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

---

Indirect estimation of resilient modulus (Mr) of subgrade soil: Gene expression programming vs multi expression programming

Structures, Journal Year: 2024, Volume and Issue: 66, P. 106837 - 106837

Published: July 1, 2024

Language: Английский

---

Enhancing unconfined compressive strength prediction in nano-silica stabilized soil: a comparative analysis of ensemble and deep learning models

Modeling Earth Systems and Environment, Journal Year: 2024, Volume and Issue: 10(4), P. 5079 - 5102

Published: May 31, 2024

Language: Английский

---

Soft computing models for prediction of bentonite plastic concrete strength

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

Published: Aug. 5, 2024

Bentonite plastic concrete (BPC) is extensively used in the construction of water-tight structures like cut-off walls dams, etc., because it offers high plasticity, improved workability, and homogeneity. Also, bentonite added to mixes for adsorption toxic metals. The modified design BPC, as compared normal concrete, requires a reliable tool predict its strength. Thus, this study presents novel attempt at application two innovative evolutionary techniques known multi-expression programming (MEP) gene expression (GEP) boosting-based algorithm AdaBoost 28-day compressive strength ( ) BPC based on mixture composition. MEP GEP algorithms expressed their outputs form an empirical equation, while failed do so. were trained using dataset 246 points gathered from published literature having six important input factors predicting. developed models subject error evaluation, results revealed that all satisfied suggested criteria had correlation coefficient (R) greater than 0.9 both training testing phases. However, surpassed terms accuracy demonstrated lower RMSE 1.66 2.02 2.38 GEP. Similarly, objective function value was 0.10 0.176 0.16 MEP, which indicated overall good performance techniques. Shapley additive analysis done model gain further insights into prediction process, cement, coarse aggregate, fine aggregate are most predicting BPC. Moreover, interactive graphical user interface (GUI) has been be practically utilized civil engineering industry

Language: Английский

---

Development of machine learning models for forecasting the strength of resilient modulus of subgrade soil: genetic and artificial neural network approaches

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

Published: Aug. 6, 2024

Accurately predicting the Modulus of Resilience (MR) subgrade soils, which exhibit non-linear stress–strain behaviors, is crucial for effective soil assessment. Traditional laboratory techniques determining MR are often costly and time-consuming. This study explores efficacy Genetic Programming (GEP), Multi-Expression (MEP), Artificial Neural Networks (ANN) in forecasting using 2813 data records while considering six key parameters. Several Statistical assessments were utilized to evaluate model accuracy. The results indicate that GEP consistently outperforms MEP ANN models, demonstrating lowest error metrics highest correlation indices (R2). During training, achieved an R2 value 0.996, surpassing (R2 = 0.97) 0.95) models. Sensitivity SHAP (SHapley Additive exPlanations) analysis also performed gain insights into input parameter significance. revealed confining stress (21.6%) dry density (26.89%) most influential parameters MR. corroborated these findings, highlighting critical impact on predictions. underscores reliability as a robust tool precise prediction applications, providing valuable performance significance across various machine-learning (ML) approaches.

Language: Английский

---

Evaluation of the mechanical behavior of concrete with the addition of dry corn fiber

Innovative Infrastructure Solutions, Journal Year: 2025, Volume and Issue: 10(1)

Published: Jan. 1, 2025

Language: Английский

---

Properties of concrete incorporating plastic wastes and its applications: A comprehensive review

Journal of Building Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 111843 - 111843

Published: Jan. 1, 2025

Language: Английский

---

Interpretable predictive modeling, sustainability assessment, and cost analysis of cement-based composite containing secondary raw materials

Construction and Building Materials, Journal Year: 2025, Volume and Issue: 473, P. 140924 - 140924

Published: March 28, 2025

Language: Английский

---

Predicting natural vibration period of concrete frame structures having masonry infill using machine learning techniques

Journal of Building Engineering, Journal Year: 2024, Volume and Issue: 96, P. 110417 - 110417

Published: Aug. 10, 2024

Language: Английский

---

Toward sustainability: Integrating experimental study and data-driven modeling for eco-friendly paver blocks containing plastic waste

REVIEWS ON ADVANCED MATERIALS SCIENCE, Journal Year: 2024, Volume and Issue: 63(1)

Published: Jan. 1, 2024

Abstract Plastic waste (PW) poses a significant threat as hazardous material, while the production of cement raises environmental concerns. It is imperative to urgently address and reduce both PW usage in concrete products. Recently, several experimental studies have been performed incorporate into paver blocks (PBs) substitute for cement. However, testing not enough optimize use plastic pavers due resource time limitations. This study proposes an innovative approach, integrating with machine learning ratios PBs efficiently. Initially, investigations are examine compressive strength (CS) sand (PSPBs). Varied mix proportions different sizes employed. Moreover, enhance CS meet minimum requirements ASTM C902-15 light traffic, basalt fibers, sustainable industrial also utilized manufacturing process environmentally friendly PSPB. The highest 17.26 MPa achieved by using finest-size particles plastic-to-sand ratio 30:70. Additionally, inclusion 0.5% fiber, measuring 4 mm length, yields further enhancement outcome significantly improving 25.4% (21.65 MPa). Following that, extensive record established, multi-expression programming (MEP) used forecast model’s projected results confirmed various statistical procedures external validation methods. Furthermore, comprehensive parametric sensitivity conducted assess effectiveness MEP-based proposed models. analysis demonstrates that size fiber content primary factors contributing more than 50% accuracy demonstrating comparable pattern results. indicate formulation exhibits high precision R 2 0.89 possesses strong ability predict. provides graphical user interface increase significance ML practical application handling management. main aim this research reuse promote sustainability economic benefits, particularly producing green environments integration investigations.

Language: Английский

---