Using marble waste as a partial aggregate replacement in the development of sustainable self-compacting concrete

Materials Today Proceedings, Год журнала: 2023, Номер unknown

Опубликована: Апрель 1, 2023

Increased population growth and industrial development have increased production in various industries, resulting waste production. Increasing consumption of non-renewable resources poses an inherent risk to future generations. In order reduce the these valuable resources, a variety methods can be used, one which is use produced by industries. This research investigated feasibility employing from marble mining byproducts make structural concrete. study replaced percentages with fine aggregates determine their effects on compressive strength, bending impact behavior, water absorption sustainable self-compacting concrete (SCC). Regarding recycled mechanical properties, it has been discovered that substituting sand increase flexural strength. It determined, via testing disk samples, amount steel fibers much greater effect resistance specimens than components. Fiber bridging shown significantly affect final strength containing number blows required for first surface fracture appear fiber-containing specimens. comparison sample served as reference. addition this, increasing replacement percentage causes them loads applied them. examining replacing absorption, was found no specific trend could indentified. Based findings, determined SCC aggregate performed satisfactorily.

Язык: Английский

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XGBoost Prediction Model Optimized with Bayesian for the Compressive Strength of Eco-Friendly Concrete Containing Ground Granulated Blast Furnace Slag and Recycled Coarse Aggregate

Applied Sciences, Год журнала: 2023, Номер 13(15), С. 8889 - 8889

Опубликована: Авг. 2, 2023

The construction industry has witnessed a substantial increase in the demand for eco-friendly and sustainable materials. Eco-friendly concrete containing Ground Granulated Blast Furnace Slag (GGBFS) Recycled Coarse Aggregate (RCA) is such material, which can contribute to reduction waste promote environmental sustainability. Compressive strength crucial parameter evaluating performance of concrete. However, predicting compressive GGBFS RCA be challenging. This study presents novel XGBoost (eXtreme Gradient Boosting) prediction model RCA, optimized using Bayesian optimization (BO). was trained on comprehensive dataset consisting several mix design parameters. assessed multiple evaluation metrics, including Root Mean Squared Error (RMSE), Absolute (MAE), coefficient determination (R2). These metrics were calculated both training testing datasets evaluate model’s accuracy generalization capabilities. results demonstrated that outperformed other state-of-the-art machine learning models, as Support Vector Regression (SVR), K-nearest neighbors algorithm (KNN), RCA. An analysis Partial Dependence Plots (PDP) carried out discern influence distinct input features prediction. PDP highlighted water-to-binder ratio, age concrete, percentage used, significant factors impacting

Язык: Английский

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Utilization of waste marine dredged clay in preparing controlled low strength materials with polycarboxylate superplasticizer and ground granulated blast furnace slag

Journal of Building Engineering, Год журнала: 2023, Номер 76, С. 107351 - 107351

Опубликована: Июль 13, 2023

Язык: Английский

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Prediction of self-consolidating concrete properties using XGBoost machine learning algorithm: Part 1–Workability

Construction and Building Materials, Год журнала: 2023, Номер 408, С. 133560 - 133560

Опубликована: Окт. 7, 2023

Язык: Английский

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Evaluating machine learning algorithms for predicting compressive strength of concrete with mineral admixture using long short-term memory (LSTM) Technique

Asian Journal of Civil Engineering, Год журнала: 2023, Номер 25(2), С. 1921 - 1933

Опубликована: Сен. 15, 2023

Язык: Английский

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Machine learning-based compressive strength estimation in nanomaterial-modified lightweight concrete

Open Engineering, Год журнала: 2024, Номер 14(1)

Опубликована: Янв. 1, 2024

Abstract The development of nanotechnology has led to the creation materials with unique properties, and in recent years, numerous attempts have been made include nanoparticles concrete an effort increase its performance create improved qualities. Nanomaterials are typically added lightweight (LWC) goal improving composite’s mechanical, microstructure, freshness, durability Compressive strength is most crucial mechanical characteristic for all varieties composites. For this reason, it essential accurate models estimating compressive (CS) LWC save time, energy, money. In addition, provides useful information planning construction schedule indicates when formwork should be removed. To predict CS mixtures or without nanomaterials, nine different were proposed study: gradient-boosted trees (GBT), random forest, tree ensemble, XGBoosted (XGB), Keras, simple regression, probabilistic neural networks, multilayer perceptron, linear relationship model. A total 2,568 samples gathered examined. significant factors influencing during modeling process taken into account as input variables, including amount cement, water-to-binder ratio, density, content aggregates, type nano, fine coarse aggregate content, water. suggested was assessed using a variety statistical measures, coefficient determination ( R 2 ), scatter index, mean absolute error, root-mean-squared error (RMSE). findings showed that, comparison other models, GBT model outperformed others predicting compression enhanced nanomaterials. produced best results, greatest value (0.9) lowest RMSE (5.286). Furthermore, sensitivity analysis that important factor prediction water content.

Язык: Английский

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Efficient compressive strength prediction of concrete incorporating industrial wastes using deep neural network

Asian Journal of Civil Engineering, Год журнала: 2023, Номер 24(8), С. 3473 - 3490

Опубликована: Май 30, 2023

Язык: Английский

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Machine learning-based compressive strength estimation in nano silica-modified concrete

Construction and Building Materials, Год журнала: 2023, Номер 408, С. 133684 - 133684

Опубликована: Окт. 11, 2023

Язык: Английский

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Brittleness index prediction using modified random forest based on particle swarm optimization of Upper Ordovician Wufeng to Lower Silurian Longmaxi shale gas reservoir in the Weiyuan Shale Gas Field, Sichuan Basin, China

Geoenergy Science and Engineering, Год журнала: 2023, Номер 233, С. 212518 - 212518

Опубликована: Ноя. 25, 2023

Язык: Английский

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Machine learning-based approach for optimizing mixture proportion of recycled plastic aggregate concrete considering compressive strength, dry density, and production cost

Journal of Building Engineering, Год журнала: 2023, Номер 83, С. 108393 - 108393

Опубликована: Дек. 30, 2023

Язык: Английский

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