3208382054 hasConcept C147176958 @default.
- W3208382054 hasConcept C154945302 @default.
- W3208382054 hasConcept C15744967 @default.
- W3208382054 hasConcept C163258240 @default.
- W3208382054 hasConcept C167063184 @default.
- W3208382054 hasConcept C2988284105 @default.
- W3208382054 hasConcept C38652104 @default.
- W3208382054 hasConcept C41008148 @default.
- W3208382054 hasConcept C43214815 @default.