Hybrid machine learning for SCC strength prediction using metaheuristic optimization.

Akhilendra Sharma, Rahul Biswas, Sharad Singh, Manish Kumar, Nadia Razali

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Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

5 authors.

Akhilendra SharmaDepartment of Applied Mechanics, Visvesvaraya National Institute of Technology, Nagpur, India.

Rahul BiswasDepartment of Applied Mechanics, Visvesvaraya National Institute of Technology, Nagpur, India. rahulbiswas@apm.vnit.ac.in.

Sharad SinghDepartment of Applied Mechanics, Visvesvaraya National Institute of Technology, Nagpur, India.

Manish KumarDepartment of Civil Engineering, SRM Institute of Science and Technology (SRMIST), Deemed to be University, Tiruchirappalli, Tamil Nadu, India.

Nadia RazaliMalaysian Institute of Chemical and Bioengineering Technology (UniKL MICET), Universiti Kuala Lumpur, Lot 1988, Kawasan Perindustrian Bandar Vendor, Taboh Naning, 78000, Alor Gajah, Melaka, Malaysia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Self-Compacting Concrete (SCC) represents a significant innovation in modern concrete technology owing to its excellent flowability, workability, and mechanical performance. Accurate prediction of SCC compressive strength is essential for optimizing mix design, minimizing experimental effort, and promoting sustainable material usage. In this study, a hybrid Machine Learning (ML) framework was developed by integrating Gradient Boosting (GB), Adaptive Boosting (ADA), and Random Forest (RF) models, optimized through metaheuristic algorithms such as Grey Wolf Optimizer (GWO), Mountain Gazelle Optimizer (MGO), Brown Bear Optimizer Algorithm (BBOA), and Fox Optimizer (FO). A dataset containing 691 SCC samples with varied mix proportions and curing conditions was utilized for model development and validation. Statistical analysis using the coefficient of determination (R

Indexed as

Compressive strength predictionGradient boostingMachine learningMetaheuristic optimizationSelf-compacting concreteSHAP analysis

Identifiers

PMID42162077

DOI10.1038/s41598-026-51974-1

PMCPMC13388961

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