Machine Learning Driven Fluidity and Rheological Properties Prediction of Fresh Cement-Based Materials

Controlling workability during the design stage of cement-based material mix ratios is a highly time-consuming and labor-intensive task. Applying artificial intelligence (AI) methods to predict and optimize the workability of cement-based materials can significantly enhance the efficiency of mix des...

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Veröffentlicht in:Materials 2024-11, Vol.17 (22), p.5400
Hauptverfasser: Liu, Yi, Mohammed, Zeyad M A, Ma, Jialu, Xia, Rui, Fan, Dongdong, Tang, Jie, Yuan, Qiang
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container_issue 22
container_start_page 5400
container_title Materials
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creator Liu, Yi
Mohammed, Zeyad M A
Ma, Jialu
Xia, Rui
Fan, Dongdong
Tang, Jie
Yuan, Qiang
description Controlling workability during the design stage of cement-based material mix ratios is a highly time-consuming and labor-intensive task. Applying artificial intelligence (AI) methods to predict and optimize the workability of cement-based materials can significantly enhance the efficiency of mix design. In this study, experimental testing was conducted to create a dataset of 233 samples, including fluidity, dynamic yield stress, and plastic viscosity of cement-based materials. The proportions of cement, fly ash (FA), silica fume (SF), water, superplasticizer (SP), hydroxypropyl methylcellulose (HPMC), and sand were selected as inputs. Machine learning (ML) methods were employed to establish predictive models for these three early workability indicators. To improve prediction capability, optimized hybrid models, such as Particle Swarm Optimization (PSO)-based CatBoost and XGBoost, were adopted. Furthermore, the influence of individual input variables on each workability indicator of the cement-based material was examined using Shapley Additive Explanations (SHAP) and Partial Dependence Plot (PDP) analyses. This study provides a novel reference for achieving rapid and accurate control of cement-based material workability.
doi_str_mv 10.3390/ma17225400
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subjects Artificial intelligence
Cement
Construction
Design optimization
Fly ash
Machine learning
Mathematical optimization
Mechanical properties
Methylcellulose
Optimization
Particle swarm optimization
Prediction models
Predictions
Rheological properties
Rheology
Shear stress
Silica fume
Superplasticizers
Viscosity
Workability
Yield stress
title Machine Learning Driven Fluidity and Rheological Properties Prediction of Fresh Cement-Based Materials
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