Blar i ODA Open Digital Archive på forfatter "Solorzano, German"
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ANN-based surrogate model for predicting the lateral load capacity of RC shear walls
Solorzano, German; Plevris, Vagelis (ECCOMAS Congress;8th European Congress on Computational Methods in Applied Sciences and Engineering, Conference object, 2022)Reinforced concrete (RC) shear walls are often used as the main lateral-resisting component in the seismic design of buildings. They provide a large percentage of the lateral stiffness of the structure, and therefore, they ... -
DNN-MLVEM: A Data-Driven Macromodel for RC Shear Walls Based on Deep Neural Networks
Solorzano, German; Plevris, Vagelis (Peer reviewed; Journal article, 2023)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 ... -
Investigation of performance metrics in regression analysis and machine learning-based prediction models
Plevris, Vagelis; Solorzano, German; Bakas, Nikolaos P.; Ben Seghier, Mohamed El Amine (ECCOMAS Congress;8th European Congress on Computational Methods in Applied Sciences and Engineering, Conference object, 2022-11-24)Performance metrics (Evaluation metrics or error metrics) are crucial components of regression analysis and machine learning-based prediction models. A performance metric can be defined as a logical and mathematical construct ... -
An Open-Source Framework for Modeling RC Shear Walls Using Deep Neural Networks
Solorzano, German; Plevris, Vagelis (Peer reviewed; Journal article, 2023)Reinforced concrete (RC) shear walls macroscopic models are simplified strategies able to simulate the complex nonlinear behavior of RC shear walls to some extent, but their efficacy and robustness are limited. In contrast, ... -
Using artificial intelligence techniques for the accurate estimation of the ultimate pure bending of steel circular tubes
Ben Seghier, Mohamed El Amine; Plevris, V.; Solorzano, German (Conference object, 2022-11-24)In this paper, the potential of building more accurate and robust models for the prediction of the ultimate pure bending capacity of steel circular tubes using artificial intelligence techniques is investigated. Therefore, ...