A Novel Hybrid Model Based on a Feedforward Neural Network and One Step Secant Algorithm for Prediction of Load-Bearing Capacity of Rectangular Concrete-Filled Steel Tube Columns

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ID: 110254
2020
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Abstract
In this study, a novel hybrid surrogate machine learning model based on a feedforward neural network (FNN) and one step secant algorithm (OSS) was developed to predict the load-bearing capacity of concrete-filled steel tube columns (CFST), whereas the OSS was used to optimize the weights and bias of the FNN for developing a hybrid model (FNN-OSS). For achieving this goal, an experimental database containing 422 instances was firstly gathered from the literature and used to develop the FNN-OSS algorithm. The input variables in the database contained the geometrical characteristics of CFST columns, and the mechanical properties of two CFST constituent materials, i.e., steel and concrete. Thereafter, the selection of the appropriate parameters of FNN-OSS was performed and evaluated by common statistical measurements, for instance, the coefficient of determination (R2), root mean square error (RMSE), and mean absolute error (MAE). In the next step, the prediction capability of the best FNN-OSS structure was evaluated in both global and local analyses, showing an excellent agreement between actual and predicted values of the load-bearing capacity. Finally, an in-depth investigation of the performance and limitations of FNN-OSS was conducted from a structural engineering point of view. The results confirmed the effectiveness of the FNN-OSS as a robust algorithm for the prediction of the CFST load-bearing capacity.
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nguyen2020moleculesa Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Quang Hung Nguyen;Hai-Bang Ly;Van Quan Tran;Thuy-Anh Nguyen;Viet-Hung Phan;Tien-Thinh Le;Binh Thai Pham;Nguyen, Quang Hung;Ly, Hai-Bang;Tran, Van Quan;Nguyen, Thuy-Anh;Phan, Viet-Hung;Le, Tien-Thinh;Pham, Binh Thai;
Journal molecules
Year 2020
DOI
10.3390/molecules25153486
URL
https://doi.org/10.3390/molecules25153486
Keywords
Machine learning Neural network optimization concrete-filled steel tube column one step secant algorithm

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