dc.contributor.author | Solorzano, German | |
dc.contributor.author | Plevris, Vagelis | |
dc.date.accessioned | 2023-10-27T05:56:34Z | |
dc.date.available | 2023-10-27T05:56:34Z | |
dc.date.created | 2023-06-13T17:21:15Z | |
dc.date.issued | 2023 | |
dc.identifier.citation | Mathematics. 2023, 11 (10), . | en_US |
dc.identifier.issn | 2227-7390 | |
dc.identifier.uri | https://hdl.handle.net/11250/3099037 | |
dc.description.abstract | This study proposes the DNN-MVLEM, a novel macromodel for the non-linear analysis of RC shear walls based on deep neural networks (DNN); while most RC shear wall macromodeling techniques follow a deterministic approach to find the right configuration and properties of the system, in this study, an alternative data-driven strategy is proposed instead. The proposed DNN- MVLEM is composed of four vertical beam-column elements and one horizontal shear spring. The beam-column elements implement the fiber section formulation with standard non-linear uniaxial material models for concrete and steel, while the horizontal shear spring uses a multi-linear force– displacement relationship. Additionally, three calibration factors are introduced to improve the performance of the macromodel. The data-driven component of the proposed strategy consists of a large DNN that is trained to predict the force–displacement curve of the shear spring and the three calibration factors. The training data is created using a parametric microscopic FEM model based on the multi-layer shell element formulation and a genetic algorithm (GA) that optimizes the response of the macromodel to match the behavior of the microscopic FEM model. The DNN-MVLEM is tested in two types of examples, first as a stand-alone model and then as part of a two-bay multi-story frame structure. The results show that the DNN-MVLEM is capable of reproducing the results obtained with the microscopic FEM model up to 100 times faster and with an estimated error lower than 5%. | en_US |
dc.language.iso | eng | en_US |
dc.rights | Navngivelse 4.0 Internasjonal | * |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/deed.no | * |
dc.title | DNN-MLVEM: A Data-Driven Macromodel for RC Shear Walls Based on Deep Neural Networks | en_US |
dc.type | Peer reviewed | en_US |
dc.type | Journal article | en_US |
dc.description.version | publishedVersion | en_US |
cristin.ispublished | true | |
cristin.fulltext | original | |
cristin.qualitycode | 1 | |
dc.identifier.doi | 10.3390/math11102347 | |
dc.identifier.cristin | 2154237 | |
dc.source.journal | Mathematics | en_US |
dc.source.volume | 11 | en_US |
dc.source.issue | 10 | en_US |
dc.source.pagenumber | 19 | en_US |