What does this research mean for the field?

A hybrid XGB-GBR machine learning model accurately predicts the compressive strength of concrete incorporating ternary industrial wastes, identifying curing age and coarse aggregate as the most influential predictive factors. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

The aim is to predict the compressive strength of concrete modified with ternary industrial wastes using machine learning algorithms.

July 13, 2026Open Access

Optimization and predictive measurement of compressive strength of iron ore slag modified concrete using data-driven supervised machine learning algorithms

MSMd. Habibur Rahman SobuzINTI International University MKMd. Kawsarul Islam KabboKhulna University of Engineering and Technology AAAbdullah AlzlfawiMajmaah University

Key Points

The aim is to predict the compressive strength of concrete modified with ternary industrial wastes using machine learning algorithms.
Developed a dataset of 366 mix ratios and corresponding compressive strength values.
Applied machine learning algorithms including extreme gradient boosting, random forest, and hybrid models for analysis.
Validated models through experimental evaluation including scanning electron microscopy.
Hybrid XGB-GBR model achieved the highest performance in training (R² = 0.911) and testing (R² = 0.869).
Identified curing age and coarse aggregate as the most influential factors on compressive strength predictions.
Created an interactive GUI to enhance practical applications and reduce material costs in concrete production.

Abstract

This study develops an integrated machine learning-experimental framework to predict the compressive strength (CS) of concrete incorporating ternary industrial wastes glass powder, marble powder, and iron ore slag. For this purpose, a dataset comprising 366 mix ratios and corresponding CS values was compiled from various sources for analysis. Advanced machine learning (ML) algorithms, including extreme gradient boosting (XGB), gradient boosting, and random forest (RF), were employed alongside hybrid techniques such as XGB-GBR and XGB-RF to evaluate the influence of these materials on strength. Based on the outcomes of the analysis, the hybrid XGB-GBR model demonstrates the highest balanced performance for both training (R 2 = 0.911) and testing (R 2 = 0.869) data sets. For validating the ML modeling and developing an interactive graphical user interface (GUI), experimental evaluation of CS and scanning electron microscopy was conducted. Additionally, feature importance modeling and optimization identified curing age and coarse aggregate as the most influential factors that would impact the model prediction. The contribution of this research lies in the combined modeling and experimental evaluation of a ternary waste concrete system, along with the development of a GUI. This deployable GUI will enhance the industrial applicability of ML-based concrete optimization by reducing material costs, minimizing trial batching, and supporting sustainable mix design practices.

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