Investigation of machine learning models in predicting compressive strength for ultra-high-performance geopolymer concrete: A comparative study

Construction and Building Materials, Journal Year: 2024, Volume and Issue: 436, P. 136884 - 136884

Published: June 12, 2024

Language: Английский

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Recent advances in cementless ultra-high-performance concrete using alkali-activated materials and industrial byproducts: A review

Cement and Concrete Composites, Journal Year: 2024, Volume and Issue: 148, P. 105470 - 105470

Published: Feb. 3, 2024

Language: Английский

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Modelling the compressive strength of geopolymer recycled aggregate concrete using ensemble machine learning

Advances in Engineering Software, Journal Year: 2024, Volume and Issue: 191, P. 103611 - 103611

Published: March 1, 2024

In the quest to reduce environmental impact of construction sector, adoption sustainable and eco-friendly materials is imperative. Geopolymer recycled aggregate concrete (GRAC) emerges as a promising solution by substituting supplementary cementitious materials, including fly ash slag cement, for ordinary Portland cement utilizing aggregates from demolition waste, thus significantly lowering carbon emissions resource consumption. Despite its potential, widespread implementation GRAC has been hindered lack an effective mix design methodology. This study seeks bridge this gap through novel machine learning (ML)-based approach accurately model compressive strength (CS) GRAC, critical parameter ensuring structural integrity safety. By compiling comprehensive database existing literature enhancing it with synthetic data generated tabular generative adversarial network, research employs eight ensemble ML techniques, comprising three bagging five boosting methods, predict CS high precision. The models, notably extreme gradient boosting, light categorical regressors, demonstrated superior performance, achieving mean absolute percentage error less than 6 %. precision in prediction underscores viability optimizing formulations enhanced applications. identification testing age, natural fine content, ratio pivotal factors offers valuable insights into process, facilitating more informed decisions material selection proportioning. Moreover, development user-friendly graphical interface exemplifies practical application research, potentially accelerating mainstream practices. enabling use contributes global effort promote within industry.

Language: Английский

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Designing low-carbon fly ash based geopolymer with red mud and blast furnace slag wastes: Performance, microstructure and mechanism

Journal of Environmental Management, Journal Year: 2024, Volume and Issue: 354, P. 120362 - 120362

Published: Feb. 16, 2024

Language: Английский

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Active learning on stacked machine learning techniques for predicting compressive strength of alkali-activated ultra-high-performance concrete

Archives of Civil and Mechanical Engineering, Journal Year: 2024, Volume and Issue: 25(1)

Published: Nov. 12, 2024

Abstract Conventional ultra-high performance concrete (UHPC) has excellent development potential. However, a significant quantity of CO 2 is produced throughout the cement-making process, which in contrary to current worldwide trend lowering emissions and conserving energy, thus restricting further advancement UHPC. Considering climate change sustainability concerns, cementless, eco-friendly, alkali-activated UHPC (AA-UHPC) materials have recently received considerable attention. Following emergence advanced prediction techniques aimed at reducing experimental tools labor costs, this study provides comparative different methods based on machine learning (ML) algorithms propose an active learning-based ML model (AL-Stacked ML) for predicting compressive strength AA-UHPC. A data-rich framework containing 284 datasets 18 input parameters was collected. comprehensive evaluation significance features that may affect AA-UHPC performed. Results confirm AL-Stacked ML-3 with accuracy 98.9% can be used general specimens, been tested research. Active improve up 4.1% enhance Stacked models. In addition, graphical user interface (GUI) introduced validated by tests facilitate comparable prospective studies predictions.

Language: Английский

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Sustainable alkali-activated foam concrete with pumice aggregate: Effects of clinoptilolite zeolite and fly ash on strength, durability, and thermal performance

Construction and Building Materials, Journal Year: 2025, Volume and Issue: 464, P. 140160 - 140160

Published: Jan. 29, 2025

Language: Английский

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Improved mechanical and thermal properties of sustainable ultra-high performance geopolymer concrete with cellulose nanofibres

Journal of Building Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 112068 - 112068

Published: Feb. 1, 2025

Language: Английский

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Durability, Microstructure, and Optimization of High-Strength Geopolymer Concrete Incorporating Construction and Demolition Waste

Sustainability, Journal Year: 2023, Volume and Issue: 15(22), P. 15832 - 15832

Published: Nov. 10, 2023

The incorporation of construction and demolition (C&D) waste in concrete production has gained great importance toward sustainability, especially geopolymer concrete. In this study, ground granulated blast-furnace slag (GGBFS) fine aggregate normal were partially replaced by clay brick powder (CBP) (FCB) derived from C&D waste, respectively, aiming to produce high-strength (HSGC). Fly ash (FA) was also used as a partial replacement for GGBFS Twenty HSGC mixtures designed using the response surface methodology with three variables, including CBP (0–25%), FA FCB (0–50%). performance proposed assessed measuring several mechanical durability properties. addition, variety physicochemical methods, X-ray fluorescence spectroscopy, diffraction, scanning electron microscopy, examine mineralogical microstructural characteristics control developed mixtures. findings revealed that compressive, splitting tensile, flexural strengths made ranged 38.0 70.3 MPa, 4.1 8.2 5.2 10.0 respectively. results indicated is an essential parameter eliminate negative impacts addition on workability. optimal proportions 5% CBP, FA, 40% FCB, which determined generate optimized highest performance, according verified models optimization findings. analyses showed thick amorphous geopolymeric gel predominated nonporous structure HSGC, had good characteristics. Furthermore, anti-carbonation freezing resistance increased 17.7% 14.6%, while apparent porosity decreased 8.4%.

Language: Английский

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AI-driven design for the compressive strength of ultra-high performance geopolymer concrete (UHPGC): From explainable ensemble models to the graphical user interface

Materials Today Communications, Journal Year: 2024, Volume and Issue: 40, P. 109915 - 109915

Published: July 22, 2024

Language: Английский

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Enhancing reactivity of granite waste powder toward geopolymer preparation by mechanical activation

Construction and Building Materials, Journal Year: 2024, Volume and Issue: 414, P. 134981 - 134981

Published: Jan. 13, 2024

Language: Английский

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