Effect of graphene oxide on mechanical, deformation and drying shrinkage properties of concrete reinforced with fly ash as cementitious material by using RSM modelling

Scientific Reports, Год журнала: 2024, Номер 14(1)

Опубликована: Авг. 12, 2024

The industrial production of cement contributes significantly to greenhouse gas emissions, making it crucial address and reduce these emissions by using fly ash (FA) as a potential replacement. Besides, Graphene oxide (GO) was utilized nanoparticle in concrete augment its mechanical characteristics, deformation resistance, drying shrinkage behaviours. However, the researchers used Response Surface Methodology (RSM) evaluate compressive strength (CS), tensile (TS), flexural (FS), modulus elasticity (ME), (DS) that mixed with 5–15% FA at 5% increment, along 0.05%, 0.065%, 0.08% GO nanomaterials. samples were prepared mix proportions design targeted CS about 45 MPa 28 days. From investigational outcomes, 10% 0.05% exhibited greatest CS, TS, FS, ME values 62 MPa, 4.96 6.82 39.37 GPa, on days correspondingly. reduction DS found amounts increased. Moreover, development validation response prediction models conducted utilizing analysis variance (ANOVA) significance level 95%. coefficient determination (R2) for varied from 94 99.90%. Research study indicated including substitute cement, when combined GO, yields best results. Therefore, this approach is an excellent option building sector.

Язык: Английский

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Utilizing contemporary machine learning techniques for determining soilcrete properties

Earth Science Informatics, Год журнала: 2025, Номер 18(1)

Опубликована: Янв. 1, 2025

Abstract Soilcrete is an innovative construction material made by combining naturally occurring earth materials with cement. It can be effectively used in areas where other are not readily available due to financial or environmental reasons since soilcrete from natural clay. also help cut down the greenhouse gas emissions industry encouraging use of resources that locally available. Thus, it imperative reliably predict different properties accurate determination these crucial for widespread materials. However, laboratory subjected significant time and resource constraints. As a result, this research was undertaken provide empirical prediction models density, shrinkage, strain mixes using two machine learning algorithms: Gene Expression Programming (GEP) Extreme Gradient Boosting (XGB). The analysis revealed XGB-based predictions correlated more real-life values than GEP having training $${\text{R}}^{2}=0.999$$ R 2 = 0.999 both density shrinkage $${\text{R}}^{2}=0.944$$ 0.944 prediction. Moreover, several explanatory analyses including individual conditional expectation (ICE) shapely were done on XGB model which showed water-to-binder ratio, metakaolin content, modulus elasticity some most important variables forecasting properties. Furthermore, interactive graphical user interface (GUI) has been developed effective utilization civil engineering forecast

Язык: Английский

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Environmental and human health impact of recycle concrete powder: an emergy-based LCA approach

Frontiers in Environmental Science, Год журнала: 2025, Номер 12

Опубликована: Янв. 10, 2025

The construction sector extensively utilizes natural resources and energy, contributing significantly to greenhouse gas emissions (GHG). Concrete production, in particular, contributes notably environmental pollution. This study investigates the human health impact of concrete focusing on parameters such as Portland Cement, organic chemicals, diesel, medium voltage electricity, crushed gravel, heat, lubricating oil, sand tap water. It also evaluates replacing cement with recycled powder (RCP) using a life cycle assessment (LCA) approach through OpenLCA 2.1 software Ecoinvent database. Four mixes were assessed substitution ratios 0, 5%, 10%, 15%. Key indicators analyzed include climate change, toxicity, ionising radiation, ozone depletion, photochemical oxidant formation, ecosystem quality, resource depletion. Results show that is most environmentally harmful ingredient, while RCP reduces impacts Notably, analysis indicates higher content leads reduce impacts. Specifically, mix containing 15% showed substantial improvements, lowering depletion from 100% 90% formation 92%. These findings provide valuable insights for industry stakeholders policymakers, supporting advancement more sustainable practices. Future research should focus optimizing content, long-term performance, techno-economic feasibility enhance

Язык: Английский

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Macroscopic and Microscopic Evaluation of the Effect of Mellowing Time on the Mechanical Properties of Expansive Clay Stabilized with Class F Fly Ash–Based Geopolymer

Journal of Materials in Civil Engineering, Год журнала: 2025, Номер 37(4)

Опубликована: Фев. 4, 2025

Язык: Английский

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Enhancing performance and sustainability of GGBFS-based self-compacting geopolymer concrete blended with coal bottom ash and metakaolin by using RSM modelling

Scientific Reports, Год журнала: 2024, Номер 14(1)

