Concrete compressive strength is usually determined 28 days after casting via crushing of samples. However, the design strength may not be achieved after this time-consuming and tedious process. While the use of machine learning (ML) and other computational intelligence methods have become increasingly common in recent years, findings from pertinent literatures show that the gradient-boosting ensemble models mostly outperform comparative methods while also allowing interpretable model. Contrary to comparison with other model types that has dominated existing studies, this study centres on a comprehensive comparative analysis of the performance of four widely used gradient-boosting ensemble implementations [namely, gradient-boosting regressor, light gradient-boosting model (LightGBM), extreme gradient boosting (XGBoost), and CatBoost] for estimation of the compressive strength of quaternary blend concrete. Given components of cement, Blast Furnace Slag (GGBS), Fly Ash, water, superplasticizer, coarse aggregate, and fine aggregate in addition to the age of each concrete mixture as input features, the performance of each model based on R², RMSE, MAPE and MAE across varying training–test ratios generally show a decreasing trend in model performance as test partition increases. Overall, the test results showed that CatBoost outperformed the other models with R², RMSE, MAE and MAPE values of 0.9838, 2.0709, 1.5966 and 0.0629, respectively, with further statistical analysis showing the significance of these results. Although the age of each concrete mixture was found to be the most important input feature for all four boosting models, sensitivity analysis of each model shows that the compressive strength of the mixtures does increase significantly after 100 days. Finally, a comparison of the performance with results from different ML-based methods in pertinent literature further shows the superiority of CatBoost over reported the methods.

混凝土抗压强度通常在浇注后 28 天通过破碎样品来测定。然而，经过这个耗时且繁琐的过程后，可能无法达到设计强度。尽管近年来机器学习（ML）和其他计算智能方法的使用变得越来越普遍，但相关文献的发现表明，梯度提升集成模型大多优于比较方法，同时也允许可解释的模型。与主导现有研究的其他模型类型相比，本研究集中于对四种广泛使用的梯度提升集成实现的性能进行全面比较分析[即梯度提升回归器、轻梯度提升模型（LightGBM）、极端梯度提升 (XGBoost) 和 CatBoost] 用于估计四元混合混凝土的抗压强度。给定水泥、高炉矿渣 (GGBS)、粉煤灰、水、高效减水剂、粗骨料和细骨料的成分以及每种混凝土混合物的年龄作为输入特征，每个模型的性能基于R ²、 RMSE、随着测试分区的增加，不同训练-测试比率的 MAPE 和 MAE 通常显示模型性能下降的趋势。总体而言，测试结果表明CatBoost优于其他模型，R ²、RMSE、MAE和MAPE值分别为0.9838、2.0709、1.5966和0.0629，进一步的统计分析显示了这些结果的显着性。尽管发现每种混凝土混合物的龄期是所有四种增强模型最重要的输入特征，但每个模型的敏感性分析表明，混合物的抗压强度在 100 天后确实显着增加。最后，将性能与相关文献中不同基于机器学习的方法的结果进行比较，进一步表明 CatBoost 相对于报道的方法的优越性。