Prediction of compressive strength of high-volume fly ash self-compacting concrete with silica fume using machine learning techniques

Construction and Building Materials, Journal Year: 2024, Volume and Issue: 438, P. 136933 - 136933

Published: June 15, 2024

Language: Английский

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Development of hybrid gradient boosting models for predicting the compressive strength of high-volume fly ash self-compacting concrete with silica fume

Structures, Journal Year: 2024, Volume and Issue: 66, P. 106850 - 106850

Published: July 8, 2024

Language: Английский

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Deep Learning and Genetic Programming-Based Soft-Computing Prediction Models for Metakaolin Mortar

Transportation Infrastructure Geotechnology, Journal Year: 2025, Volume and Issue: 12(1)

Published: Jan. 1, 2025

Language: Английский

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Machine learning approach for predicting the compressive strength of biomedical waste ash in concrete: a sustainability approach

Discover Materials, Journal Year: 2025, Volume and Issue: 5(1)

Published: Feb. 21, 2025

Language: Английский

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Development of a prediction tool for the compressive strength of ternary blended ultra-high performance concrete using machine learning techniques

Journal of Structural Integrity and Maintenance, Journal Year: 2024, Volume and Issue: 9(3)

Published: July 2, 2024

Language: Английский

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Assessing the seismic sensitivity of bridge structures by developing fragility curves with ANN and LSTM integration

Asian Journal of Civil Engineering, Journal Year: 2024, Volume and Issue: 25(8), P. 5865 - 5888

Published: Aug. 29, 2024

Language: Английский

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Comparison of experimental and analytical studies in light gauge steel sections on CFST using SFRC in beams subjected to high temperatures

Asian Journal of Civil Engineering, Journal Year: 2024, Volume and Issue: unknown

Published: Nov. 6, 2024

Language: Английский

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Predicting the compressive strength of polymer-infused bricks: A machine learning approach with SHAP interpretability

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: March 8, 2025

Abstract The rapid increase in global waste production, particularly Polymer wastes, poses significant environmental challenges because of its nonbiodegradable nature and harmful effects on both vegetation aquatic life. To address this issue, innovative construction approaches have emerged, such as repurposing Polymers into building materials. This study explores the development eco-friendly bricks incorporating cement, fly ash, M sand, polypropylene (PP) fibers derived from Polymers. primary innovation lies leveraging advanced machine learning techniques, namely, artificial neural networks (ANN), support vector machines (SVM), Random Forest AdaBoost to predict compressive strength these Polymer-infused bricks. polymer bricks’ was recorded output parameter, with PP waste, age serving input parameters. Machine models often function black boxes, thereby providing limited interpretability; however, our approach addresses limitation by employing SHapley Additive exPlanations (SHAP) interpretation method. enables us explain influence different variables predicted outcomes, thus making more transparent explainable. performance each model evaluated rigorously using various metrics, including Taylor diagrams accuracy matrices. Among compared models, ANN RF demonstrated superior which is close agreement experimental results. achieves R 2 values 0.99674 0.99576 training testing respectively, whereas RMSE value 0.0151 (Training) 0.01915 (Testing). underscores reliability estimating strength. Age, ash were found be most important variable predicting determined through SHAP analysis. not only highlights potential enhance predictive for sustainable materials demonstrates a novel application improve interpretability context repurposing.

Language: Английский

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Machine learning prediction of the unconfined compressive strength of controlled low strength material using fly ash and pond ash

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

Published: Nov. 11, 2024

The sustainable use of industrial byproducts in civil engineering is a global priority, especially reducing the environmental impact waste materials. Among these, coal ash from thermal power plants poses significant challenge due to its high production volume and potential for pollution. This study explores controlled low-strength material (CLSM), flowable fill made ash, cement, aggregates, water, admixtures, as solution large-scale utilization. CLSM suitable both structural geotechnical applications, balancing management with resource conservation. research focuses on two key properties: flowability unconfined compressive strength (UCS) at 28 days. Traditional testing methods are resource-intensive, empirical models often fail accurately predict UCS complex nonlinear relationships among variables. To address these limitations, four machine learning models-minimax probability regression (MPMR), multivariate adaptive splines (MARS), group method data handling (GMDH), functional networks (FN) were employed UCS. MARS model performed best, achieving R

Language: Английский

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Enhancing urban sustainability: a study on lightweight and pervious concrete incorporating recycled plastic

Discover Sustainability, Journal Year: 2024, Volume and Issue: 5(1)

Published: Nov. 20, 2024

Abstract Increasing of plastic waste threatening ecosystems globally, this experimental work investigates recycled plastics as sustainable aggregate replacements in pervious concrete. Pervious concrete allows water passage but has installation/maintenance difficulty due to high weight. This research addresses the lack eco-friendly lightweight solutions by assessing physical and mechanical performance mixes with 100% traditional percentages. Density reduced 12% using a mix, achieving 1358 kg/m 3 compressive strength 3.92 MPa, adequate for non-structural applications. A 7.8% decrease absorption versus conventional signifies retained porosity permeability despite aggregates. Though early material limitations increase costs over 199.32%, show viability effective, substitutes natural aggregates With further availability affordability improvements, these recyclable can enable significantly greener construction practices. Findings provide key insights on balancing structural requirements, eco-friendliness infiltration capacity plastic-based broader adoption. The examines durability characteristics Light-Weight Concrete (LWPC) composed entirely aggregate. It also economic potential urban cost assessment reveals long-term environmental advantages, even though initial expenses are higher. Additionally, study considers an approach that combines plant growth promote greater sustainability.

Language: Английский

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