Predicting the crack repair rate of self-healing concrete using soft-computing tools

Materials Today Communications, Journal Year: 2024, Volume and Issue: 38, P. 108043 - 108043

Published: Jan. 5, 2024

Language: Английский

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AI-driven Modeling for the Optimization of Concrete Strength for Low-Cost Business Production in the USA Construction Industry

Engineering Technology & Applied Science Research, Journal Year: 2025, Volume and Issue: 15(1), P. 20529 - 20537

Published: Feb. 2, 2025

The need to develop ecologically friendly sustainable building materials is made apparent by the worldwide construction industry's substantial contribution global greenhouse gas emissions. use of supplemental in concrete one potential solution lessen environmental footprint. Thus, purpose this work Machine Learning (ML) algorithms forecast and create an empirical formula for Compressive Strength (CS) with materials. Six distinct ML models—XGBoost, Linear Regression, Decision Tree, k-Nearest Neighbors, Bagging, Adaptive Boosting—were trained tested using a dataset that included 359 experimental data varying mix proportions. most significant factors used as input parameters are cement, aggregates, water, superplasticizer, silica fume, ambient curing, material. Several statistical measures, such Mean Absolute Error (MAE), coefficient determination (R2), Square (MSE), were evaluate models. XGBoost model outperformed other models R2 values 0.99 at training stage. To ascertain how affected outcome, feature importance analysis Shapely Additive exPlanations (SHAP) was conducted. It demonstrated curing age cement type significantly strength high SHAP values. By eliminating procedures, reducing demand labor resources, increasing time efficiency, offering insightful information enhancing manufacturing concrete, research advances low-cost production USA industry.

Language: Английский

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Evaluation of machine learning models for predicting TiO2 photocatalytic degradation of air contaminants

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

Published: June 13, 2024

Abstract The escalation of global urbanization and industrial expansion has resulted in an increase the emission harmful substances into atmosphere. Evaluating effectiveness titanium dioxide (TiO 2 ) photocatalytic degradation through traditional methods is resource-intensive complex due to detailed photocatalyst structures wide range contaminants. Therefore this study, recent advancements machine learning (ML) are used offer data-driven approach using thirteen techniques namely XG Boost (XGB), decision tree (DT), lasso Regression (LR2), support vector regression (SVR), adaBoost (AB), voting Regressor (VR), CatBoost (CB), K-Nearest Neighbors (KNN), gradient boost (GB), random Forest (RF), artificial neural network (ANN), ridge (RR), linear (LR1) address problem estimation TiO rate air models developed literature data different methodical tools evaluate ML models. XGB, DT LR2 have high R values 0.93, 0.926 training 0.936, 0.924 test phase. While ANN, RR LR lowest 0.70, 0.56 0.40 0.62, 0.63 0.31 phase respectively. low MAE RMSE 0.450 min -1 /cm , 0.494 0.49 for 0.263 0.285 0.29 stage. DT, 93% percent errors within 20% error XGB 92% 94% with remained highest performing most robust effective predictions. Feature importances reveal role input parameters prediction made by Dosage, humidity, UV light intensity remain important experimental factors. This study will impact positively providing efficient estimate contaminants .

Language: Английский

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Highly reactive carbonated recycled concrete fines prepared via mechanochemical carbonation: Influence on the early performance of cement composites

Cement and Concrete Composites, Journal Year: 2024, Volume and Issue: 152, P. 105636 - 105636

Published: June 21, 2024

Language: Английский

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Machine learning for predicting compressive strength of sustainable cement paste incorporating copper mine tailings as supplementary cementitious materials

Case Studies in Construction Materials, Journal Year: 2024, Volume and Issue: 21, P. e03373 - e03373

Published: June 7, 2024

Copper mining produces significant amounts of copper mine tailings (CMT), necessitating appropriate waste handling and disposal practices. By substituting a portion cement with CMT as supplementary cementitious materials (SCMs), we aim to address two environmental issues simultaneously: reducing in landfills decreasing embodied carbon by using less cement. The exploration recycling replacement requires evaluation its impact on material performance, such compressive strength. In this paper, machine learning that features data fusion large public our own small strength CMT-incorporated We developed critically evaluated three models: simple linear model, Gaussian process, random forest predict the pastes different mix designs (e.g., varying water-binder ratios) curing ages. Hyperparameters model were tuned Bayesian optimization. Following comprehensive models, find can accurately estimate paste across designs. Furthermore, results from SHapley Additive exPlanation (SHAP), Individual Conditional Expectation (ICE), Partial Dependence Plots (PDP) revealed cement, ground-granulated blast furnace slag, superplasticizers, ages positively influence This study contributes acceleration sustainable technology obtain best design desired

Language: Английский

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A review on properties and multi-objective performance predictions of concrete based on machine learning models

Materials Today Communications, Journal Year: 2025, Volume and Issue: unknown, P. 112017 - 112017

Published: Feb. 1, 2025

Language: Английский

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A systematic literature review of AI-based prediction methods for self-compacting, geopolymer, and other eco-friendly concrete types: Advancing sustainable concrete

Construction and Building Materials, Journal Year: 2024, Volume and Issue: 440, P. 137370 - 137370

Published: July 16, 2024

Language: Английский

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AI-Driven Prediction of Compressive Strength in Self-Compacting Concrete: Enhancing Sustainability through Ultrasonic Measurements

Sustainability, Journal Year: 2024, Volume and Issue: 16(15), P. 6644 - 6644

Published: Aug. 3, 2024

This study investigates the application of artificial intelligence (AI) to predict compressive strength self-compacting concrete (SCC) through ultrasonic measurements, thereby contributing sustainable construction practices. By leveraging advancements in computational techniques, specifically neural networks (ANNs), we developed highly accurate predictive models forecast SCC based on pulse velocity (UPV) measurements. Our findings demonstrate a clear correlation between higher UPV readings and improved quality, despite general trend decreased with increased air-entraining admixture (AEA) concentrations. The ANN show exceptional effectiveness predicting strength, coefficient (R2) 0.99 predicted actual values, providing robust tool for optimizing mix designs ensuring quality control. AI-driven approach enhances sustainability by improving material efficiency significantly reducing need traditional destructive testing methods, thus offering rapid, reliable, non-destructive alternative assessing properties.

Language: Английский

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Predictive modeling for durability characteristics of blended cement concrete utilizing machine learning algorithms

Case Studies in Construction Materials, Journal Year: 2025, Volume and Issue: unknown, P. e04209 - e04209

Published: Jan. 1, 2025

Language: Английский

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Self-solidification of As-containing bio-oxidation waste into cementitous materials: Performance enhancement via mechanochemical-activated recycled concrete fines

Journal of environmental chemical engineering, Journal Year: 2025, Volume and Issue: 13(2), P. 115531 - 115531

Published: Jan. 22, 2025

Language: Английский

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