Durability, mechanical, workability, and environmental assessment of self-consolidating concrete containing blast furnace slag and natural zeolite

Journal of Building Engineering, Год журнала: 2024, Номер 86, С. 108737 - 108737

Опубликована: Фев. 4, 2024

This study aims to investigate the possibility of using industrial and natural supplementary cementitious materials (SCMs) in self-consolidating concrete (SCC) with lower embodied carbon. Binary ternary blends ground granulated blast furnace slag (GGBFS) zeolite (NZ) at higher range replacements up 50 wt% Portland cement (PC) were used. Ten SCC mixtures various binder compositions evaluated for workability, compressive strength, durability properties, as well corresponding environmental impacts based on life cycle assessment (LCA). A new approach LCA's functional unit which specifies required cover effective height a beam section was used include structural design assessment. Utilizing GGBFS NZ presents positive synergistic effect resulting improved water impermeability, electrical resistivity, resistance chloride migration compared binary + PC or PC. LCA service 100 years showed mixture 10% NZ, 40% could reduce global warming potential by 38% control without SCM. concludes that cleaner enhanced properties reduced footprint can be achieved replacing wt%.

Язык: Английский

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Interpretable predictive modeling, sustainability assessment, and cost analysis of cement-based composite containing secondary raw materials

Construction and Building Materials, Год журнала: 2025, Номер 473, С. 140924 - 140924

Опубликована: Март 28, 2025

Язык: Английский

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Prediction of self-consolidating concrete properties using XGBoost machine learning algorithm: Rheological properties

Powder Technology, Год журнала: 2024, Номер 438, С. 119623 - 119623

Опубликована: Март 13, 2024

Rheological properties are critical for assessing self-consolidating concrete (SCC)'s performance and application. However, predicting these accurately, specifically plastic viscosity yield stress, faces challenges due to inconsistent data, small sample sizes, measurement inaccuracies, with the type of rheometer significantly impacting results. This study meticulously analyzes 348 mixtures from 19 peer-reviewed sources, focusing on experiments that detail types understand variability in rheological properties. Twelve variables, including cement content water-to-powder ratio, were identified as key SCC's rheology. Utilizing these, an XGBoost model demonstrated exceptional accuracy (R2 0.99), markedly better than traditional methods. advance not only aids SCC design but also showcases potential machine learning construction materials research, suggesting a new direction material property prediction innovation construction.

Язык: Английский

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Prediction Models for the Hybrid Effect of Nano Materials on Radiation Shielding Properties of Concrete Exposed to Elevated Temperatures

Case Studies in Construction Materials, Год журнала: 2024, Номер 21, С. e03750 - e03750

Опубликована: Сен. 12, 2024

Язык: Английский

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Machine learning based prediction of compressive and flexural strength of recycled plastic waste aggregate concrete

Structures, Год журнала: 2024, Номер 69, С. 107363 - 107363

Опубликована: Сен. 28, 2024

Язык: Английский

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Predicting compressive strength in cement mortar: The impact of fly ash composition through machine learning

Sustainable Chemistry and Pharmacy, Год журнала: 2025, Номер 43, С. 101915 - 101915

Опубликована: Янв. 19, 2025

Язык: Английский

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Compressive strength and flowability of high volume eco-binder high performance concrete: A physical-data dual drive

Structures, Год журнала: 2025, Номер 74, С. 108645 - 108645

Опубликована: Март 13, 2025

Язык: Английский

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Machine Learning as an Innovative Engineering Tool for Controlling Concrete Performance: A Comprehensive Review

Archives of Computational Methods in Engineering, Год журнала: 2025, Номер unknown

Опубликована: Апрель 10, 2025

Язык: Английский

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XGBoost assisted in identifying the structural damage behavior of fiber-reinforced concrete during the soundless demolition process through electrical resistance changes

Construction and Building Materials, Год журнала: 2025, Номер 478, С. 141376 - 141376

Опубликована: Апрель 23, 2025

Язык: Английский

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Self-consolidating concrete: Dataset on mixture design and key properties

Data in Brief, Год журнала: 2024, Номер 53, С. 110256 - 110256

Опубликована: Фев. 28, 2024

This manuscript delineates the assembly and structure of an extensive dataset encompassing more than 2500 self-consolidating concrete (SCC) mixtures, meticulously compiled from 176 scholarly sources. The has been subjected to a thorough curation process eliminate feature redundancy, rectify transcriptional inaccuracies, excise duplicative entries. refinement culminated in primed for advanced data-driven inquiries within SCC research domain, marking novel contribution field. serves as robust foundational resource, poised subsequent augmentations stringent applications data-centric studies. It facilitates detailed characterization properties, potentially through implementation machine learning algorithms, or comparative benchmark assess performance across diverse formulations. In conclusion, crucial resource scholars engaged studying similar substances. offers deep insights into ecological benefits substituting conventional Portland with alternatives. compilation not only advances understanding properties but also contributes broader conversation about sustainable construction practices.

Язык: Английский

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