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Prediction of axial load capacity of rectangular concrete-filled steel tube columns using machine learning techniques

This work aims to develop a novel and practical equation for predicting the axial load of rectangular concrete-filled steel tubular (CFST) columns based on soft computing techniques. More precisely, a dataset containing 880 experimental tests was first collected from the available literature for the...

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Main Authors: Tien-Thinh Le, Panagiotis G. Asteris, Minas E. Lemonis
Format: Bài trích
Language:eng
Published: Engineering with Computers 2021
Subjects:
Artificial neural networks (ANNs)
Genetic programming (GP)
Machine learning
Online Access:https://link.springer.com/article/10.1007%2Fs00366-021-01461-0
https://dlib.phenikaa-uni.edu.vn/handle/PNK/2838
https://doi.org/10.1007/s00366-021-01461-0
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spelling oai:localhost:PNK-28382022-08-17T05:54:47Z Prediction of axial load capacity of rectangular concrete-filled steel tube columns using machine learning techniques Tien-Thinh Le Panagiotis G. Asteris Minas E. Lemonis Artificial neural networks (ANNs) Genetic programming (GP) Machine learning This work aims to develop a novel and practical equation for predicting the axial load of rectangular concrete-filled steel tubular (CFST) columns based on soft computing techniques. More precisely, a dataset containing 880 experimental tests was first collected from the available literature for the development of an artificial neural network (ANN) model. An optimization strategy was conducted to obtain a final set of ANN’s architecture as well as its weight and bias parameters. The performance of the developed ANN was then compared to current codes (AS, EN, AIJ, ACI, AISC, LRFD, and DBJ) and existing empirical equations. The accuracy of the present model was found superior to the results obtained by others when predicting the axial load of rectangular CFST columns. For practical application, an explicit equation and an Excel-based Graphical User Interface were derived based on the ANN model. The graphical user interface is provided freely for all interested users, to support the design, teaching, and interpretation of the axial behavior of CFST columns. 2021-09-14T07:14:52Z 2021-09-14T07:14:52Z 2021 Bài trích https://link.springer.com/article/10.1007%2Fs00366-021-01461-0 https://dlib.phenikaa-uni.edu.vn/handle/PNK/2838 https://doi.org/10.1007/s00366-021-01461-0 eng Engineering with Computers
institution Digital Phenikaa
collection Digital Phenikaa
language eng
topic Artificial neural networks (ANNs)
Genetic programming (GP)
Machine learning
spellingShingle Artificial neural networks (ANNs)
Genetic programming (GP)
Machine learning
Tien-Thinh Le
Panagiotis G. Asteris
Minas E. Lemonis
Prediction of axial load capacity of rectangular concrete-filled steel tube columns using machine learning techniques
description This work aims to develop a novel and practical equation for predicting the axial load of rectangular concrete-filled steel tubular (CFST) columns based on soft computing techniques. More precisely, a dataset containing 880 experimental tests was first collected from the available literature for the development of an artificial neural network (ANN) model. An optimization strategy was conducted to obtain a final set of ANN’s architecture as well as its weight and bias parameters. The performance of the developed ANN was then compared to current codes (AS, EN, AIJ, ACI, AISC, LRFD, and DBJ) and existing empirical equations. The accuracy of the present model was found superior to the results obtained by others when predicting the axial load of rectangular CFST columns. For practical application, an explicit equation and an Excel-based Graphical User Interface were derived based on the ANN model. The graphical user interface is provided freely for all interested users, to support the design, teaching, and interpretation of the axial behavior of CFST columns.
format Bài trích
author Tien-Thinh Le
Panagiotis G. Asteris
Minas E. Lemonis
author_facet Tien-Thinh Le
Panagiotis G. Asteris
Minas E. Lemonis
author_sort Tien-Thinh Le
title Prediction of axial load capacity of rectangular concrete-filled steel tube columns using machine learning techniques
title_short Prediction of axial load capacity of rectangular concrete-filled steel tube columns using machine learning techniques
title_full Prediction of axial load capacity of rectangular concrete-filled steel tube columns using machine learning techniques
title_fullStr Prediction of axial load capacity of rectangular concrete-filled steel tube columns using machine learning techniques
title_full_unstemmed Prediction of axial load capacity of rectangular concrete-filled steel tube columns using machine learning techniques
title_sort prediction of axial load capacity of rectangular concrete-filled steel tube columns using machine learning techniques
publisher Engineering with Computers
publishDate 2021
url https://link.springer.com/article/10.1007%2Fs00366-021-01461-0
https://dlib.phenikaa-uni.edu.vn/handle/PNK/2838
https://doi.org/10.1007/s00366-021-01461-0
_version_ 1751856265051504640
score 8.887929

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