Abstract
This study investigates the application of machine learning (ML) models to predict the compressive, flexural, and split tensile strength of concrete incorporating coconut shell as a partial replacement for coarse aggregate. This study utilizes a comprehensive dataset compiled from reputable literature, encompassing various experimental samples and input variables. Statistical analyses, including Pearson correlation and frequency distribution, lay the groundwork for preprocessing, involving standard scaling of features. Five prominent ML models, namely, support vector regression, random forest regression, gradient boosting regression, extreme gradient boosting regression, and multi-layer perceptron regression, are trained on the preprocessed dataset. The models' performances are rigorously evaluated using R2, RMSE, MAE, MAPE, and Comprehensive Performance Index (CPI) metrics. The feature importance analysis unveils the critical role of variables such as the age of concrete, coarse aggregate, and water in sha** concrete strength. gradient boosting regression consistently emerges as the top-performing model. This study concludes with insights into the implications for sustainable construction practices and suggests future research directions, emphasizing the continual refinement of predictive models and on-site validation for real-world applications in construction engineering.
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Data availability
The dataset will be available upon request to the corresponding author.
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RKT conceived and designed the study, while KK contributed to data interpretation. RKT and RA primarily handled data acquisition, with RA. preparing Figures 1-3. The main manuscript text was drafted by RKT, and all authors participated in critical revisions and manuscript review. The final version was collectively approved by RKT, RA, and KK
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Tipu, R.K., Arora, R. & Kumar, K. Machine learning-based prediction of concrete strength properties with coconut shell as partial aggregate replacement: A sustainable approach in construction engineering. Asian J Civ Eng 25, 2979–2992 (2024). https://doi.org/10.1007/s42107-023-00957-y
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DOI: https://doi.org/10.1007/s42107-023-00957-y