Mathematics / Statistics

Lijun Wang 1 and Pengcheng Yang2  

1School of Zhejiang Guangsha Vocational and Technical University of construction; Dongyang Zhejiang 322100 China
2S&C Design Group; Hangzhou Zhejiang 310000 China

 

Received: August 4, 2022
Accepted: October 2, 2022
Publication Date: December 14, 2022

 Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.


Download Citation: ||https://doi.org/10.6180/jase.202309_26(9).0011  


The compressive strength of ultra great workability concrete (UGWC) is a function of the kind, characteristics and quantities of its material components. Empirically gaining this relation sometimes needs the usage of intelligent algorithms to receive a simulative model that fits into experimental data records. In this study, the usefulness of developing hybridized regression analysis on UGWC was analyzed with the aim of reducing the consumed time and experimental efforts. To this aim, a dataset including 170 samples collected from published papers different hybridized support vector regression (SVR) analyses were produced, where the optimal values of determinant attributes of SVR were explored by metaheuristic optimization algorithms named particle swarm optimization (PSO), Cuckoo optimization algorithm (COA), and Bat algorithm (BAT). The performance evaluators demonstrate that all three hybridized SVR models have remarkable potential in compressive strength estimation of UGWC. The first rank belonged to the SVR-COA model, where it could gain the highest value of R2 and variance account factor (VAF) in both training (R2=0.9056 and VAF=90.17%) and validating section (R2=0.9208 and VAF=91.81%), and the lowest value of root mean square error, and mean absolute error in both training and validating sections. Therefore, the hybridized SVR COA model could receive the proper accuracy in comparison with other models as well as literature.


Keywords: Ultra great workability concrete; Compressive strength; Prediction; SVR analysis; Optimization algorithms


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