Hybrid extreme gradient boosting regressor models for the multi-objective mixture design optimization of cementitious mixtures incorporating mine tailings as fine aggregates

IF 10.8 1区 工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY Cement & concrete composites Pub Date : 2024-10-04 DOI:10.1016/j.cemconcomp.2024.105787
Chathuranga Balasooriya Arachchilage, Guangping Huang, Jian Zhao, Chengkai Fan, Wei Victor Liu
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Abstract

The design of cementitious mixtures incorporating mine tailings as fine aggregates is a multi-objective optimization (MOO) problem, in which both the uniaxial compressive strength (UCS) and cost of the mixtures need to be considered simultaneously. Given that data-driven methods have shown promising results when solving similar MOO problems, this study developed an extreme gradient boosting regressor (XGBR) model on a dataset extracted from the literature to predict the UCS. Among the efforts taken to improve the models, a genetic algorithm (GA)-based XGBR model demonstrated the optimal prediction performance, with an R2 of 0.959. Next, the GA-XGBR model and a cost equation were used as objective functions in the MOO problem. The non-dominated sorting genetic algorithm with elite strategy (NSGA-II) was selected to solve the optimization problem. A case study was conducted, generating mixture designs that offered improved trade-offs between cost and UCS compared to experimental designs. Finally, a graphical user interface was developed to provide access to the prediction model and optimization method. Overall, this work can be used as a guide for optimal mixture designs as it facilitates informed decision-making before the actual applications.
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混合极端梯度提升回归模型用于将矿山尾矿作为细骨料的水泥基混合物的多目标混合物设计优化
以矿山尾矿为细集料的水泥基混合物设计是一个多目标优化(MOO)问题,需要同时考虑混合物的单轴抗压强度(UCS)和成本。鉴于数据驱动方法在解决类似的 MOO 问题时已显示出良好的效果,本研究在从文献中提取的数据集上开发了极端梯度提升回归器 (XGBR) 模型,用于预测 UCS。在改进模型的过程中,基于遗传算法(GA)的 XGBR 模型表现出最佳预测性能,R2 为 0.959。接下来,GA-XGBR 模型和成本方程被用作 MOO 问题的目标函数。我们选择了具有精英策略的非优势排序遗传算法(NSGA-II)来解决优化问题。进行了一项案例研究,与实验设计相比,生成的混合物设计能更好地权衡成本和 UCS。最后,还开发了一个图形用户界面,用于访问预测模型和优化方法。总之,这项工作可作为优化混合物设计的指南,因为它有助于在实际应用之前做出明智的决策。
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来源期刊
Cement & concrete composites
Cement & concrete composites 工程技术-材料科学:复合
CiteScore
18.70
自引率
11.40%
发文量
459
审稿时长
65 days
期刊介绍: Cement & concrete composites focuses on advancements in cement-concrete composite technology and the production, use, and performance of cement-based construction materials. It covers a wide range of materials, including fiber-reinforced composites, polymer composites, ferrocement, and those incorporating special aggregates or waste materials. Major themes include microstructure, material properties, testing, durability, mechanics, modeling, design, fabrication, and practical applications. The journal welcomes papers on structural behavior, field studies, repair and maintenance, serviceability, and sustainability. It aims to enhance understanding, provide a platform for unconventional materials, promote low-cost energy-saving materials, and bridge the gap between materials science, engineering, and construction. Special issues on emerging topics are also published to encourage collaboration between materials scientists, engineers, designers, and fabricators.
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