Abstract
This investigation develops a machine learning model, namely the k-nearest neighbors (k-NN) model, for quickly forecasting the compressive strength at 28 age-days of high-performance concrete (HPC). To achieve this, the k-NN was first established through a total of 56 experimental data points collected from a published document. For that the cement (Ce), strength of cement (Sc), fly ash (Fa), water to binder ratio (W/B), sand (Sa), coarse aggregate (Co), air entraining (Ae), and superplasticizer (Sp) were selected as input parameters, whereas the compressive strength (\(f_{c - 28}^{^{\prime}}\)) was considered an output variable. The data set was divided into two sets, one for training purposes and one for testing purposes, according to an approximate ratio of 7:3. Five statistical indicators, including the correlation coefficient (R2), mean square error (MSE), mean absolute error (MAE), and root mean square error (RMSE), and \(\alpha_{20}\) were elected to evaluate the performance of the proposed k-NN model. The results showed that the k-NN model performed a good prediction of the compressive strength of HPC with high values of R2 (0.92) and \(\alpha_{20}\) (0.98) coefficients and small values of MAE (2.77 MPa), MSE (11.18 MPa2), and RMSE (3.34 MPa) coefficients. Furthermore, the sensitivity and shapely additive explanations analysis revealed that the cement is the most important input parameter in the k-NN model to estimate the compressive strength of HPC. Finally, a graphical user interface (GUI) tool and a web application (WA) were constructed based on the proposed k-NN model to help rapidly determine the compressive strength of HPC.
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All the data used for the development of the model has already been included in the submitted document. Other data related to this research may be generated and made available on reasonable request from the corresponding author.
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This research was funded by Ho Chi Minh University of Technology (HCMUT), VNU-HCM.
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Phan, TD. Fast prediction of the compressive strength of high-performance concrete through a k-nearest neighbor approach. Asian J Civ Eng 25, 51–66 (2024). https://doi.org/10.1007/s42107-023-00756-5
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DOI: https://doi.org/10.1007/s42107-023-00756-5