Optimization-driven XGBoost model with metaheuristic algorithms for assessing compressive strength of high-performance concrete

Compressive strength (CS) is a key property of concrete mix, but the determination of CS requires costly and time intensive experimental procedures. Leveraging machine learning (ML) techniques for CS prediction can enhance accuracy and reliability while reducing the extensive need for laboratory tes...

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Bibliographic Details
Published in:	Asian journal of civil engineering. Building and housing Vol. 26; no. 8; pp. 3401 - 3421
Main Authors:	Rai, Amit Kumar, Kumar, Shiv Shankar
Format:	Journal Article
Language:	English
Published:	Cham Springer International Publishing 01.08.2025 Springer Nature B.V
Subjects:	Accuracy Algorithms Antennae Artificial intelligence Bayesian analysis Building Materials Cement Civil Engineering Compressive strength Computational efficiency Concrete mixes Concrete mixing Concrete properties Data collection Datasets Efficiency Engineering Genetic algorithms Heuristic methods Laboratory tests Machine learning Optimization techniques Particle swarm optimization Performance enhancement Prediction models Research methodology Robustness (mathematics) Root-mean-square errors Sustainable Architecture/Green Buildings Variables Compressive strength Concrete Optuna XGBoost optimization Machine learning Hyperparameter tuning
ISSN:	1563-0854, 2522-011X
Online Access:	Get full text
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