Prediction and Feature Analysis of Self-Compacting Concrete Strength Using Machine Learning with Recycled Coarse Aggregate and Supplementary Cementitious Materials

Aug 6, 2026

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A machine learning framework predicts the compressive strength (CS) of self-compacting concrete (SCC) by incorporating recycled coarse aggregate (RCA) and supplementary cementitious materials (SCMs), supporting sustainable construction through data-driven mix optimization. The study uses 337 SCC mixtures with controlled ranges for cement content, SCM dosage, water-to-binder ratio, and curing age. Five regression models are compared, with Gradient Boosting delivering the best performance (R²=0.8876, RMSE=4.032 MPa, MAE=2.6382 MPa). SHAP interpretation identifies cement content and curing age as the most influential inputs, demonstrating interpretability and reducing reliance on time-intensive laboratory testing.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages22

Reading Time55 min

AccessFree to download

Introduction

Construction and demolition waste and sustainability context

Cement emissions and mitigation via SCMs

Role of SCC and motivation for combined modeling

| Research Paper
Prediction and Feature Analysis of Self-Compacting Concrete Strength Using Machine Learning with Recycled Coarse Aggregate and Supplementary Cementitious Materials
Kavendra Pulkit a, *, ShiviNigam b
aAssitant Manager, Bureau Veritas India Pvt Limited, Noida, Uttar Pradesh, India 201301 b Engineer, Bureau Veritas India Pvt limited, Noida, Uttar Pradesh, India 201301 | | | | --- | --- | --- | | AR TICLE INF O
Article history
Received : 6 July 2025
Revised : 2nd September 2025
Accepted : 5 September 2025
Keywords:
Machine Learning Gradient Boosting K Fold Cross Validation
Hyperparameter Tuning | | A B S T RA C T
This study presents a machine learning based approach to predict the CS of SCC incorporating RCA and supplementary cementitious materials (SCMs), aimed at promoting sustainable construction practices. A dataset comprising 337 SCC mixes with varying input parameters such as cement (40–635 kg/m³), SCMs (0–592 kg/m³), water tobinder ratio (0.25–0.45), and curing age (7–120 days) was utilized. Five regression models were evaluated-Extra Trees, AdaBoost, SVR Gradient Boosting, and ANN. Among them, Gradient Boosting achieved the highest accuracy with R² = 0.8876, RMSE = 4.032 MPa, and MAE=2.6382 MPa. Extra Trees followed closely with R²=0.8309 and RMSE=4.9444 MPa. SHAP interpretability analysis revealed that cement content and curing age were the most influential parameters in predicting CS. The study confirms the effectiveness of ML models in replacing time consuming lab tests and provides an interpretable, data driven framework for optimizing concrete mix design using industrial waste materials. | 1 Introduction The increasing volume of construction and demolition (C&D) waste poses a significant global sustainability issue, as it makes up almost half of the world's total solid waste and plays a major role in environmental harm and health risks [1] . Sustainable practices like material reuse and recycling have become more popular as they help lessen environmental impacts by promoting efficient resource recovery. The integration of circular economy principles focused on sustainable production and consumption has demonstrated potential in reducing waste and improving economic resilience [2] . Furthermore, the development of policies and legislative support is essential, as financial incentives and regulations can encourage waste minimization [3] . Additionally, raising awareness and educating stakeholders can enhance the effectiveness of implementation outcomes [4] . Even with these advancements, practical issues such as regulatory inconsistencies and local economic limitations need to be tackled to achieve sustainable and efficient management of construction and demolition waste worldwide. Cement production has a considerable environmental impact, mainly because of its high energy use and significant carbon dioxide (CO2) emissions. The production of cement accounts for about 5% of global industrial energy consumption, with emissions varying between 0.65 and 0.92 tons of CO2 for each ton of cement produced [5] . This process contributes to greenhouse gas emissions and results in ecological degradation due to resource extraction and pollution. The cement industry is one of the largest sources of global greenhouse gas emissions because of its energy-intensive manufacturing process. This is particularly evident during the kiln and calcination stages, where calcium carbonate breaks down into calcium oxide, resulting in substantial CO₂. This process produces nitrogen oxides (NOₓ) and sulfur dioxide (SO₂), which worsen environmental and health issues [6]. In response, strategies for mitigation such as carbon capture technologies, the use of low carbon materials, and the adoption of blended cements that include materials like limestone have been developed, which could reduce carbon footprints by as much as 6.4%[7] . Furthermore, policies that promote circular economy practic

FAQs

What inputs and dataset size are used to predict SCC strength?

The dataset includes 337 SCC mixes with varying cement content, SCM quantities, water-to-binder ratio, and curing age. These inputs support training and evaluation of predictive models for compressive strength.

Which machine learning model achieved the highest prediction accuracy?

Gradient Boosting achieved the best accuracy with R²=0.8876, RMSE=4.032 MPa, and MAE=2.6382 MPa. Extra Trees ranked next closely with R²=0.8309 and RMSE=4.9444 MPa.

Which factors were found most influential for predicting compressive strength?

SHAP interpretability analysis shows cement content and curing age are the most influential parameters. These variables drive the model’s predictions more than other mixture inputs.

Aug 6, 2026

Upload by Aria