Ternary blended concrete strength evaluation: experimental and artificial intelligence techniques
Abstract
Purpose
The purpose of this study is to forecast the mechanical properties of ternary blended concrete (TBC) modified with oyster shell powder (OSP) and shea nutshell ash (SNA) using deep neural network (DNN) models.
Design/methodology/approach
DNN models with three hidden layers, each layer containing 5–30 nodes, were used to predict the target variables (compressive strength [CS], flexural strength [FS] and split tensile strength [STS]) for the eight input variables of concrete classes 25 and 30 MPa. The concrete samples were cured for 3–120 days. Levenberg−Marquardt's backpropagation learning technique trained the networks, and the model's precision was confirmed using the experimental data set.
Findings
The DNN model with a 25-node structure yielded a strong relation for training, validating and testing the input and output variables with the lowest mean squared error (MSE) and the highest correlation coefficient (R) values of 0.0099 and 99.91% for CS and 0.010 and 98.42% for FS compared to other architectures. However, the DNN model with a 20-node architecture yielded a strong correlation for STS, with the lowest MSE and the highest R values of 0.013 and 97.26%. Strong relationships were found between the developed models and raw experimental data sets, with R2 values of 99.58%, 97.85% and 97.58% for CS, FS and STS, respectively.
Originality/value
To the best of the authors’ knowledge, this novel research establishes the prospects of replacing SNA and OSP with Portland limestone cement (PLC) to produce TBC. In addition, predicting the CS, FS and STS of TBC modified with OSP and SNA using DNN models is original, optimizing the time, cost and quality of concrete.
Keywords
Acknowledgements
Data and code generation availability: All the presented methodologies are implemented in Matlab. The complete results of mechanical properties (CS, FS and STS) and code generations for CS, FS and STS are freely accessible through open access in the Zenodo Repository at https://zenodo.org/doi/10.5281/zenodo.10997857.
Erratum: It has come to the attention of the publisher that the article “Ternary blended concrete strength evaluation: experimental and artificial intelligence techniques” by Oyebisi, S., Shammas, M.I., Owamah, H., and Oladeji, S., published in World Journal of Engineering, Vol. ahead-of-print, No. ahead-of-print, https://doi.org/10.1108/WJE-05-2024-0299, contained an error in the affiliation for Hilary Owamah. Department of Civil and Environmental Engineering, Delta State University, Cleveland, Mississippi, USA has been corrected to Department of Civil and Environmental Engineering, Delta State University, Abraka, Nigeria. The publisher sincerely apologises for this error and for any confusion caused.
Citation
Oyebisi, S., Shammas, M.I., Owamah, H. and Oladeji, S. (2024), "Ternary blended concrete strength evaluation: experimental and artificial intelligence techniques", World Journal of Engineering, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/WJE-05-2024-0299
Publisher
:Emerald Publishing Limited
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