Modelling the properties of aerated concrete on the basis of raw materials and ash-and-slag wastes using machine learning paradigm DOI Creative Commons
О. В. Руденко,

Darya Galkina,

Marzhan Anuarbekovna Sadenova

et al.

Frontiers in Materials, Journal Year: 2024, Volume and Issue: 11

Published: Oct. 22, 2024

The thermal power industry, as a major consumer of hard coal, significantly contributes to harmful emissions, affecting both air quality and soil health during the operation transportation ash slag waste. This study presents modeling aerated concrete using local raw materials ash-and-slag waste in seismic areas through machine learning techniques. A comprehensive literature review comparative analysis normative documentation underscore relevance feasibility employing non-autoclaved blocks such regions. Machine methods are particularly effective for disjointed datasets, with neural networks demonstrating superior performance complex relationships predicting strength density. results reveal that networks, especially those Bayesian Regularisation, consistently outperformed decision trees, achieving higher regression values (R = 0.9587 R density 0.91997) lower error metrics (MSE, RMSE, RIE, MAE). indicates their advanced capability capture intricate non-linear patterns. concludes artificial robust tool properties, crucial producing curing wall suitable earthquake-resistant construction. Future research should focus on optimizing balance between by enhancing properties utilizing reliable models.

Language: Английский

Investigation of machine learning models in predicting compressive strength for ultra-high-performance geopolymer concrete: A comparative study DOI

Mohamed Abdellatief,

Youssef M. Hassan,

Mohamed T. Elnabwy

et al.

Construction and Building Materials, Journal Year: 2024, Volume and Issue: 436, P. 136884 - 136884

Published: June 12, 2024

Language: Английский

Citations

36
Towards a Reliable Design of Geopolymer Concrete for Green Landscapes: A Comparative Study of Tree-Based and Regression-Based Models DOI Creative Commons
Ranran Wang, Jun Zhang, Yijun Lü

et al.

Buildings, Journal Year: 2024, Volume and Issue: 14(3), P. 615 - 615

Published: Feb. 26, 2024

The design of geopolymer concrete must meet more stringent requirements for the landscape, so understanding and designing with a higher compressive strength challenging. In performance prediction strength, machine learning models have advantage being accurate faster. However, only single model is usually used at present, there are few applications ensemble models, optimization processes lacking. Therefore, this paper proposes to use Firefly Algorithm (AF) as an tool perform hyperparameter tuning on Logistic Regression (LR), Multiple (MLR), decision tree (DT), Random Forest (RF) models. At same time, reliability efficiency four integrated were analyzed. was analyze influencing factors determine their ability. According experimental data, RF-AF had lowest RMSE value. value training set test 4.0364 8.7202, respectively. R 0.9774 0.8915, compared other three has stronger generalization ability accuracy. addition, molar concentration NaOH most important factors, its influence far greater than possible including content. it necessary pay attention molarity when concrete.

Language: Английский

Citations

13
Exploratory literature review and scientometric analysis of artificial intelligence applied to geopolymeric materials DOI
Aldo Ribeiro de Carvalho, Romário Parreira Pita, Thaís Mayra de Oliveira

et al.

Engineering Applications of Artificial Intelligence, Journal Year: 2025, Volume and Issue: 145, P. 110210 - 110210

Published: Feb. 20, 2025

Language: Английский

Citations

1
Carbon Emission Optimization of Ultra-High-Performance Concrete Using Machine Learning Methods DOI Open Access
Min Wang,

Mingfeng Du,

Yue Jia

et al.

Materials, Journal Year: 2024, Volume and Issue: 17(7), P. 1670 - 1670

Published: April 5, 2024

Due to its exceptional qualities, ultra-high-performance concrete (UHPC) has recently become one of the hottest research areas, although material's significant carbon emissions go against current development trend. In order lower UHPC, this study suggests a machine learning-based strategy for optimizing mix proportion UHPC. To accomplish this, an artificial neural network (ANN) is initially applied develop prediction model compressive strength and slump flow Then, genetic algorithm (GA) employed reduce UHPC while taking into account strength, flow, component content, proportion, absolute volume as constraint conditions. The outcome then supported by results experiments. comparison experimental results, findings show that ANN excellent accuracy with error less than 10%. are decreased 688 kg/m3 after GA optimization, effect optimization substantial. learning (ML) can provide theoretical support various aspects

Language: Английский

Citations

4
Modelling the properties of aerated concrete on the basis of raw materials and ash-and-slag wastes using machine learning paradigm DOI Creative Commons
О. В. Руденко,

Darya Galkina,

Marzhan Anuarbekovna Sadenova

et al.

Frontiers in Materials, Journal Year: 2024, Volume and Issue: 11

Published: Oct. 22, 2024

The thermal power industry, as a major consumer of hard coal, significantly contributes to harmful emissions, affecting both air quality and soil health during the operation transportation ash slag waste. This study presents modeling aerated concrete using local raw materials ash-and-slag waste in seismic areas through machine learning techniques. A comprehensive literature review comparative analysis normative documentation underscore relevance feasibility employing non-autoclaved blocks such regions. Machine methods are particularly effective for disjointed datasets, with neural networks demonstrating superior performance complex relationships predicting strength density. results reveal that networks, especially those Bayesian Regularisation, consistently outperformed decision trees, achieving higher regression values (R = 0.9587 R density 0.91997) lower error metrics (MSE, RMSE, RIE, MAE). indicates their advanced capability capture intricate non-linear patterns. concludes artificial robust tool properties, crucial producing curing wall suitable earthquake-resistant construction. Future research should focus on optimizing balance between by enhancing properties utilizing reliable models.

Language: Английский

Citations

2