Matches in SemOpenAlex for { <https://semopenalex.org/work/W4366994581> ?p ?o ?g. }
- W4366994581 abstract "Constructing meta-models and selecting a suitable deterministic analysis method are important to improve the computational efficiency and accuracy of the nonintrusive reliability analysis of a spatially varying soil slope. However, existing meta-models are not applicable to the slopes considering multiple parameters with high spatial variability. Moreover, it is difficult to identify the failure modes when the spatial variability is high by using deterministic analysis methods based on slip surface search. Therefore, a nonintrusive stochastic strength reduction finite-element method (SRFEM) is developed based on the multi-input convolution neural networks (CNNs) and ABAQUS 2016. The SRFEM developed based on ABAQUS is adopted as the deterministic analysis method to avoid the uncertain search for the critical slip surfaces of slopes with high spatial variability. A multi-input CNN is proposed to construct the meta-model to avoid the “curse of dimensionality” and replace the overmuch times of time-consuming finite-element simulations. It can fit the relationships between multiple spatially varying parameters and the factor of safety by processing different parameters with different streams of CNNs. Two illustrative examples show that the proposed method can accurately identify the failure modes of slopes with different degrees of spatial variability. The agreement of the reliability results based on the proposed method and the general random finite-element method (RFEM) shows the high accuracy of the proposed method. The time cost of the proposed method can be reduced to 6.0 × 10−3 times that of the general RFEM, verifying the high computational efficiency of the proposed method. The multi-input CNN also shows higher fitting accuracy and better interpretability than the single-stream CNN and the support vector machines (SVMs). The generalization ability, accuracy, and efficiency of the proposed method show its potential to carry out the reliability analyses of slopes with multiple spatially varying parameters." @default.
- W4366994581 created "2023-04-27" @default.
- W4366994581 creator A5042334259 @default.
- W4366994581 creator A5057999649 @default.
- W4366994581 date "2023-07-01" @default.
- W4366994581 modified "2023-09-26" @default.
- W4366994581 title "Reliability Analyses of Soil Slopes with Multiple Spatially Varying Parameters Using Multi-Input Convolutional Neural Networks" @default.
- W4366994581 cites W1572063013 @default.
- W4366994581 cites W1965597870 @default.
- W4366994581 cites W1966837944 @default.
- W4366994581 cites W1973090811 @default.
- W4366994581 cites W1974904935 @default.
- W4366994581 cites W1979547539 @default.
- W4366994581 cites W1986206586 @default.
- W4366994581 cites W1991317455 @default.
- W4366994581 cites W2027751236 @default.
- W4366994581 cites W2046403766 @default.
- W4366994581 cites W2084364530 @default.
- W4366994581 cites W2100318547 @default.
- W4366994581 cites W2136284614 @default.
- W4366994581 cites W2144354855 @default.
- W4366994581 cites W2161064703 @default.
- W4366994581 cites W2162547718 @default.
- W4366994581 cites W2190226824 @default.
- W4366994581 cites W2537646176 @default.
- W4366994581 cites W2810932392 @default.
- W4366994581 cites W2914331134 @default.
- W4366994581 cites W2948915539 @default.
- W4366994581 cites W2955255825 @default.
- W4366994581 cites W2955754037 @default.
- W4366994581 cites W2972534151 @default.
- W4366994581 cites W2983557499 @default.
- W4366994581 cites W2995508556 @default.
- W4366994581 cites W2996886619 @default.
- W4366994581 cites W3004932743 @default.
- W4366994581 cites W3014673353 @default.
- W4366994581 cites W3015423958 @default.
- W4366994581 cites W3019645317 @default.
- W4366994581 cites W3036346296 @default.
- W4366994581 cites W3038187968 @default.
- W4366994581 cites W3039084241 @default.
- W4366994581 cites W3096607603 @default.
- W4366994581 cites W3100321043 @default.
- W4366994581 cites W3120565179 @default.
- W4366994581 cites W3121101663 @default.
- W4366994581 cites W3126940265 @default.
- W4366994581 cites W3133712560 @default.
- W4366994581 cites W3135308760 @default.
- W4366994581 cites W3143588993 @default.
- W4366994581 cites W3203629242 @default.
- W4366994581 cites W3205469028 @default.
- W4366994581 cites W33518744 @default.
- W4366994581 cites W4210257255 @default.
- W4366994581 cites W4210270710 @default.
- W4366994581 cites W4226551898 @default.
- W4366994581 cites W4284699140 @default.
- W4366994581 doi "https://doi.org/10.1061/ijgnai.gmeng-8234" @default.
- W4366994581 hasPublicationYear "2023" @default.
- W4366994581 type Work @default.
- W4366994581 citedByCount "0" @default.
- W4366994581 crossrefType "journal-article" @default.
- W4366994581 hasAuthorship W4366994581A5042334259 @default.
- W4366994581 hasAuthorship W4366994581A5057999649 @default.
- W4366994581 hasConcept C111030470 @default.
- W4366994581 hasConcept C11413529 @default.
- W4366994581 hasConcept C121332964 @default.
- W4366994581 hasConcept C127413603 @default.
- W4366994581 hasConcept C135628077 @default.
- W4366994581 hasConcept C146978453 @default.
- W4366994581 hasConcept C154945302 @default.
- W4366994581 hasConcept C163258240 @default.
- W4366994581 hasConcept C195268267 @default.
- W4366994581 hasConcept C41008148 @default.
- W4366994581 hasConcept C43214815 @default.
- W4366994581 hasConcept C45347329 @default.
- W4366994581 hasConcept C50644808 @default.
- W4366994581 hasConcept C62520636 @default.
- W4366994581 hasConcept C66938386 @default.
- W4366994581 hasConcept C81363708 @default.
- W4366994581 hasConceptScore W4366994581C111030470 @default.
- W4366994581 hasConceptScore W4366994581C11413529 @default.
- W4366994581 hasConceptScore W4366994581C121332964 @default.
- W4366994581 hasConceptScore W4366994581C127413603 @default.
- W4366994581 hasConceptScore W4366994581C135628077 @default.
- W4366994581 hasConceptScore W4366994581C146978453 @default.
- W4366994581 hasConceptScore W4366994581C154945302 @default.
- W4366994581 hasConceptScore W4366994581C163258240 @default.
- W4366994581 hasConceptScore W4366994581C195268267 @default.
- W4366994581 hasConceptScore W4366994581C41008148 @default.
- W4366994581 hasConceptScore W4366994581C43214815 @default.
- W4366994581 hasConceptScore W4366994581C45347329 @default.
- W4366994581 hasConceptScore W4366994581C50644808 @default.
- W4366994581 hasConceptScore W4366994581C62520636 @default.
- W4366994581 hasConceptScore W4366994581C66938386 @default.
- W4366994581 hasConceptScore W4366994581C81363708 @default.
- W4366994581 hasIssue "7" @default.
- W4366994581 hasLocation W43669945811 @default.
- W4366994581 hasOpenAccess W4366994581 @default.
- W4366994581 hasPrimaryLocation W43669945811 @default.
- W4366994581 hasRelatedWork W2285788670 @default.