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Reduction of computational error by optimizing SVR kernel coefficients to simulate concrete compressive strength through the use of a human learning optimization algorithm

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Abstract

This research presents a new model for finding optimal conditions in the concrete technology area. To do that, results of a series of laboratory investigations on concrete samples were considered and used to design several artificial intelligence (AI) models. The data samples include 8 parameters i.e., silica fume replacement ratio, fly ash replacement ratio, fine aggregate, water content, high rate water reducing agent, coarse aggregate, total cementitious material, and age of samples, were used to predict and optimize the compressive strength of concrete samples. For optimization purposes, this study used a human learning optimization (HLO) algorithm to find the optimal results as well as optimizing the kernel coefficients of the support vector regression (SVR) models. Initially, to form the core of this research, various models were constructed and proposed to design the required relationship between the data using SVR. Since different SVR kernels have their own coefficients, using optimization theory, the probability of error in the models was reduced and the models were identified and executed with the highest accuracy. Finally, the polynomial model was selected as the model with the lowest computational error and the highest accuracy for evaluating the compressive strength of the concrete samples. The accuracy of the proposed SVR model for training and testing data was obtained as the coefficient of determination (R2) = 0.9694 and R2 = 0.9470, respectively. This function was considered as a relation, to be developed by the HLO algorithm to find optimal options under different conditions. The results for 14 samples, which are the most important examples of this research, showed that the optimal states are obtained with a high level of accuracy. This confirms the proper use/develop of the SVR-HLO algorithm in designing the predictive model as well as finding optimal conditions in the concrete technology area.

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Acknowledgements

This research was funded by the Faculty Start-up Grant of China University of Mining and Technology (Grant no. 102520282).

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Authors and Affiliations

  1. School of Mines, China University of Mining and Technology, Xuzhou, 221116, China

    Jiandong Huang

  2. School of Mines, China University of Mining and Technology, Xuzhou, 221116, China

    Yuantian Sun

  3. School of Civil and Transportation Engineering, Hebei University of Technology, Tianjin, 300401, China

    Junfei Zhang

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  1. Jiandong Huang
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  2. Yuantian Sun
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  3. Junfei Zhang
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Correspondence to Junfei Zhang.

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Huang, J., Sun, Y. & Zhang, J. Reduction of computational error by optimizing SVR kernel coefficients to simulate concrete compressive strength through the use of a human learning optimization algorithm. Engineering with Computers 38, 3151–3168 (2022). https://doi.org/10.1007/s00366-021-01305-x

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