Metaheuristic-optimized machine learning models for predicting compressive strength and assessing sustainability of waste glass powder additive mortars

The research hypothesis of this study is that combining metaheuristic optimization algorithms with ensemble machine learning can significantly improve the accuracy of predicting the compressive strength of mortars containing waste glass powder. The dataset consists of 281 experimental data points compiled from 17 different scientific studies, covering nine input features: cement, sand, glass powder, water, water-to-binder ratio, particle size, curing age, slag, and superplasticizer. The data shows that the PSO-RF model provides the highest predictive performance, achieving an R2 value of 0.943 on test data and 0.841 in real-world experimental validation. Notable findings indicate that curing age and water content are the most critical variables for strength, while a 10% glass powder replacement at 28 days offers the optimal balance between structural performance and environmental sustainability. This information serves as a data-driven decision support system for engineers and researchers to optimize mortar mix designs while reducing carbon emissions and energy consumption.本研究的研究假设为：将元启发式优化算法（metaheuristic optimization algorithms）与集成机器学习（ensemble machine learning）相结合，可显著提升含废玻璃粉砂浆抗压强度的预测精度。本数据集共包含281组实验数据点，均汇编自17项不同的科学研究，涵盖9项输入特征：水泥、砂、玻璃粉、水、水胶比、粒径、养护龄期、矿渣以及超塑化剂。数据表明，PSO-RF模型的预测性能最优，在测试集上的决定系数（R²）达0.943，在真实世界实验验证中亦可达0.841。本研究的重要发现显示：养护龄期与含水率是影响砂浆强度的最关键变量；而在28天养护龄期下采用10%的玻璃粉替代率，可实现结构性能与环境可持续性的最优平衡。本数据集可作为数据驱动的决策支持系统，助力工程师与研究人员优化砂浆配合比设计，同时降低碳排放与能源消耗。