Journal of Applied Science and Engineering

Published by Tamkang University Press

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Material Engineering

Yan YuanThis email address is being protected from spambots. You need JavaScript enabled to view it.

Faculty of Civil and Architectural Engineering, Zhengzhou University of Science & Technology; Henan Zhengzhou,450064, China

 

Received: February 6, 2023
Accepted: July 18, 2023
Publication Date: September 24, 2023

 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.202404_27(4).0008  


In order to assess the compressive strength (CS) of high-performance concrete (HPC) prepared with fly ash and blast furnace slag, several artificial-based analytics were applied. This study, it was employed the Chimp optimizer (CO) to identify optimal values of determinative factors of Support vector regression (SVR) and adaptive neuro-fuzzy inference system (ANFIS), which could be adjusted to improve performance. The suggested approaches were established using 1030 tests, eight inputs (a primary component of mix designs, admixtures, aggregates, and curing age), and the CS as the forecasting objective. The outcomes were then contrasted with those found in the body of existing scientific literature. Calculation results point to the potential benefit of combining CO − SVR and CO − ANFIS study. When compared to the CO − SVR, the CO − ANFIS showed much higher R2 and lower Root means square error values. Comparing the findings shows that the created CO− ANFIS is superior to anything that has previously been published. In conclusion, the suggested CO − ANFIS analysis might be used to determine the proposed approach for estimating the CS of HPC augmented with blast furnace slag and fly ash.


Keywords: High-performance concrete; Compressive strength; Support vector regression; Adaptive neuro-fuzzy inference system; Optimization algorithm


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