Metaheuristic optimization algorithms-based prediction modeling for titanium dioxide-Assisted photocatalytic degradation of air contaminants

Results in Engineering, Journal Year: 2024, Volume and Issue: 23, P. 102637 - 102637

Published: July 29, 2024

Airborne contaminants pose significant environmental and health challenges. Titanium dioxide (TiO2) has emerged as a leading photocatalyst in the degradation of air compared to other photocatalysts due its inherent inertness, cost-effectiveness, photostability. To assess effectiveness, laboratory examinations are frequently employed measure photocatalytic rate TiO2. However, this approach involves time-consuming requirements, labor-intensive tasks, high costs. In literature, ensemble or standalone models commonly used for assessing performance TiO2 water contaminants. Nonetheless, application metaheuristic hybrid potential be more effective predictive accuracy efficiency. Accordingly, research utilized machine learning (ML) algorithms estimate photo-degradation constants organic pollutants using nanoparticles exposure ultraviolet light. Six metaheuristics optimization algorithms, namely, nuclear reaction (NRO), differential evolution algorithm (DEA), human felicity (HFA), lightning search (LSA), Harris hawks (HHA), tunicate swarm (TSA) were combined with random forest (RF) technique establish models. A database 200 data points was acquired from experimental studies model training testing. Furthermore, multiple statistical indicators 10-fold cross-validation examine established model's robustness. The TSA-RF demonstrated superior prediction among six suggested models, achieving an impressive correlation (R) 0.90 lower root mean square error (RMSE) 0.25. contrast, HFA-RF, HHA-RF, NRO-RF exhibited slightly R-value 0.88, RMSE scores 0.32. DEA-RF LSA-RF while effective, showed marginally 0.85, values 0.45 0.44, respectively. Moreover, SHapley Additive exPlanation (SHAP) results indicated that rates through photocatalysis most notably influenced by factors such reactor sizes, dosage, humidity, intensity.

Language: Английский

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Comparative analysis of various machine learning algorithms to predict strength properties of sustainable green concrete containing waste foundry sand

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: June 25, 2024

Abstract The use of waste foundry sand (WFS) in concrete production has gained attention as an eco-friendly approach to reduction and enhancing cementitious materials. However, testing the impact WFS through experiments is costly time-consuming. Therefore, this study employs machine learning (ML) models, including support vector regression (SVR), decision tree (DT), AdaBoost regressor (AR) ensemble model predict properties accurately. Moreover, SVR was employed conjunction with three robust optimization algorithms: firefly algorithm (FFA), particle swarm (PSO), grey wolf (GWO), construct hybrid models. Using 397 experimental data points for compressive strength (CS), 146 elastic modulus (E), 242 split tensile (STS), models were evaluated statistical metrics interpreted using SHapley Additive exPlanation (SHAP) technique. SVR-GWO demonstrated exceptional accuracy predicting (WFSC) characteristics. exhibited correlation coefficient values (R) 0.999 CS E, 0.998 STS. Age found be a significant factor influencing WFSC properties. also comparable prediction model. In addition, SHAP analysis revealed optimal content input variables mix. Overall, showed compared individual application these sophisticated soft computing techniques holds potential stimulate widespread adoption sustainable production, thereby fostering bolstering environmentally conscious construction practices.

Language: Английский

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Predicting the compressive strength of engineered geopolymer composites using automated machine learning

Construction and Building Materials, Journal Year: 2024, Volume and Issue: 442, P. 137509 - 137509

Published: July 31, 2024

Language: Английский

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Optimized prediction modeling of micropollutant removal efficiency in forward osmosis membrane systems using explainable machine learning algorithms

Journal of Water Process Engineering, Journal Year: 2024, Volume and Issue: 66, P. 105937 - 105937

Published: Aug. 19, 2024

Language: Английский

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Optimizing high-strength concrete compressive strength with explainable machine learning

Multiscale and Multidisciplinary Modeling Experiments and Design, Journal Year: 2025, Volume and Issue: 8(3)

Published: Feb. 3, 2025

Language: Английский

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Predicting the shield effectiveness of carbon fiber reinforced mortars utilizing metaheuristic algorithms

Case Studies in Construction Materials, Journal Year: 2025, Volume and Issue: unknown, P. e04357 - e04357

Published: Feb. 1, 2025

Language: Английский

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Prediction of self-healing ability of recurring cracks in Engineered Cementitious Composites with a machine learning based computational approach

Journal of Building Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 112323 - 112323

Published: March 1, 2025

Language: Английский

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Sustainability-oriented construction materials for traditional residential buildings: from material characteristics to environmental suitability

Case Studies in Construction Materials, Journal Year: 2024, Volume and Issue: 21, P. e03820 - e03820

Published: Oct. 6, 2024

Language: Английский

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Predictive modeling for compressive strength of blended cement concrete using hybrid machine learning models

Multiscale and Multidisciplinary Modeling Experiments and Design, Journal Year: 2024, Volume and Issue: 8(1)

Published: Nov. 7, 2024

Language: Английский

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Incorporating Crumb Rubber in Slag-Based Geopolymer: Experimental Work and Predictive Modelling

Case Studies in Construction Materials, Journal Year: 2024, Volume and Issue: 21, P. e03893 - e03893

Published: Oct. 28, 2024

Language: Английский

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