Prediction model for rice husk ash concrete using AI approach: Boosting and bagging algorithms

Structures, Journal Year: 2023, Volume and Issue: 50, P. 745 - 757

Published: Feb. 22, 2023

Language: Английский

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Forecasting the strength of graphene nanoparticles-reinforced cementitious composites using ensemble learning algorithms

Results in Engineering, Journal Year: 2024, Volume and Issue: 21, P. 101837 - 101837

Published: Feb. 6, 2024

Contemporary infrastructure requires structural elements with enhanced mechanical strength and durability. Integrating nanomaterials into concrete is a promising solution to improve However, the intricacies of such nanoscale cementitious composites are highly complex. Traditional regression models encounter limitations in capturing these intricate compositions provide accurate reliable estimations. This study focuses on developing robust prediction for compressive (CS) graphene nanoparticle-reinforced (GrNCC) through machine learning (ML) algorithms. Three ML models, bagging regressor (BR), decision tree (DT), AdaBoost (AR), were employed predict CS based comprehensive dataset 172 experimental values. Seven input parameters, including graphite nanoparticle (GrN) diameter, water-to-cement ratio (wc), GrN content (GC), ultrasonication (US), sand (SC), curing age (CA), thickness (GT), considered. The trained 70 % data, remaining 30 data was used testing models. Statistical metrics as mean absolute error (MAE), root square (RMSE) correlation coefficient (R) assess predictive accuracy DT AR demonstrated exceptional accuracy, yielding high coefficients 0.983 0.979 training, 0.873 0.822 testing, respectively. Shapley Additive exPlanation (SHAP) analysis highlighted influential role positively impacting CS, while an increased (w/c) negatively affected CS. showcases efficacy techniques accurately predicting nanoparticle-modified concrete, offering swift cost-effective approach assessing nanomaterial impact reducing reliance time-consuming expensive experiments.

Language: Английский

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Compressive strength prediction of concrete blended with carbon nanotubes using gene expression programming and random forest: hyper-tuning and optimization

Journal of Materials Research and Technology, Journal Year: 2023, Volume and Issue: 24, P. 7198 - 7218

Published: May 1, 2023

The strength of carbon nanotubes (CNTs) and cement composites is dependent on multiple variables. In addition, CNTs added to a cement-based matrix can boost its strength. However, the information related characteristics limited scarce. Their incorporation may substantially enhance mechanical durability properties cementitious mixtures. Despite challenges such as high cost workability problems. Therefore, proper consumption these materials must be used attain desired qualities. principal plan this investigation create predictive framework by utilizing machine-learning algorithms. Gene expression programming (GEP), random forest algorithm (RFA) employed estimate compressive concrete mixed with CNTs. GEP an individual approach, RFA ensemble method depict most influential model. outcomes two models are assessed employing external K-fold cross-validation, comparison done. A comprehensive database established comprising 282 data points for CS blended model then calibrated using six inputs, including curing time (CT), water-to-cement ratio (W/C), fine aggregate (FA), nanotube content (CNTs), (CC), coarse (CA). predicted results validated k-fold performance metrics, mean absolute error (MAE), root squared (RSE), correlation coefficient (R2), square (RMSE), relative (RRMSE). result shows that RF regression nth estimator robust accuracy showing minimal errors analyzed models. Likewise, depicts higher R2 = 0.96, validation demonstrate low errors. Moreover, excels in terms prediction through empirical equation. Shapley analysis (SHAP) performed check distribution parameters output. reveals time, cement, water binder have substantial influence CNT based composite.

Language: Английский

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A comprehensive GEP and MEP analysis of a cement-based concrete containing metakaolin

Structures, Journal Year: 2023, Volume and Issue: 53, P. 937 - 948

Published: May 7, 2023

Language: Английский

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Short-term load forecasting method based on secondary decomposition and improved hierarchical clustering

Results in Engineering, Journal Year: 2024, Volume and Issue: 22, P. 101993 - 101993

Published: March 15, 2024

In the context of large-scale grid connection new energy, short-term load forecasting is a vital and challenging task for power system to balance supply demand. To effectively improve accuracy, method proposed aiming mine characteristics data study application artificial intelligence algorithms. this paper, seasonal trend decomposition using loess (STL) firstly applied decompose into trend, residual components component with highest complexity further decomposed by complete ensemble empirical mode adaptive noise (CEEMDAN) approach. Secondly, in order reduce number components, improved hierarchical clustering technique cluster all intrinsic functions (IMFs) obtained CEEMDAN high-frequency low-frequency components. Then, different network models are trained get prediction results total value achieved stacking them. Finally, national demand dataset Great Britain 2021–2022 used conduct ablation comparative experiments. The mean absolute percentage error (MAPE) root square (RMSE) 2.064% 724.01 MW, respectively, which verified effectiveness advancement method.

Language: Английский

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Data-driven short term load forecasting with deep neural networks: Unlocking insights for sustainable energy management

Electric Power Systems Research, Journal Year: 2024, Volume and Issue: 232, P. 110376 - 110376

Published: April 10, 2024

Language: Английский

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Comparison of algorithms for heat load prediction of buildings

Energy, Journal Year: 2024, Volume and Issue: 297, P. 131318 - 131318

Published: April 15, 2024

Language: Английский

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A Comprehensive Survey on Load Forecasting Hybrid Models: Navigating the Futuristic Demand Response Patterns through Experts and Intelligent Systems

Results in Engineering, Journal Year: 2024, Volume and Issue: 23, P. 102773 - 102773

Published: Aug. 24, 2024

Language: Английский

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Short-term electrical load curve forecasting with MEWMA-CP monitoring techniques

Measurement, Journal Year: 2025, Volume and Issue: unknown, P. 117207 - 117207

Published: March 1, 2025

Language: Английский

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Seq2Seq-LSTM With Attention for Electricity Load Forecasting in Brazil

IEEE Access, Journal Year: 2024, Volume and Issue: 12, P. 30020 - 30029

Published: Jan. 1, 2024

Electricity load forecasting is important to planning the decision-making regarding use of energy resources, in which power system must be operated guarantee supply electricity future at lowest possible price. With rise ability based on deep learning, these approaches are promising this context. Considering attention mechanism capture long-range dependencies, it highly recommended for sequential data processing, where time series-related tasks stand out. a sequence-to-sequence (Seq2Seq) series Brazil, paper proposes long short-term memory (LSTM) with perform forecasting. The proposed Seq2Seq-LSTM outperforms other well-established models. Having mean absolute error equal 0.3027 method shown field applications. contributes by implementing an Seq2Seq data, therefore, more than one correlated signal can used prediction enhancing its capacity when avaliable.

Language: Английский

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