A hybrid machine learning model to estimate self-compacting concrete compressive strength

doi:10.1007/s11709-022-0864-7

Front. Struct. Civ. Eng.

2022, Vol. 16

Issue (8) : 990-1002 https://doi.org/10.1007/s11709-022-0864-7

RESEARCH ARTICLE

A hybrid machine learning model to estimate self-compacting concrete compressive strength

Hai-Bang LY¹, Thuy-Anh NGUYEN¹, Binh Thai PHAM¹, May Huu NGUYEN^1,²

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

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Abstract

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.

Keywords artificial neural network grey wolf optimize algorithm compressive strength self-compacting concrete

Corresponding Author(s): May Huu NGUYEN

Just Accepted Date: 23 August 2022 Online First Date: 01 November 2022 Issue Date: 02 December 2022

Cite this article:

Hai-Bang LY,Thuy-Anh NGUYEN,Binh Thai PHAM, et al. A hybrid machine learning model to estimate self-compacting concrete compressive strength[J]. Front. Struct. Civ. Eng., 2022, 16(8): 990-1002.

URL:

https://academic.hep.com.cn/fsce/EN/10.1007/s11709-022-0864-7
https://academic.hep.com.cn/fsce/EN/Y2022/V16/I8/990

Fig.1 The categorized leadership structure of grey wolves.

Tab.1 Statistical analysis of the inputs and SCC’s CS

Fig.2 Calibration of the optimal number of neurons and GWO’s population size based on: (a) average of R; (b) average of IA; (c) average of Slope; (d) average of RMSE; (e) average of MAE; (f) average of MAPE (The optimal zone is also highlighted).

Fig.3 Statistical convergence over 1000 random samplings for: (a) R; (b) IA; (c) Slope; (d) RMSE; (e) MAE; (f) MAPE.

Tab.2 Statistical analysis over 1000 random samplings quality assessment criteria

Fig.4 Probability distribution over 1000 random samplings for: (a) R; (b) IA; (c) Slope; (d) RMSE; (e) MAE; (f) MAPE.

Fig.5 Evaluation of: (a) R; (b) RMSE; (c) MAE during training processes.

Tab.3 Performance indicators of the optimal ANN-GWO algorithm

Fig.6 Regression graphs for: (a) the training part; (b) the testing part.

Fig.7 Prediction errors for: (a) training part; (b) testing part using ANN-GWO.

Fig.8 Confidence intervals for estimating the CS of SCC using ANN-GWO model.

Tab.4 Comparison of ANN-GWO with ANN, SVR, KNN, DT, and ELM

Tab.5 The sensitivity level

δ m n

of inputs at different percentiles in defining the

δ 50 n

of nth input is 0

Fig.9 Evaluation of PDP for each input variable.

Fig.10 Sensitivity index ranking.

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