Опубликована: Авг. 26, 2024

This research study is performed on the self-compacting geopolymer concrete (SCGC) combining coal bottom ash (CBA) and metakaolin (MK) as a substitution for GGBFS alone combined analysing fresh properties (slump flow, V-Funnel, T50 flow), mechanical characteristics (compressive, splitting tensile flexural strengths) durability tests (permeability sulfate attack test). Though, total 195 SCGC samples were made tested 28 days. It has been revealed that consumption of CBA MK combine in production decreased workability while are enhanced by utilizing up to 10%. In addition, compressive, strengths calculated 59.40 MPa, 5.68 6.12 MPa using 5CBA5MK after days correspondingly. Furthermore, permeability growing quantity weight jointly Besides, minimum change length specimen recorded 0.062 mm at 7.5MK7.5CBA maximum 0.11 10CBA10MK 180 embodied carbon reduce addition it getting higher when accumulation or with SCGC. response models prediction constructed confirmed ANOVA an accuracy rate 95%. The models' R

Язык: Английский

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Multi-targeted strength properties of recycled aggregate concrete through a machine learning approach

Engineering Computations, Год журнала: 2024, Номер 42(1), С. 388 - 430

Опубликована: Ноя. 22, 2024

Purpose Rapid industrialization and construction generate substantial concrete waste, leading to significant environmental issues. Nearly 10 billion metric tonnes of waste are produced globally per year. In addition, also accelerates the consumption natural resources, depletion these resources. Therefore, this study uses artificial intelligence (AI) examine utilization recycled aggregate (RCA) in concrete. Design/methodology/approach An extensive database 583 data points collected from literature for predictive modeling. Four machine learning algorithms, namely neural network (ANN), random forest (RF), ridge regression (RR) least adjacent shrinkage selection operator (LASSO) (LR), predicting simultaneously compressive tensile strength were evaluated. The dataset contains independent variables two dependent variables. Statistical parameters, including coefficient determination (R 2 ), mean square error (MSE), absolute (MAE) root (RMSE), employed assess accuracy algorithms. K-fold cross-validation was validate obtained results, SHapley Additive exPlanations (SHAP) analysis applied identify most sensitive parameters out input parameters. Findings results indicate that RF prediction model performance is better more satisfactory than other Furthermore, ANN algorithm ranks as second accurate algorithm. However, RR LR exhibit poor findings with low accuracy. successfully SHAP indicates cement content percentages effective parameter. special attention should be given enhance performance. Originality/value This uniquely applies AI optimize use RCA production. By evaluating four ANN, RF, on a comprehensive dataset, identities models strength. determine key result validation adds robustness. highlight superior provide actionable insights into enhancing RCA, contributing sustainable practice.

Язык: Английский

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Predicting residual strength of hybrid fibre-reinforced Self-compacting concrete (HFR-SCC) exposed to elevated temperatures using machine learning

Case Studies in Construction Materials, Год журнала: 2024, Номер 22, С. e04112 - e04112

Опубликована: Дек. 11, 2024

Язык: Английский

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Investigating the effect of locally available volcanic ash on mechanical and microstructure properties of concrete

REVIEWS ON ADVANCED MATERIALS SCIENCE, Год журнала: 2025, Номер 64(1)

Опубликована: Янв. 1, 2025

Abstract Environmental degradation is developing due to rising pollution from the depletion of raw materials and growing mandate for concrete goods. Investigators experts have focused on creating sustainable utilizing renewable elements. Volcanic ash (VA) a promising supplementary cementitious material among these minerals. Therefore, it crucial examine attributes voids in aggregate how they impact performance concrete. VA Gini Chilas (Gigilat Baltistan) was used prepare specimens. Mixing regimes with altering concentrations ranging 0 40% replacement cast. Water-to-cement ratio reserved persistent all mixes. Chemical compositions properties relation workability, density, compressive strength were carried out. In addition, thermo-gravimetric analysis, scanning electron microscope (SEM), X-ray diffraction analysis also examined. The results reveals that 10% gives an adamant response. This natural pozzolanic effect details creation additional dense gel (C–S–H), deviation cracks observed SEM. 10 exhibits thermally stable behavior at temperature less percentage mass loss. However, up can exhibit better than normal

Язык: Английский

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Influence of aluminum waste, hydrogen peroxide, and silica fume ratios on the mechanical and thermal characteristics of alkali-activated sustainable raw perlite based geopolymer paste

Sustainable Chemistry and Pharmacy, Год журнала: 2024, Номер 41, С. 101727 - 101727

Опубликована: Авг. 12, 2024

Язык: Английский

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Investigating the effects of aging and New Jig Coal Tailings (NJCT) on mixed-mode I/II fracture behavior of asphalt mixture modified with BituSul

Theoretical and Applied Fracture Mechanics, Год журнала: 2024, Номер 134, С. 104654 - 104654

Опубликована: Сен. 6, 2024

Язык: Английский

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