Optimizing rheological properties of 3D printed cementitious materials via ensemble machine learning

IF 11.1 1区 工程技术 Q1 ENGINEERING, MANUFACTURING
Muhammad Saeed Zafar , Farid Javadnejad , Maryam Hojati
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引用次数: 0

Abstract

The complex interaction between rheology-modifying admixtures and fresh cementitious mix printability limits 3D printing applications in construction. To optimize the properties of 3D printable concrete, this study presents a machine learning (ML)-based, knowledge-guided framework that integrates data-driven modeling with expert validation. A structured workflow uses a small dataset to predict and refine optimal mix designs. A total of 77 lab samples were prepared with varying amounts of nano-clay (NC), silica fume (SF), bentonite volclay (BC), and methylcellulose (MC). Their rheological properties, including plastic viscosity (VIS), dynamic yield stress (DYS), and static yield stress (SYS), were measured using a rheometer. Ensemble ML models were developed through automated preprocessing, cross-validated hyperparameter tuning, and RMSE-based selection. The top five models per rheological responses were combined using a voting regressor, improving predictive accuracy while mitigating overfitting. Predictions were visualized using contour maps from gridded synthetic data, revealing nonlinear interactions among input features. A key innovation is applying expert ratings to contour maps to guide the selection of high-performing mixes. This step allows domain knowledge to define acceptable printability ranges and helps address ML uncertainty from limited training data. Optimized mixes were selected based on rating maps and re-evaluated through additional rheology and 3D printing tests. The results demonstrated that the mixes met satisfactory extrudability and buildability requirements, confirming the validity of the defined expert rating criteria and the practical utility of the framework in optimizing 3D printable concrete mixes containing the defined additives. The proposed approach ensures both predictive robustness and practical applicability. It enables iterative refinement of models as new data becomes available and offers a systematic approach to navigating complex mix interactions. Overall, combining ensemble modeling, contour visualization, and knowledge-driven evaluation provides a powerful tool for advancing 3D concrete printing mix design.
通过集成机器学习优化3D打印胶凝材料的流变性能
流变改性外加剂和新鲜胶凝混合物之间复杂的相互作用限制了3D打印在建筑中的应用。为了优化3D可打印混凝土的性能,本研究提出了一种基于机器学习(ML)的知识导向框架,该框架将数据驱动建模与专家验证相结合。结构化工作流程使用小数据集来预测和优化最佳混合设计。总共77个实验室样品由不同数量的纳米粘土(NC)、硅灰(SF)、膨润土volclay (BC)和甲基纤维素(MC)制备。用流变仪测量了它们的流变特性,包括塑性粘度(VIS)、动态屈服应力(DYS)和静态屈服应力(SYS)。集成ML模型是通过自动预处理、交叉验证的超参数调优和基于rmse的选择开发的。每个流变反应的前五个模型使用投票回归器组合,提高了预测精度,同时减轻了过拟合。使用网格化合成数据的等高线图将预测可视化,揭示了输入特征之间的非线性相互作用。一个关键的创新是将专家评级应用于等高线地图,以指导高性能混合料的选择。这一步允许领域知识定义可接受的可打印范围,并帮助解决有限训练数据的ML不确定性。根据评级图选择优化的混合物,并通过额外的流变学和3D打印测试重新评估。结果表明,混合料满足了令人满意的可挤压性和可建造性要求,证实了所定义的专家评级标准的有效性,以及该框架在优化含有所定义添加剂的3D打印混凝土混合料方面的实用性。该方法具有预测鲁棒性和实用性。当新数据变得可用时,它支持模型的迭代细化,并提供了导航复杂混合交互的系统方法。总体而言,将集成建模、轮廓可视化和知识驱动评估相结合,为推进3D混凝土打印混合设计提供了强大的工具。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Additive manufacturing
Additive manufacturing Materials Science-General Materials Science
CiteScore
19.80
自引率
12.70%
发文量
648
审稿时长
35 days
期刊介绍: Additive Manufacturing stands as a peer-reviewed journal dedicated to delivering high-quality research papers and reviews in the field of additive manufacturing, serving both academia and industry leaders. The journal's objective is to recognize the innovative essence of additive manufacturing and its diverse applications, providing a comprehensive overview of current developments and future prospects. The transformative potential of additive manufacturing technologies in product design and manufacturing is poised to disrupt traditional approaches. In response to this paradigm shift, a distinctive and comprehensive publication outlet was essential. Additive Manufacturing fulfills this need, offering a platform for engineers, materials scientists, and practitioners across academia and various industries to document and share innovations in these evolving technologies.
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