A hybrid machine learning model to estimate self-compacting concrete compressive strength
Hai-Bang LY1, Thuy-Anh NGUYEN1, Binh Thai PHAM1, May Huu NGUYEN1,2
1. Civil Engineering Department, University of Transport Technology, Hanoi 100000, Vietnam 2. Civil and Environmental Engineering Program, Graduate School of Advanced Science and Engineering, Hiroshima University, Hiroshima 739-8527, Japan
This study examined the feasibility of using the grey wolf optimizer (GWO) and artificial neural network (ANN) to predict the compressive strength (CS) of self-compacting concrete (SCC). The ANN-GWO model was created using 115 samples from different sources, taking into account nine key SCC factors. The validation of the proposed model was evaluated via six indices, including correlation coefficient (R), mean squared error, mean absolute error (MAE), IA, Slope, and mean absolute percentage error. In addition, the importance of the parameters affecting the CS of SCC was investigated utilizing partial dependence plots. The results proved that the proposed ANN-GWO algorithm is a reliable predictor for SCC’s CS. Following that, an examination of the parameters impacting the CS of SCC was provided.
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