ItemArticle

Experimental data-driven machine learning approach for predicting workability in sustainable concrete using green material replacements

Title
Experimental data-driven machine learning approach for predicting workability in sustainable concrete using green material replacements
Creator
Janamala, Varaprasad; M, Beulah; Chaparala, Aparna; Daram, Suresh Babu
Description
Concrete workability is a critical factor governing the placement, compaction, and durability of fresh concrete, yet it remains less explored in data-driven studies compared to hardened properties. This study presents an experimentally validated machine learning framework for predicting fresh concrete workability, namely Compaction Factor Equivalent (CFE) and Vee Bee Time Equivalent (VBTE), using a newly generated laboratory dataset comprising 300 concrete mixes. The dataset was developed through controlled experiments by systematically varying the waterbinder ratio (W/B), aggregatebinder ratio (A/B), type of green material, and replacement percentage, with fly ash and ground granulated blast-furnace slag (GGBS) used as partial cement replacements to promote sustainability, aligning strategies with SDG 9 (Industry, Innovation, and Infrastructure) and SDG 12 (Responsible Consumption and Production). To capture the nonlinear relationships between mix design parameters and workability indicators, three ensemble learning modelsRandom Forest (RF), Gradient Boosting (GB), and Extreme Gradient Boosting (XGBoost)were developed and evaluated. Model performance was assessed using standard statistical metrics, including R, RMSE, MAE, and MAPE. The results indicate that boosting-based models outperform baseline approaches, with XGBoost achieving the highest prediction accuracy for both CFE and VBTE. By shifting the focus from hardened properties to fresh-state performance, this study addresses a critical research gap and demonstrates that ensemble machine learning models, when combined with experimentally generated datasets, can significantly reduce experimental workload while supporting intelligent and sustainable concrete mix design for practical engineering applications. 2026 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY-NC license. http://creativecommons.org/licenses/by-nc/4.0/
Source
Next Materials;Volume;12;Issue;;Article No.;102234;
Date
01-01-2026
Publisher
Elsevier B.V.
Subject
Compaction Factor Equivalent (CFE); Concrete workability; Ensemble learning; Experimental data; Gradient Boosting; Green material replacement; Machine learning; Predictive modelling; Random Forest; Sustainable concrete; Vee Bee Time Equivalent (VBTE); XGBoost
Coverage
Janamala V., Department of Electrical and Electronics Engineering, School of Engineering and Technology, Christ University, Karnataka, Bangalore, 560074, India; M B., Department of Civil Engineering, School of Engineering and Technology, Christ University, Karnataka, Bangalore, 560074, India; Chaparala A., Department of Computer Science and Engineering, R.V.R. & J.C. College of Engineering, Andhra Pradesh, Guntur, 522019, India; Daram S.B., Department of Electrical and Electronics Engineering, Mohan Babu University, Andhra Pradesh, Tirupati, 517102, India
Rights
All Open Access; Gold Open Access
Relation
ISSN: 29498228;
Format
online
Language
English
Type
Article

Citation

Janamala, Varaprasad; M, Beulah; Chaparala, Aparna; Daram, Suresh Babu, “Experimental data-driven machine learning approach for predicting workability in sustainable concrete using green material replacements,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 18, 2026, https://archives.christuniversity.in/items/show/22428.

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