DNN-MLVEM: A Data-Driven Macromodel for RC Shear Walls Based on Deep Neural Networks

German Solorzano () and Vagelis Plevris
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German Solorzano: Department of Civil Engineering and Energy Technology, OsloMet–Oslo Metropolitan University, 0166 Oslo, Norway
Vagelis Plevris: Department of Civil and Architectural Engineering, Qatar University, Doha P.O. Box 2713, Qatar

Mathematics, 2023, vol. 11, issue 10, 1-19

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%.

Keywords: shear wall; macromodel; deep neural network; genetic algorithm; OpenSees (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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