Wu Siyuan, Liu Xinrui, Fu Bo
Department of Civil Engineering, Dalian University of Technology, Dalian, 116024, China.
China Construction Eighth Engineering Division Co., Ltd, Shanghai, 200122, China.
Sci Rep. 2025 Sep 26;15(1):33027. doi: 10.1038/s41598-025-18666-8.
The urgent demand for sustainable concrete has intensified as greenhouse gas emissions increasingly threaten global environmental stability. High-performance geopolymer concrete (HPGC) presents a promising sustainable alternative to traditional cement-based materials. The mix design of HPGC considering compressive strength, cost, and carbon emissions is crucial while hard to implement using traditional non-destructive methods. To resolve the issue, this study proposes an integrated framework combining automated machine learning (AutoML) modeling and multi-objective optimization (MOO) to balance compressive strength, cost, and carbon emissions in HPGC mix-design. Our research mainly includes three innovations: (1) We integrate ML-based predictive modeling with MOO framework for HPGC; (2) we establish an AutoML-Shapley Additive Explanations (SHAP) framework that harmonizes predictive accuracy with interpretability; and (3) we introduce the Pareto non-dominated sorting into MOO for HPGC. First, we employ an advanced AutoML algorithm to automatically develop a robust predictive model for HPGC compressive strength. Based on a database containing 295 mixes, the AutoML model demonstrated comparable accuracy compared to conventional ensemble learning methods, achieving a validation dataset determination coefficient (R) of 0.9280, root mean squared error (RMSE) of 5.2954 MPa, mean absolute error (MAE) of 4.2307 MPa, mean absolute percentage error (MAPE) of 0.0724, and a20 index of 0.9677. Subsequently, the SHAP method is applied to identify critical factors influencing HPGC performance and enhance the interpretability of the AutoML model. Finally, a Pareto non-dominated sorting algorithm is integrated into MOO to generate solutions that minimize unit cost and carbon emissions while maintaining compressive strength. The optimization framework reduces CO₂ emissions by 23-60% and unit costs by 16-36%, confirming the method's efficacy in balancing multiple objectives. This research advances eco-efficient concrete design methodologies and supports the broader adoption of green building technologies. It should be highlighted that the trained ML models are based on limited data, so the application of the models should be restricted to a certain range. The collected database will be expanded, which can resolve the ML limitations such as model generalizability.
随着温室气体排放对全球环境稳定性的威胁日益加剧，对可持续混凝土的迫切需求也日益增强。高性能地质聚合物混凝土（HPGC）是一种很有前景的可持续传统水泥基材料的替代品。考虑抗压强度、成本和碳排放的HPGC配合比设计至关重要，但使用传统无损方法难以实现。为解决这一问题，本研究提出了一个结合自动机器学习（AutoML）建模和多目标优化（MOO）的综合框架，以平衡HPGC配合比设计中的抗压强度、成本和碳排放。我们的研究主要包括三项创新：（1）我们将基于机器学习的预测建模与HPGC的MOO框架相结合；（2）我们建立了一个将预测准确性与可解释性相协调的AutoML-夏普利值附加解释（SHAP）框架；（3）我们将帕累托非支配排序引入HPGC的MOO中。首先，我们采用先进的AutoML算法自动开发一个用于HPGC抗压强度的稳健预测模型。基于一个包含295种配合比的数据库，该AutoML模型与传统集成学习方法相比具有相当的准确性，验证数据集的决定系数（R）为0.9280，均方根误差（RMSE）为5.2954MPa，平均绝对误差（MAE）为4.2307MPa，平均绝对百分比误差（MAPE）为0.0724，以及a20指数为0.9677。随后，应用SHAP方法识别影响HPGC性能的关键因素，并提高AutoML模型的可解释性。最后，将帕累托非支配排序算法集成到MOO中，以生成在保持抗压强度的同时使单位成本和碳排放最小化的解决方案。该优化框架将二氧化碳排放量降低了23-60%，单位成本降低了16-36%，证实了该方法在平衡多个目标方面的有效性。本研究推进了生态高效混凝土设计方法，并支持绿色建筑技术的更广泛应用。需要强调的是，训练得到的机器学习模型基于有限的数据，因此模型的应用应限制在一定范围内。收集的数据库将得到扩展，这可以解决诸如模型泛化能力等机器学习的局限性。
