Compressive strength prediction of sustainable concrete incorporating rice husk ash (RHA) using hybrid machine learning algorithms and parametric analyses

Case Studies in Construction Materials, Journal Year: 2024, Volume and Issue: 20, P. e03030 - e03030

Published: March 5, 2024

The construction industry is making efforts to reduce the environmental impact of cement production in concrete by incorporating alternative and supplementary cementitious materials, as well lowering carbon emissions. One such material that has gained popularity this context rice husk ash (RHA) due its pozzolanic reactions. This study aims forecast compressive strength (CS) RHA-based (RBC) examining effects several factors cement, RHA content, curing age, water usage, aggregate amount, superplasticizer content. To accomplish this, collected analyzed data from literature, resulting a dataset 1404 observations. Several machine learning (ML) models, light gradient boosting (LGB), extreme (XGB), random forest (RF), hybrid (HML) approaches like XGB-LGB XGB-RF were employed thoroughly analyze these parameters assess their on strength. was split into training testing groups, statistical analyses performed determine relationships between input CS. Moreover, performance all models evaluated using various evaluation criteria, including mean absolute percentage error (MAPE), coefficient efficiency (CE), root square (RMSE), determination (R2). model found have higher precision (R2 = 0.95, RMSE 5.255 MPa) compared other models. SHAP (SHapley Additive exPlanations) analysis revealed RHA, had positive effect Overall, study's findings suggest with identified can be used accurately predict CS RBC. application technologies sector facilitate rapid low-cost identification qualities parameters.

Language: Английский

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Use of supplementary cementitious materials in seawater–sea sand concrete: State-of-the-art review

Construction and Building Materials, Journal Year: 2024, Volume and Issue: 425, P. 136009 - 136009

Published: March 30, 2024

Language: Английский

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Predicting Penetration Depth in Ultra-High-Performance Concrete Targets under Ballistic Impact: An Interpretable Machine Learning Approach Augmented by Deep Generative Adversarial Network

Results in Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 103909 - 103909

Published: Jan. 1, 2025

Language: Английский

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Towards sustainable construction: Machine learning based predictive models for strength and durability characteristics of blended cement concrete

Materials Today Communications, Journal Year: 2023, Volume and Issue: 37, P. 107428 - 107428

Published: Oct. 26, 2023

Language: Английский

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Forecasting the strength characteristics of concrete incorporating waste foundry sand using advance machine algorithms including deep learning

Case Studies in Construction Materials, Journal Year: 2023, Volume and Issue: 19, P. e02459 - e02459

Published: Sept. 9, 2023

The incorporation of waste foundry sand (WFS) into concrete has been recognized as a sustainable approach to improve the strength properties (WFSC). However, machine learning (ML) techniques are still necessary forecast characteristics WFSC and evaluate dominant input features for suitable mix design. For this purpose, present work selected five ML-based based on gene expression programming (GEP), deep neural network (DNN), optimizable Gaussian process regressor (OGPR) predict mechanical WFSC. To build up predictive models, database containing 397 values compressive (CS) 169 flexural (FS) is collected from published literature. models' performance was evaluated via various statistical metrics additionally, external validation criteria were employed validate developed models. Furthermore, Shapley additive explanation (SHAP) carried out interpret model's prediction. DNN2 model exhibited superior performance, with R-values 0.996 (training), 0.999 (testing), 0.997 (validation) estimation. In contrast, GEP2 showed poor accuracy in estimating CS compared other 0.851, 0.901, 0.844 training, testing, sets, respectively. Similarly, estimation, provided indicating its robust performance. SHAP analysis revealed that age, water-cement ratio, coarse aggregate-to-cement ratio have prime influence determining strength, comparison models accurately estimated output high lower error might be utilized practical fields reduce labor cost by optimizing combinations. Finally, future studies, it recommended utilize ensemble hybrid algorithms, well post-hoc explanatory techniques, accurately.

Language: Английский

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Estimation of compressive strength of waste concrete utilizing fly ash/slag in concrete with interpretable approaches: optimization and graphical user interface (GUI)

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

Published: Feb. 26, 2024

Abstract Geo-polymer concrete has a significant influence on the environmental condition and thus its use in civil industry leads to decrease carbon dioxide (CO 2 ) emission. However, problems lie with mixed design casting field. This study utilizes supervised artificial-based machine learning algorithms (MLAs) anticipate mechanical characteristic of fly ash/slag-based geopolymer (FASBGPC) by utilizing AdaBoost Bagging MLPNN make an ensemble model 156 data points. The consist GGBS (kg/m 3 ), Alkaline activator Fly ash SP dosage NaOH Molarity, Aggregate Temperature (°C) compressive strength as output parameter. Python programming is utilized Anaconda Navigator using Spyder version 5.0 predict response. Statistical measures validation are done splitting dataset into 80/20 percent K-Fold CV employed check accurateness MAE, RMSE, R . analysis relies errors, tests against external indicators help determine how well models function terms robustness. most important factor measurements examined permutation characteristics. result reveals that ANN outclassed giving maximum enhancement = 0.914 shows least error statistical validations. Shapley GGBS, temperature influential parameter content making FASBGPC. Thus, methods suitable for constructing prediction because their strong reliable performance. Furthermore, graphical user interface (GUI) generated through process training forecasts desired outcome values when corresponding inputs provided. It streamlines provides useful tool applying model's abilities field engineering.

Language: Английский

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Machine and Deep Learning Methods for Concrete Strength Prediction: A Bibliometric and Content Analysis Review of Research Trends and Future Directions

Applied Soft Computing, Journal Year: 2024, Volume and Issue: 164, P. 111956 - 111956

Published: July 8, 2024

Language: Английский

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Forecasting the strength of nanocomposite concrete containing carbon nanotubes by interpretable machine learning approaches with graphical user interface

Structures, Journal Year: 2024, Volume and Issue: 59, P. 105821 - 105821

Published: Jan. 1, 2024

Language: Английский

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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

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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

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