Concrete is the most commonly used construction material. However, its production leads to high carbon dioxide (CO₂) emissions and energy consumption. Therefore, developing waste-substitutable concrete components is necessary. Improving the sustainability and greenness of concrete is the focus of this research. In this regard, 899 data points were collected from existing studies where cement, slag, fly ash, superplasticizer, coarse aggregate, and fine aggregate were considered potential influential factors. The complex relationship between influential factors and concrete compressive strength makes the prediction and estimation of compressive strength difficult. Instead of the traditional compressive strength test, this study combines five novel metaheuristic algorithms with extreme gradient boosting (XGB) to predict the compressive strength of green concrete based on fly ash and blast furnace slag. The intelligent prediction models were assessed using the root mean square error (RMSE), coefficient of determination (R²), mean absolute error (MAE), and variance accounted for (VAF). The results indicated that the squirrel search algorithm-extreme gradient boosting (SSA-XGB) yielded the best overall prediction performance with R² values of 0.9930 and 0.9576, VAF values of 99.30 and 95.79, MAE values of 0.52 and 2.50, RMSE of 1.34 and 3.31 for the training and testing sets, respectively. The remaining five prediction methods yield promising results. Therefore, the developed hybrid XGB model can be introduced as an accurate and fast technique for the performance prediction of green concrete. Finally, the developed SSA-XGB considered the effects of all the input factors on the compressive strength. The ability of the model to predict the performance of concrete with unknown proportions can play a significant role in accelerating the development and application of sustainable concrete and furthering a sustainable economy.

混凝土是最常用的建筑材料。然而，其生产会导致大量二氧化碳（CO ₂）排放和能源消耗。因此，开发可替代废物的混凝土构件十分必要。提高混凝土的可持续性和绿色性是本研究的重点。在这方面，从现有研究中收集了 899 个数据点，其中水泥、矿渣、粉煤灰、高效减水剂、粗骨料和细骨料被认为是潜在的影响因素。影响因素与混凝土抗压强度之间的复杂关系，给抗压强度的预测和估算带来了困难。本研究没有采用传统的抗压强度测试，而是将五种新颖的元启发式算法与极限梯度提升（XGB）相结合，以预测基于粉煤灰和高炉矿渣的新混凝土的抗压强度。使用均方根误差（RMSE）、决定系数（R ²）、平均绝对误差（MAE）和方差解释（VAF）来评估智能预测模型。结果表明，松鼠搜索算法-极限梯度提升（SSA-XGB）的总体预测性能最好，R ²值为0.9930和0.9576，VAF值为99.30和95.79，MAE值为0.52和2.50，RMSE为1.34， 3.31 分别表示训练集和测试集。其余五种预测方法产生了有希望的结果。因此，所开发的混合 XGB 模型可以作为一种准确、快速的技术来预测新浇混凝土的性能。最后，开发的SSA-XGB考虑了所有输入因素对抗压强度的影响。该模型预测未知配比混凝土性能的能力可以在加速可持续混凝土的开发和应用以及促进可持续经济方面发挥重要作用。