Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Janamala, Varaprasad; M, Beulah; Chaparala, Aparna; Daram, Suresh Babu Title A name given to the resource Experimental data-driven machine learning approach for predicting workability in sustainable concrete using green material replacements Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived Next Materials;Volume;12;Issue;;Article No.;102234; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1016/j.nxmate.2026.102234" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.nxmate.2026.102234</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105038237896?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105038237896?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant 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. &amp; 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 Description An account of the resource 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/ Subject The topic of the resource 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 Publisher An entity responsible for making the resource available Elsevier B.V. Relation A related resource ISSN: 29498228; Language A language of the resource English Type The nature or genre of the resource Article Rights Information about rights held in and over the resource All Open Access; Gold Open Access Format The file format, physical medium, or dimensions of the resource online