Compressive strength prediction of high-performance concrete using gradient tree boosting machine

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• MARS-GBM is adopted to predict the compressive strength of high-performance concrete. • MARS is used to extract the inputs’ parameters. • GBM is used to predict the strength and calculate the input parameter impacts. • MARS-GBM, -KRR, and -GPR are compared to show the improvements of the proposed models. • MARS-GBM outperforms other and previous studies models. In structural engineering, concrete compressive strength (CCS) is the most important performance parameter for designing the conventional concrete and high-performance concrete (HPC) structures. The precise prediction of this parameter becomes more crucial when considering this parameter for cost-benefits analysis and time point of view. This research investigates the multivariate adaptive regression splines model (MARS) as a feature extraction method to extract the optimum inputs that use to design the HPC. Furthermore, the extracted feature is feed to a gradient tree boosting machine (GBM) learning technique to predict the CCS. In addition, a comparative study has been done using different framework models (Kernel ridge regression and Gaussian process regression) to find its robustness. A total of 1030 data sets of eight input variables, i.e., cement, blast furnace slag, water, superplasticizer, fine aggregate, concrete age, etc. are used as inputs to estimate the CCS of HPC. The results of the analysis show that the relative importance of each parameters’ weights during the processing of GBM. Amongst the six most influential parameters, concrete age was found to be highly sensitive to predict the CCS. Moreover, the integrated MARS-GBM approach shows a simplified approach for the prediction of CCS of HPC based on different fitness indices (e.g., correlation coefficient and mean absolute error are 0.965 and 0.037 MPa, respectively). Therefore, this research concludes that such an ensemble approach can be a viable option to achieve higher performance with statistical accountability.