Buy article PDF
The purchased file will be sent to you
via email after the payment is completed.
US$ 35
|
Computers and Concrete Volume 33, Number 1, January 2024 , pages 55-75 DOI: https://doi.org/10.12989/cac.2024.33.1.055 |
|
|
|
Assessment of compressive strength of high-performance concrete using soft computing approaches |
||
Chukwuemeka Daniel, Jitendra Khatti and Kamaldeep Singh Grover
|
||
| Abstract | ||
| The present study introduces an optimum performance soft computing model for predicting the compressive strength of high-performance concrete (HPC) by comparing models based on conventional (kernel-based, covariance function-based, and tree-based), advanced machine (least square support vector machine-LSSVM and minimax probability machine regressorMPMR), and deep (artificial neural network-ANN) learning approaches using a common database for the first time. A compressive strength database, having results of 1030 concrete samples, has been compiled from the literature and preprocessed. For the purpose of training, testing, and validation of soft computing models, 803, 101, and 101 data points have been selected arbitrarily from preprocessed data points, i.e., 1005. Thirteen performance metrics, including three new metrics, i.e., a20-index, index of agreement, and index of scatter, have been implemented for each model. The performance comparison reveals that the SVM (kernel-based), ET (tree-based), MPMR (advanced), and ANN (deep) models have achieved higher performance in predicting the compressive strength of HPC. From the overall analysis of performance, accuracy, Taylor plot, accuracy metric, regression error characteristics curve, Anderson-Darling, Wilcoxon, Uncertainty, and reliability, it has been observed that model CS4 based on the ensemble tree has been recognized as an optimum performance model with higher performance, i.e., a correlation coefficient of 0.9352, root mean square error of 5.76 MPa, and mean absolute error of 4.1069 MPa. The present study also reveals that multicollinearity affects the prediction accuracy of Gaussian process regression, decision tree, multilinear regression, and adaptive boosting regressor models, novel research in compressive strength prediction of HPC. The cosine sensitivity analysis reveals that the prediction of compressive strength of HPC is highly affected by cement content, fine aggregate, coarse aggregate, and water content. | ||
| Key Words | ||
| compressive strength; hypothesis testing; machine learning models; multicollinearity | ||
| Address | ||
| Chukwuemeka Daniel: Pan African University Institute of Basic Sciences Technology and Innovation, Hosted atJomo Kenyatta University of Agriculture and Technology, Kenya Jitendra Khatti and Kamaldeep Singh Grover: Department of Civil Engineering, Rajasthan Technical University, Kota, Rajasthan, India | ||