Recent Advances in Sustainable Construction Materials and the Applications of Machine Learning in Construction Technology

Abstract

The construction sector must reduce the environmental burden of its materials while keeping structures safe, durable and affordable. Sustainable construction materials—supplementary cementitious materials (SCMs), agricultural ashes, recycled aggregates, waste-derived fillers, fibres, polymer modifiers and bio-mediated self-healing systems—offer ways to cut clinker use, conserve natural resources and divert waste from landfill. Their performance, however, is sensitive to source variability, processing, dosage, curing and exposure, which makes mixture design and quality control harder than for conventional concrete. Machine learning (ML) can help by learning the nonlinear links between composition, processing and performance from experimental data. This review summarises recent advances in the main families of sustainable materials and examines how ML is being applied across construction technology: predicting strength and durability, optimising low-carbon mixtures, building fast surrogates of structural response and automating inspection. The review finds that the value of ML depends more on data quality, representative material variability and honest validation than on the choice of algorithm, and that sustainability claims should be judged on a functional, life-cycle basis rather than on waste content alone. A simple integrated framework linking material characterisation, testing, ML and environmental assessment is proposed, and priorities for future research are identified.

Introduction

The text reviews sustainable construction materials and the role of Machine Learning (ML) in developing eco-friendly, durable, and cost-effective concrete. Concrete production contributes significantly to carbon emissions because of Portland cement manufacturing, while natural aggregate extraction and construction waste disposal create additional environmental problems. Sustainable construction aims to reduce these impacts by using alternative materials, recycling waste, improving durability, and assessing environmental effects throughout a structure's life cycle.

1. Advances in Sustainable Construction Materials

The text discusses several materials that can reduce the environmental impact of concrete:

• Supplementary Cementitious Materials (SCMs): Fly ash, blast-furnace slag, and rice husk ash partially replace cement, reducing clinker consumption and carbon emissions. They can improve later-age strength and durability, although their performance depends on composition and processing.

• Recycled Aggregates: Obtained from demolished concrete, these aggregates reduce construction waste and conserve natural resources. However, their higher porosity and water absorption may reduce concrete strength and durability.

• Waste Marble: Marble dust can act as a filler and partially replace cement or fine aggregate. Moderate quantities may maintain or improve concrete properties, while excessive replacement can reduce strength.

• Fibres and Polymer Modifiers: Polypropylene fibres help control cracking and improve toughness, while polymer additives can enhance cohesion, adhesion, and water resistance. Their effectiveness depends on the type and dosage used.

• Bio-Mediated Self-Healing Concrete: Bacteria such as Bacillus subtilis can produce calcium carbonate that seals cracks, potentially improving durability and extending service life. However, bacterial survival, cost, and long-term performance remain challenges.

No single material is suitable for every application. Material selection must consider strength, durability, cost, local availability, and environmental impact.

2. Applications of Machine Learning in Construction

Machine Learning helps engineers predict concrete performance and optimise material mixtures using experimental data. Common techniques include artificial neural networks, support vector regression, decision trees, random forests, and gradient-boosting algorithms.

The main applications include:

• Mechanical property prediction: Estimating compressive strength and other properties based on mixture proportions and curing conditions.

• Durability prediction: Assessing deterioration caused by freeze–thaw cycles and other environmental exposures.

• Mixture optimisation: Identifying concrete mixtures that achieve required strength while reducing cost and embodied carbon.

• Structural analysis: Predicting structural responses under different loading conditions, including blast effects.

• Crack detection and inspection: Using computer vision and deep learning to identify cracks and monitor infrastructure.

• Model interpretation: Using methods such as SHAP to explain predictions and improve engineers' confidence in ML models.

However, ML predictions depend on reliable, representative datasets. Models trained on laboratory experiments may require further validation before being applied to real-world structures.

3. Integrated Framework for Sustainable Concrete

The paper proposes combining material science, experimental testing, machine learning, and life-cycle assessment. The process involves:

1. Identifying and documenting material sources and processing methods.

2. Characterising materials through particle-size analysis, chemical composition, and microstructural studies.

3. Conducting standardised laboratory tests and creating consistent datasets.

4. Training and validating ML models using independent data.

5. Comparing candidate mixtures based on technical performance, cost, and environmental impact.

6. Experimentally verifying the recommended mixtures before practical application.

Conclusion

Sustainable construction materials can reduce cement use, conserve natural resources and divert waste, but their benefits depend on material quality, processing, dosage and service life. SCMs and agricultural ashes, recycled aggregates, waste marble, fibres, polymers and bacterial self-healing systems each offer clear advantages alongside practical limitations, and none is universally best. Machine learning is now applied across construction technology—to predict strength and durability, optimise low-carbon mixtures, build fast structural surrogates and automate inspection. Its usefulness depends on representative data, honest validation and interpretable outputs rather than on algorithm complexity. An integrated approach that combines characterisation, standardised testing, validated ML and life-cycle assessment offers the most reliable path towards construction materials that are both high-performing and genuinely sustainable. ML should be treated as a decision-support tool that complements experiments and engineering judgement.

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Copyright © 2026 Satej Shingan. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.