Journal of Applied Science and Engineering

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Pan JIANG1This email address is being protected from spambots. You need JavaScript enabled to view it. and Nan GE2

1Department of Civil Engineering, Hebei University of Water Resources and Electric Engineering, Cangzhou 061001, China

2College of Civil and Architectural Engineering, North China University of Science and Technology, Tangshan 063210, China


 

 

Received: July 13, 2023
Accepted: December 10, 2023
Publication Date: February 21, 2024

 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.202412_27(12).0005  


Regression techniques were developed to determine the concrete initial (Gf) and total (GF) fracture energy based on prior data using mechanical features and mixed design elements. There were 264 samples retrieved from prior investigations in all. Research contributes to the field by improving the accuracy of predicting concrete fracture energy, offering a methodology for optimizing hyperparameters, and providing a model comparison that demonstrates the practical value of the new approach. These findings can benefit the construction and engineering industries by enhancing the accuracy of material property predictions and improving the quality and safety of constructed structures. This study merged support vector regression (SVR) assessment with arithmetic optimization algorithm (AOA) and whale optimization algorithm (WOA) to predict the Gf and GFF of concrete. The aim of combining the optimization algorithms with SVR analysis was to determine the optimal values of hyperparameters that play pivotal role in developed models’ accuracy. The computation and analysis for Gf and GF using five criteria shows that optimized SVR-AOA and SVR-WOA analyses can do admirably well throughout the forecasting model. When the outperforming SVR analysis was compared to the library, it was discovered that the newly constructed SVR-AOA also present a small raise in accuracy, with modification in all metrics. In conclusion, while the SVR-WOA demonstrates its effectiveness in the forecasting outline, the SVR-AOA analysis appears to be a reliable approach for determining accurate Gf values R2train=0.921, and R2test=0.9853 and GF values R2train=0.9281, and R2test=0.9236, as supported by the arguments and feasibility of the models.


Keywords: Concrete; Fracture energy; Support vector regression; Optimization algorithms


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