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

Results in Engineering, Год журнала: 2024, Номер 23, С. 102637 - 102637

Опубликована: Июль 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.

Язык: 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, Год журнала: 2024, Номер 14(1)

Опубликована: Июнь 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.

Язык: Английский

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

Construction and Building Materials, Год журнала: 2024, Номер 442, С. 137509 - 137509

Опубликована: Июль 31, 2024

Язык: 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, Год журнала: 2024, Номер 66, С. 105937 - 105937

Опубликована: Авг. 19, 2024

Язык: Английский

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Optimizing high-strength concrete compressive strength with explainable machine learning

Multiscale and Multidisciplinary Modeling Experiments and Design, Год журнала: 2025, Номер 8(3)

Опубликована: Фев. 3, 2025

Язык: Английский

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

Case Studies in Construction Materials, Год журнала: 2025, Номер unknown, С. e04357 - e04357

Опубликована: Фев. 1, 2025

Язык: 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, Год журнала: 2025, Номер unknown, С. 112323 - 112323

Опубликована: Март 1, 2025

Язык: Английский

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

Case Studies in Construction Materials, Год журнала: 2024, Номер 21, С. e03820 - e03820

Опубликована: Окт. 6, 2024

Язык: 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, Год журнала: 2024, Номер 8(1)

Опубликована: Ноя. 7, 2024

Язык: Английский

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

Case Studies in Construction Materials, Год журнала: 2024, Номер 21, С. e03893 - e03893

Опубликована: Окт. 28, 2024

Язык: Английский

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