Predicting the capillary water absorption values of building stones by hybrid models

Gang  Li; Yufei  Fu; Zixin  Deng; Yuan  Huang

doi:10.6180/jase.202506_28(6).0013

Predicting the capillary water absorption values of building stones by hybrid models

Gang Li¹This email address is being protected from spambots. You need JavaScript enabled to view it., Yufei Fu², Zixin Deng¹, and Yuan Huang³

¹School of Design and Art, Jiangxi University of Finance and Economics; Nanchang JiangXi, 330032, China

²Faculty of Humanities and Arts, Macau University of Science and Technology; Macao 999078, China

³Art Institute, Xiangtan University; Xiangtan Hunan, 411105, China

Received: May 14, 2024
Accepted: August 5, 2024
Publication Date: September 11, 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.202506_28(6).0013

The determination of rock’s Capillary water absorption (CWA) requires extensive and complex empirical investigations, while forecasting algorithms could reduce the cost and time required. To achieve this goal, various rock records were collected from different rock types. For the forecasting model, two techniques named ANFIS and SVR were developed, with the model parameters determined using the Imperialist Competitive Algorithm (ICA). The proposed hybrid ICA-SVR and ICA-ANFIS models demonstrate exceptional performance in predicting CWA, with R 2 values exceeding 0.9851 and 0.9586 for the testing and training stages, respectively, indicating a strong correlation between predicted and measured CWA values. Considering all computed metrics, the SVR model optimized with ICA yielded better results than the ICA-ANFIS model in both testing and training stages. For example, the R 2 values for the ICA-SVR model were 0.9758 and 0.9747 for the testing and training datasets, respectively, with VAF values of 0.9889 and 0.9859 . The ICA-ANFIS model also produced acceptable results, though its performance was slightly weaker than the SVR model. Compared to a previous study, the proposed models show a significant improvement in efficiency, with the R² value increasing from 0.708 to 0.9889 . In summary, the enhanced ICA-SVR model can be considered a reliable and powerful tool for accurately determining the optimal values of the system’s key variables.

Keywords: Capillary water absorption; Building stones; Estimation; imperialist competitive algorithm; ANFIS; SVR

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