Microstructural behavior and explainable machine learning aided mechanical strength prediction and optimization of recycled glass-based solid waste concrete

Case Studies in Construction Materials, Journal Year: 2025, Volume and Issue: unknown, P. e04305 - e04305

Published: Jan. 1, 2025

Language: Английский

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Experimental Investigation on Fresh, Hardened and Durability Characteristics of Partially Replaced E-Waste Plastic Concrete: A Sustainable Concept with Machine Learning Approaches

Heliyon, Journal Year: 2025, Volume and Issue: 11(2), P. e41924 - e41924

Published: Jan. 1, 2025

The rapid global expansion of e-waste poses significant environmental and health risks, making it crucial to find sustainable uses mitigate its harmful effects. significance this research is look into the impact as a possible substitute for natural coarse aggregates (NCA) on fresh, hardened durability characteristics concrete, alongside machine learning (ML) predictive analysis. Four kinds concrete mixes were made with produced material NCA, substitution levels calculated 0 %, 10 15 % 20 (by mass NCA). Compressive splitting tensile tests evaluated mechanical properties whereas water permeability electrical resistivity assessed determine optimal proportion construction. compressive strengths reduced by 13.41%-25.50 11%-19.26 respectively, replacement ranging from at 28 days. specimens, 300 °C, exhibited reductions in strength 15.26%-30.87 10.52%-19.74 10%-20 respectively. With high coefficient correlation (R2) values, linear regression (LR) model predicted property outcomes more accurately than random forest (RF) model. test showed better results increased range 239.06 %-478.82 %. findings improved when quantity plastic was In terms all percentage results, best construction material.

Language: Английский

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Assessment of Hybrid Fiber Reinforced Graphene Nano-Engineered Concrete Composites: From Experimental Testing to Explainable Machine Learning Modeling

Journal of Materials Research and Technology, Journal Year: 2025, Volume and Issue: unknown

Published: March 1, 2025

Language: Английский

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Experimental assessment and hybrid machine learning-based feature importance analysis with the optimization of compressive strength of waste glass powder-modified concrete

Materials Today Communications, Journal Year: 2025, Volume and Issue: unknown, P. 112081 - 112081

Published: March 1, 2025

Language: Английский

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Microstructural assessment and supervised machine learning-aided modeling to explore the potential of quartz powder as an alternate binding material in concrete

Case Studies in Construction Materials, Journal Year: 2025, Volume and Issue: unknown, P. e04568 - e04568

Published: March 1, 2025

Language: Английский

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Experimental Assessment and Machine Learning Quantification of Structural Eco-Cellular Lightweight Concrete Incorporating Waste Marble Powder and Silica Fume

Journal of Building Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 112557 - 112557

Published: April 1, 2025

Language: Английский

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Enhancing malware detection with feature selection and scaling techniques using machine learning models

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: March 17, 2025

Abstract The increasing prevalence of malware presents a critical challenge to cybersecurity, emphasizing the need for robust detection methods. This study uses binary tabular classification dataset evaluate impact feature selection, scaling, and machine learning (ML) models on detection. methodology involves experimenting with three scaling techniques (no normalization, min-max scaling), selection methods Linear Discriminant Analysis (LDA), Principal Component (PCA)), twelve ML models, including traditional algorithms ensemble A publicly available 11,598 samples 139 features is utilized, model performance assessed using metrics such as accuracy, precision, recall, F1-score, AUC-ROC. Results reveal that Light Gradient Boosting Machine (LGBM) achieves highest accuracy 97.16% when PCA either or normalization are applied. Additionally, consistently outperform demonstrating their effectiveness in enhancing These findings offer valuable insights into optimizing preprocessing strategies developing reliable efficient systems.

Language: Английский

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Pozzolanic Activity of Biochar with High Carbon Content and its Influence on Comprehensive Strength-Emission Performance of Biochar-Cement Composite Paste

Published: Jan. 1, 2025

Language: Английский

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Interpretable machine learning model for performance characterization of lightweight concrete and composition design

Materials Today Communications, Journal Year: 2025, Volume and Issue: unknown, P. 112266 - 112266

Published: March 1, 2025

Language: Английский

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Triaxial constitutive modelling and failure criteria of rubberised concrete materials

Journal of Building Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 112460 - 112460

Published: March 1, 2025

Language: Английский

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