Spatial correlation and pore morphology analysis of limestone calcined clay cement (LC3) via machine learning and image-based characterisation

Sui, Hao; Wang, Wei; Lin, Junlin; Tang, Zhao Qing; Yang, D.S.; Duan, Wenhui

doi:10.1016/j.conbuildmat.2023.132721

Cited by 9 publications

(1 citation statement)

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“…Machine learning is a promising solution to predicting the properties of multi-component materials. Although many studies have employed ML models to predict the properties of cementitious materials [19][20][21], only a few studies [22][23][24] have applied ML to LC 3 . Thus, there is a technological gap associated with the limited development and use of ML applications in LC 3 systems, at least compared with other cementitious materials (e.g., OPC and alkali-activated cement).…”

Section: Introductionmentioning

confidence: 99%

On the Prediction of the Mechanical Properties of Limestone Calcined Clay Cement: A Random Forest Approach Tailored to Cement Chemistry

Han,

Aylas-Paredes,

Huang

et al. 2023

Minerals

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Limestone calcined clay cement (LC3) is a sustainable alternative to ordinary Portland cement, capable of reducing the binder’s carbon footprint by 40% while satisfying all key performance metrics. The inherent compositional heterogeneity in select components of LC3, combined with their convoluted chemical interactions, poses challenges to conventional analytical models when predicting mechanical properties. Although some studies have employed machine learning (ML) to predict the mechanical properties of LC3, many have overlooked the pivotal role of feature selection. Proper feature selection not only refines and simplifies the structure of ML models but also enhances these models’ prediction performance and interpretability. This research harnesses the power of the random forest (RF) model to predict the compressive strength of LC3. Three feature reduction methods—Pearson correlation, SHapley Additive exPlanations, and variable importance—are employed to analyze the influence of LC3 components and mixture design on compressive strength. Practical guidelines for utilizing these methods on cementitious materials are elucidated. Through the rigorous screening of insignificant variables from the database, the RF model conserves computational resources while also producing high-fidelity predictions. Additionally, a feature enhancement method is utilized, consolidating numerous input variables into a singular feature while feeding the RF model with richer information, resulting in a substantial improvement in prediction accuracy. Overall, this study provides a novel pathway to apply ML to LC3, emphasizing the need to tailor ML models to cement chemistry rather than employing them generically.

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