Article(id=1321944728619303494, tenantId=1146029695717560320, journalId=1263187385517883426, issueId=1321944712408314326, articleNumber=null, orderNo=null, doi=10.14062/j.issn.0454-5648.20250811, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1762272000000, receivedDateStr=2025-11-05, revisedDate=1764259200000, revisedDateStr=2025-11-28, acceptedDate=null, acceptedDateStr=null, onlineDate=1793105783847, onlineDateStr=2026-10-27, pubDate=1773936000000, pubDateStr=2026-03-20, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793105783847, onlineIssueDateStr=2026-10-27, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793105783847, creator=13701087609, updateTime=1793105783847, updator=13701087609, issue=Issue{id=1321944712408314326, tenantId=1146029695717560320, journalId=1263187385517883426, year='2026', volume='54', issue='3', pageStart='821', pageEnd='1176', issueExtLink='null', onlineDate='null', pubDate='1770652800000', pubDateStr='2026-02-10', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1793105779983, creator='13701087609', updateTime=1793105779983, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext=null, issueFiles=null, downloadFileDto=null}, startPage=970, endPage=981, ext={EN=ArticleExt(id=1321944729793708616, articleId=1321944728619303494, tenantId=1146029695717560320, journalId=1263187385517883426, language=EN, title=Intelligent Mixture Design of Limestone Calcined Clay Cement Based on Ensemble Learning and Multi-Objective Optimization, columnId=1321944713276535255, journalTitle=Journal of the Chinese Ceramic Society, columnName=Special Issue on Genes and AI-Based Design of Cement-Based Materials—Research Articles, runingTitle=null, highlight=null, articleAbstract=
Introduction Limestone Calcined Clay Cement (LC3) has attracted attention as a promising low-carbon cementitious material due to its reduced CO2 emissions and satisfactory mechanical performance. However, its properties are strongly affected by raw-material composition, processing conditions, and mixture proportions. In this study, a database encompassing diverse material parameters and preparation processes was developed, and multiple machine-learning models were applied to predict the compressive strength of LC3 systems. The predictive accuracy of these models was further enhanced through the Bayesian optimization and the Boosting-based ensemble strategies. Based on the optimized predictive models, a multi-objective optimization framework was proposed to integrate compressive strength, materials cost, and embodied carbon emissions. A genetic algorithm was subsequently applied to identify optimal mixture proportions for LC3 matrices. The results showed that the proposed approach could achieve a balanced trade-off between mechanical performance and sustainability, thereby improving both the scientific robustness and practical applicability of LC3 mixture design. This work could provide a data-driven pathway for performance-oriented optimization of emerging low-carbon cementitious materials.
Methods A database of 291 LC3 mixtures was compiled, covering a wide range of compositions and curing conditions. Input variables included clinker, calcined clay, limestone, gypsum, water-to-binder ratio, calcination parameters, curing conditions, and curing age. The compressive strength served as an output. Correlation analysis and variance inflation factor (VIF) evaluation were applied to reduce multicollinearity. Four machine learning models (i.e., Multilayer Perceptron (MLP), Random Forest (RF), Gaussian Process Regression (GPR), and Extreme Gradient Boosting (XGB)) were trained and tuned via the Bayesian optimization. An ensemble framework was then constructed with MLP as the meta-learner and XGB, RF, and GPR as base models within a Boosting strategy to enhance accuracy.
In parallel, a multi-objective optimization model was formulated, targeting compressive strength, material cost, and embodied carbon emissions. Cost and emission factors were drawn from market and life cycle databases. Practical constraints on binder fractions, water-to-binder ratio, and curing requirements ensured a feasibility. The non-dominated Sorting Genetic Algorithm II (NSGA-II) was used to search for Pareto-optimal mixtures that reflect different trade-offs among strength, cost, and carbon footprint. Results and Discussion The ensemble learning notably improves prediction compared with individual models. The XGB provides the strongest performance among single algorithms, while GPR and RF also yield the good results. The MLP alone is weaker, but the predictive accuracy is further enhanced when integrating as a meta-learner in the Boosting ensemble. This underscores the advantage of combining diverse learners to model a complex LC3 behavior.
The variable importance analysis identifies curing age as a dominant positive factor, followed by calcined clay fineness and curing temperature. In contrast, the excessive replacement of calcined clay and limestone has a negative effect, revealing threshold behaviors. The interactions among clinker, calcined clay, and limestone confirm synergistic benefits in moderate levels, but dilution effects at a higher dosage. These findings clarify the nonlinear mechanisms affecting LC3 strength and validate the interpretability of the model.
The NSGA-II optimization generates a Pareto front of candidate solutions. A strength-prioritized mixture achieves mechanical properties, comparable to OPC concrete with a reduced carbon impact. A carbon-prioritized mixture minimizes emissions, while maintaining an acceptable strength, making it suitable for sustainability-driven projects. A balanced solution achieves compromise among all three objectives, representing a practical option for general engineering applications. The verification against experimental results confirms the reliability of predictions, demonstrating that the proposed framework can simultaneously address the performance, economic, and environmental targets.
Conclusions This study introduced an integrated framework that could combine ensemble learning with a multi-objective optimization for LC3 mixture design. The main contributions involved, 1) Boosting-based ensemble learning significantly enhanced the accuracy of compressive strength prediction; 2) Curing age, calcined clay fineness, and curing temperature were identified as key positive drivers, while excessive calcined clay and limestone replacement exerted negative influences; 3) NSGA-II effectively produced Pareto-optimal mixtures balancing strength, cost, and carbon emissions, offering flexible solutions under different priorities; and 4) The experimental validation confirmed that optimized LC3 mixtures could maintain competitive strength, while substantially reducing both cost and carbon footprint.
The integration of predictive modeling and optimization provided a powerful pathway for advancing LC3 research and practice. It could contribute a methodological innovation and demonstrate a clear potential for sustainable, design of low-carbon cementitious materials in the construction industry.
, authors=Jiaxuan YANG, Shiqi WANG, Fengyi ZHUO, Fuyuan GONG, authorsList=Jiaxuan YANG, Shiqi WANG, Fengyi ZHUO, Fuyuan GONG, authorCompany=null, correspAuthors=Fuyuan GONG, authorNote=
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石灰石煅烧黏土水泥（LC3）以其低碳排放量和优异性能迅速成为新型胶凝材料的研究热点，但其性能常受到原料组分、处理工艺、制备方法等因素的影响，其中，基体配比是决定其性能的关键因素，基于此，本工作搜集整理了涵盖不同材料组分参数、制备条件、处理工艺的数据库，采用多种单一机器学习模型进行抗压强度预测，并通过贝叶斯优化优化各模型参数，提升模型性能，进而采用Boosting方法提升单一模型性能，得到性能良好的LC3基体抗压强度预测模型，在此基础上，提出了结合抗压强度、成本与碳排放的多目标优化框架，并利用遗传算法获得LC3基体的最优配合比。结果表明，该方法能够兼顾力学性能与可持续性，显著提升设计的科学性与工程适用性。本工作为新型低碳水泥材料的智能配比设计提供了有效思路。
, authors=杨嘉轩, 王仕奇, 卓峰谊, 弓扶元, authorsList=杨嘉轩, 王仕奇, 卓峰谊, 弓扶元, authorCompany=null, correspAuthors=弓扶元, authorNote=
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弓扶元（1988—），男，博士，教授。
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杨嘉轩（2001—），男，博士研究生。
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杨嘉轩（2001—），男，博士研究生。
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