Using The Support Vector Regression Model With IGWO And DA Algorithms To Predict High-performance Concrete’s Compressive Strength

Huifang Chen; Lingyang Li

doi:10.6180/jase.202308_26(8).0013

Using The Support Vector Regression Model With IGWO And DA Algorithms To Predict High-performance Concrete’s Compressive Strength

Computer Science and Information Engineering

Huifang Chen and Lingyang LiThis email address is being protected from spambots. You need JavaScript enabled to view it.

Cangzhou Jiaotong College, Cangzhou 061100, China

Received: July 6, 2022
Accepted: September 15, 2022
Publication Date: November 15, 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.202308_26(8).0013

ABSTRACT

Concrete is an important factor in construction. It is necessary to use a special structure using high-performance concrete. High-performance concrete (HPC) is concrete with high strength and performance. Predicting the compressive strength (CS) of concrete is done in the laboratory, so it takes time. To save time, use a software model to predict the compressive strength of concrete. Support Vector Regression (SVR) is an innovative model for predicting most models, and SVR belongs to the Support Vector Machine (SVM) regression model. This model uses dual Improved Grey Wolf optimizer (I_GWO) and Dragonfly optimization algorithms (DA) for better output. Both of these are innovations in hidden layer optimization. Models and algorithms are specified in combination with SVR-GWO and SVR-DA. This modeling is error-prone and contains RMSE, MAPE, MDAPE, WAPE, and SMAPE and is shown as a parameter and the amount of R2 most specified in any hybrid model.

Keywords: High-performance-concrete; compressive strength; Improved Grey Wolf algorithm; Dragonfly optimization algorithm; support vector machine

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