The cementitious matrix of high-performance concrete (HPC) is highly complex, and ambiguity exists with its mix design. Compressive strength can vary with the composition and proportion of constituent material used. To predict the strength of such a complex matrix the use of robust and efficient machine learning approaches has become indispensable. This study uses machine intelligence algorithms with individual learners and ensemble learners (bagging, boosting) to predict the strength of (HPC) prepared with waste materials. This is done by employing Anaconda (Python). Ensemble learner bagging, adaptive boosting algorithm, and random forest as modified bagging algorithm are employed to construct strong ensemble learner by incorporating weak learner. The ensemble learners are used on individual learners or weak learners including support vector machine and decision tree through regression and multilayer perceptron neural network. The data consists of 1030 data samples in which eight parameters namely cement, water, sand, gravels, superplasticizer, concrete age, fly ash and granulated blast furnace slag were chosen to predict the output. Twenty bagging and boosting sub-models are trained on data and optimization was done to give maximum R². The test data is also validated by means of K-Fold cross-validation using R², MAE, and RMSE. Moreover, evaluation of ensemble models with individual one is also checked by statistical model performance index (e.g., MAE, MSE, RMSE, and RMLSE). The result suggested that the individual model response is enhanced by using the bagging and boosting learners. Overall, random forest and decision tree with bagging give the robust performance of the models with R² = 0.92 with the least errors. On average, the ensemble model in machine learning would enhance the performance of the model.

高性能混凝土（HPC）的胶结基质非常复杂，其混合设计存在歧义。抗压强度可随所用组成材料的组成和比例而变化。为了预测这种复杂矩阵的强度，使用健壮和高效的机器学习方法已变得不可或缺。这项研究将机器智能算法与单个学习者和整体学习者（装袋，提升）一起使用，以预测用废料制备的（HPC）的强度。这是通过使用Anaconda（Python）完成的。采用集合学习器袋装，自适应提升算法和随机森林作为改进的袋装算法，通过结合弱学习者来构造强大的整体学习器。通过回归和多层感知器神经网络，集成学习器可用于单个学习器或弱学习器，包括支持向量机和决策树。该数据由1030个数据样本组成，其中选择了八个参数，即水泥，水，沙子，砾石，高效减水剂，混凝土年龄，粉煤灰和高炉矿渣颗粒来预测产量。训练了20个装袋和提升子模型的数据，并进行了优化以提供最大R²。还通过使用R ²，MAE和RMSE的K-fold交叉验证来验证测试数据。此外，还通过统计模型性能指标（例如，MAE，MSE，RMSE和RMLSE）来检查具有单个模型的集成模型的评估。结果表明，通过使用袋装和助推学习器可以增强个体模型的反应。总体而言，带有装袋法的随机森林和决策树为R ²  = 0.92的模型提供了鲁棒的性能，且误差最小。平均而言，机器学习中的集成模型将提高模型的性能。