Cover Image

Development of user-friendly kernel-based Gaussian process regression model for prediction of load-bearing capacity of square concrete-filled steel tubular members

A Machine Learning (ML) model based on Gaussian regression, using different kernel functions, is introduced in this paper to assess the load-carrying capacity of square concrete-filled steel tubular (CFST) columns. The input data used to develop the prediction model, which consists of 314 datasets i...

Full description

Saved in:
Bibliographic Details
Main Authors: Tien-Thinh, Le, Minh Vuong, Le
Format: Article
Language:English
Published: Materials and Structures 54(2), 59 2021
Subjects:
square concrete-filled steel tubular members
Online Access:https://link.springer.com/article/10.1617%2Fs11527-021-01646-5
https://dlib.phenikaa-uni.edu.vn/handle/PNK/1429
Tags: Add Tag
No Tags, Be the first to tag this record!
id oai:localhost:PNK-1429
record_format dspace
spelling oai:localhost:PNK-14292022-08-17T05:54:41Z Development of user-friendly kernel-based Gaussian process regression model for prediction of load-bearing capacity of square concrete-filled steel tubular members Tien-Thinh, Le Minh Vuong, Le square concrete-filled steel tubular members A Machine Learning (ML) model based on Gaussian regression, using different kernel functions, is introduced in this paper to assess the load-carrying capacity of square concrete-filled steel tubular (CFST) columns. The input data used to develop the prediction model, which consists of 314 datasets including the structural geometrical parameters and the mechanical properties of the materials, was collected from available resources in the literature. The performance of the prediction model has also been validated by comparing with: (i) other ML models such as Artificial neural network, Support vector machine, etc.; and (ii) existing formulations in the literature for predicting load-carrying capacity of square CFST columns (including several codes such as EC4, AISC and ACI). The obtained results showed that the proposed model has outperformed them. The drawbacks of the model have been investigated by studying the influence of the input variables, together with uncertainty analysis providing 68, 95, and 99% prediction confidence intervals. Finally, a user-friendly interface has been developed to facilitate the application of the proposed model, providing the prediction value as well as confidence levels. 2021-05-31T07:49:36Z 2021-05-31T07:49:36Z 2021 Article Working Paper https://link.springer.com/article/10.1617%2Fs11527-021-01646-5 https://dlib.phenikaa-uni.edu.vn/handle/PNK/1429 en Materials and Structures 54(2), 59
institution Digital Phenikaa
collection Digital Phenikaa
language English
topic square concrete-filled steel tubular members
spellingShingle square concrete-filled steel tubular members
Tien-Thinh, Le
Minh Vuong, Le
Development of user-friendly kernel-based Gaussian process regression model for prediction of load-bearing capacity of square concrete-filled steel tubular members
description A Machine Learning (ML) model based on Gaussian regression, using different kernel functions, is introduced in this paper to assess the load-carrying capacity of square concrete-filled steel tubular (CFST) columns. The input data used to develop the prediction model, which consists of 314 datasets including the structural geometrical parameters and the mechanical properties of the materials, was collected from available resources in the literature. The performance of the prediction model has also been validated by comparing with: (i) other ML models such as Artificial neural network, Support vector machine, etc.; and (ii) existing formulations in the literature for predicting load-carrying capacity of square CFST columns (including several codes such as EC4, AISC and ACI). The obtained results showed that the proposed model has outperformed them. The drawbacks of the model have been investigated by studying the influence of the input variables, together with uncertainty analysis providing 68, 95, and 99% prediction confidence intervals. Finally, a user-friendly interface has been developed to facilitate the application of the proposed model, providing the prediction value as well as confidence levels.
format Article
author Tien-Thinh, Le
Minh Vuong, Le
author_facet Tien-Thinh, Le
Minh Vuong, Le
author_sort Tien-Thinh, Le
title Development of user-friendly kernel-based Gaussian process regression model for prediction of load-bearing capacity of square concrete-filled steel tubular members
title_short Development of user-friendly kernel-based Gaussian process regression model for prediction of load-bearing capacity of square concrete-filled steel tubular members
title_full Development of user-friendly kernel-based Gaussian process regression model for prediction of load-bearing capacity of square concrete-filled steel tubular members
title_fullStr Development of user-friendly kernel-based Gaussian process regression model for prediction of load-bearing capacity of square concrete-filled steel tubular members
title_full_unstemmed Development of user-friendly kernel-based Gaussian process regression model for prediction of load-bearing capacity of square concrete-filled steel tubular members
title_sort development of user-friendly kernel-based gaussian process regression model for prediction of load-bearing capacity of square concrete-filled steel tubular members
publisher Materials and Structures 54(2), 59
publishDate 2021
url https://link.springer.com/article/10.1617%2Fs11527-021-01646-5
https://dlib.phenikaa-uni.edu.vn/handle/PNK/1429
_version_ 1751856248229199872
score 8.887929

Similar Items