Estimating compressive strength of lightweight foamed concrete using neural, genetic and ensemble machine learning approaches

Article


Salami, Babatunde Abiodun, Iqbal, Mudassir, Abdulraheem, Abdulazeez, Jalal, Fazal E., Alimi, Wasiu, Jamal, Arshad, Tafsirojjaman, T., Liu, Yue and Bardhan, Abidhan. 2022. "Estimating compressive strength of lightweight foamed concrete using neural, genetic and ensemble machine learning approaches." Cement and Concrete Composites. 133, pp. 1-16. https://doi.org/10.1016/j.cemconcomp.2022.104721
Article Title

Estimating compressive strength of lightweight foamed concrete using neural, genetic and ensemble machine learning approaches

ERA Journal ID21082
Article CategoryArticle
AuthorsSalami, Babatunde Abiodun, Iqbal, Mudassir, Abdulraheem, Abdulazeez, Jalal, Fazal E., Alimi, Wasiu, Jamal, Arshad, Tafsirojjaman, T., Liu, Yue and Bardhan, Abidhan
Journal TitleCement and Concrete Composites
Journal Citation133, pp. 1-16
Article Number104721
Number of Pages16
Year2022
PublisherElsevier
Place of PublicationUnited Kingdom
ISSN0958-9465
1873-393X
Digital Object Identifier (DOI)https://doi.org/10.1016/j.cemconcomp.2022.104721
Web Address (URL)https://www.sciencedirect.com/science/article/pii/S0958946522003146
Abstract

Foamed concrete is special not only in terms of its unique properties, but also in terms of its challenging compositional mixture design, which necessitates multiple experimental trials before obtaining the desired property like compressive strength. Regardless of design challenges, artificial intelligence (AI) techniques have shown to be useful in reliably estimating desired concrete properties based on optimized mixture proportions. This study proposes AI-based models to predict the compressive strength of foamed concrete. Three novel AI approaches, namely artificial neural network (ANN), gene expression programming (GEP), and gradient boosting tree (GBT) models, were employed. The models were developed using 232 experimental results, considering easily acquired variables, such as the density of concrete, water-cement ratio and sand-cement ratio as inputs to estimate the compressive strength of foamed concrete. In training the models, 80% of the experimental data was used and the rest was used to validate the models. The optimized models were selected using their respective best hyper-parameters on trial and error basis; variable number of hidden layers, number of neurons and training algorithms were used for ANN, number of chromosomes, head size, number of genes, variable function set for the GEP and GBT employed number of trees, maximal depth and learning rate. The trained models were validated using parametric and sensitivity analyses of a simulated dataset. The prediction abilities of proposed models were evaluated using the coefficient of correlation (R), mean absolute error (MAE), and root mean squared error (RMSE). For the validation data, empirical results from the performance evaluation revealed that GBT model (R = 0.977, MAE = 1.817 and RMSE = 2.69) has relative superior performance with highest correlation and least error in comparison with ANN (R = 0.975, MAE = 2.695 and RMSE = 3.40) and GEP (R = 0.96, MAE = 2.07 and RMSE = 2.80). The study concludes that the developed GBT model offered reliable accuracy in predicting the compressive strength of foamed concrete. Finally, the simple prediction equation generated from the GEP model signifies its importance and can reliably be used in estimating compressive strength of foamed concrete. It is recommended that the prediction models shall be used for the ranges of input variables employed in this study.

KeywordsArtificial neural network; Foamed concrete; Gene expression programming; Gradient boosting tree; Lightweight concrete; Optimization
FunderBeijing University of Technology
Byline AffiliationsKing Fahd University of Petroleum and Minerals, Saudi Arabia
Shanghai Jiao Tong University, China
University of Engineering and Technology, Pakistan
Imam Abdulrahman Bin Faisal University, Saudi Arabia
University of Adelaide
Centre for Future Materials
University of Science and Technology Beijing, China
National Institute of Technology Patna, India
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