TY - JOUR
T1 - Development of novel design strength model for sustainable concrete columns
T2 - A new machine learning-based approach
AU - Munir, Muhammad Junaid
AU - Kazmi, Syed Minhaj Saleem
AU - Wu, Yu Fei
AU - Lin, Xiaoshan
AU - Ahmad, Muhammad Riaz
N1 - Funding Information:
This work was supported by the Australian Research Council ( DP200100631 ) and the Victoria-Jiangsu Program for Technology and Innovation R&D by the Department of Economic Development, Jobs, Transport and Resources, the state of Victoria, Australia .
Publisher Copyright:
© 2022 Elsevier Ltd
PY - 2022/7/10
Y1 - 2022/7/10
N2 - Billions of tons of construction and demolition (C&D) waste generation is causing global environmental crises. The application of C&D waste in concrete columns is a sustainable avenue but hindered due to a lack of comprehensive design guidelines. Currently, no work is available in the open literature regarding the machine-learning-based comprehensive design strength models of spiral steel confined natural aggregate concrete (SSCNAC) and recycled aggregate concrete (SSCRAC) columns. This study comprehensively evaluates ten machine-learning techniques, including random forest, gradient boost, Ada boost, k-nearest neighbor, bagging regressor, support vector, XG boost, decision tree, artificial neural network, and gene expression programming for design strength modeling of SSCNAC and SSCRAC columns. A test database comprising 290 experiment results is developed and used for modeling. The accuracy of models is improved using hyper-parameter tuning and cross-validation functions. The performance of machine-learning techniques is evaluated through various statistical parameters. Results indicate that the spiral steel strength and the unconfined concrete compressive strength are the most critical inputs to predict the design strength of SSCNAC and SSCRAC columns. All the machine-learning models can accurately predict the design strength of SSCNAC and SSCRAC better than the existing models and can be practical tools for the accurate design strength modeling of the SSCNAC and SSCRAC. A one of its kind of a comprehensive machine learning-based design equation is proposed in this study, which can be used to predict the design strength of SSCNAC and SSCRAC accurately, leading towards the sustainable design of eco-friendly concrete columns.
AB - Billions of tons of construction and demolition (C&D) waste generation is causing global environmental crises. The application of C&D waste in concrete columns is a sustainable avenue but hindered due to a lack of comprehensive design guidelines. Currently, no work is available in the open literature regarding the machine-learning-based comprehensive design strength models of spiral steel confined natural aggregate concrete (SSCNAC) and recycled aggregate concrete (SSCRAC) columns. This study comprehensively evaluates ten machine-learning techniques, including random forest, gradient boost, Ada boost, k-nearest neighbor, bagging regressor, support vector, XG boost, decision tree, artificial neural network, and gene expression programming for design strength modeling of SSCNAC and SSCRAC columns. A test database comprising 290 experiment results is developed and used for modeling. The accuracy of models is improved using hyper-parameter tuning and cross-validation functions. The performance of machine-learning techniques is evaluated through various statistical parameters. Results indicate that the spiral steel strength and the unconfined concrete compressive strength are the most critical inputs to predict the design strength of SSCNAC and SSCRAC columns. All the machine-learning models can accurately predict the design strength of SSCNAC and SSCRAC better than the existing models and can be practical tools for the accurate design strength modeling of the SSCNAC and SSCRAC. A one of its kind of a comprehensive machine learning-based design equation is proposed in this study, which can be used to predict the design strength of SSCNAC and SSCRAC accurately, leading towards the sustainable design of eco-friendly concrete columns.
KW - Concrete columns
KW - Confinement
KW - Design strength prediction
KW - Machine learning techniques
KW - Natural aggregate concrete
KW - Recycled aggregate concrete
KW - Steel spirals
UR - http://www.scopus.com/inward/record.url?scp=85129054449&partnerID=8YFLogxK
U2 - 10.1016/j.jclepro.2022.131988
DO - 10.1016/j.jclepro.2022.131988
M3 - Journal article
AN - SCOPUS:85129054449
SN - 0959-6526
VL - 357
JO - Journal of Cleaner Production
JF - Journal of Cleaner Production
M1 - 131988
ER -