Analysis of the characteristics and environmental benefits of rice husk ash as a supplementary cementitious material through experimental and machine learning approaches

Innovative Infrastructure Solutions, Год журнала: 2024, Номер 9(4)

Опубликована: Март 28, 2024

Язык: Английский

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Enhancing compressive strength prediction in self-compacting concrete using machine learning and deep learning techniques with incorporation of rice husk ash and marble powder

Case Studies in Construction Materials, Год журнала: 2023, Номер 19, С. e02557 - e02557

Опубликована: Окт. 7, 2023

Focusing on sustainable development, the demand for alternative materials in concrete, especially Self-Compacting Concrete (SCC), has risen due to excessive cement usage and resulting CO2 emissions. As Compressive Strength (CS) is dominant among concrete properties, this research concentrates developing SCC by incorporating Rice Husk Ash (RHA) Marble Powder (MP) as filler replacements, respectively, while applying Machine Learning (ML) Deep (DL) techniques forecast CS of RHA/MP-based SCC. The further evaluates material characteristics, with a strong emphasis ML DL prediction. samples various mixed ratios were cast examined after 91 days collect data model application. In experimental technique, 133 gathered, was predicted using seven input factors (cement, RHA, MP, superplasticizer, coarse aggregate, fine water) an 80:20 ratio. Various algorithms, including linear regression, ridge lasso K-nearest neighbors (KNN), support vector machine (SVM), decision tree (DT), random forest (RF), boosting methods such gradient boost (GB), XG (XGB), adaptive (ADB) are employed, along technique backpropagation neural network (BPNN) different optimizer algorithms (Adam, SGD, RMSprop) predict validated evaluation parameters R-squared (R2), mean squared error (MSE), normalized root (NRMSE), absolute (MAE), percentage (MAPE). Comparatively, ensemble BPNN Adam RMSprop optimizers demonstrate high accuracy predicting outcomes, indicated their coefficient correlation R2 values low values.

Язык: Английский

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Effect of crumb rubber and steel slag on asphalt mixtures for a micropavement

Innovative Infrastructure Solutions, Год журнала: 2024, Номер 9(2)

Опубликована: Янв. 17, 2024

Язык: Английский

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Enhancing Concrete Performance with Crumb Rubber and Waste Materials: A Study on Mechanical and Durability Properties

Buildings, Год журнала: 2024, Номер 14(1), С. 161 - 161

Опубликована: Янв. 9, 2024

In addressing the dual challenges of sustainable waste management and environmental conservation in construction industry, particularly disposal tire crumb rubber (CR) demand for eco-friendly building materials, this study explores a novel solution. It examines incorporation mineral additions—namely silica fume (SF), marble slurry powder (MSP), fly ash (FA)—as partial substitutes natural fine aggregates cement concrete. Through comprehensive testing seventeen concrete samples, reveals that specific mix R10S5M10F15 contained 10% as replacement aggregates, 5% fume, 15% replacements cement, not only achieves compressive split tensile strength comparable to control mix, while 90 days flexural was improved by 4.48%; credited SF’s pozzolanic action filler effects MSP FA, but also inclusion CR, reducing due material variations, enhances ductility improves resistance sulfate acid attacks, despite increasing water absorption. The primary goal research is investigate feasibility effectiveness using materials foster more practices. objectives include detailed assessment mechanical properties durability incorporating these aiming determine optimal proportions their effective utilization. This study’s novelty lies its analysis synergistic combining SF, MSP, FA concrete, contributing field offering alternative approach traditional formulations highlighting delicate balance required optimized performance.

Язык: Английский

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Preliminary Life Cycle Assessment and Environmental Impact Evaluation of RC Bridge Deck: A Case Study in Norway

Lecture notes in civil engineering, Год журнала: 2024, Номер unknown, С. 233 - 244

Опубликована: Янв. 1, 2024

Язык: Английский

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Experimental and analytical investigation of concrete properties made with recycled coarse aggregate and bottom ash

Innovative Infrastructure Solutions, Год журнала: 2023, Номер 8(7)

Опубликована: Июнь 22, 2023

Язык: Английский

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Development of eco-friendly binder using waste cement powder-silica fume and micro steel fibres

Innovative Infrastructure Solutions, Год журнала: 2025, Номер 10(2)

Опубликована: Фев. 1, 2025

Abstract Interest has grown in recycled cement powder waste’s application building projects as a workable, long-term solution to environmental issues. This work presents experimental results investigating the behaviour of plain and fibre-reinforced waste paste with different volume fraction percentages micro steel fibres (1% 2%), where densified silica partially replaces 10% cement. For each mix, superplasticiser water-cement ratios were maintained constant. The study involved number studies, including flow table inspections, Field emission scanning electron microscopy (FE-SEM) tests, energy dispersive X-ray (SEM–EDX) testing, compressive flexural strength assessments, dry density measurements, ultrasonic tests. These evaluations aimed analyse specimens’ mechanical physical characteristics thoroughly. showed that substituting fume (SF) for certain amount could improve cement’s properties. Using 2% micro-steel significantly affected paste’s strengths. Nevertheless, an investigation revealed inclusion resulted reduction amplitude sound waves decrease stagnation flow. SEM–EDX tests satisfactory adherence between SF. clarifies why adding SF causes increase.

Язык: Английский

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Mechanistic insights into the impact of coal gangue on solid waste-based grouting fire extinguishing materials

Construction and Building Materials, Год журнала: 2025, Номер 470, С. 140547 - 140547

Опубликована: Март 4, 2025

Язык: Английский

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Industrial byproducts in concrete: A state-of-the-art review

Next Materials, Год журнала: 2025, Номер 8, С. 100593 - 100593

Опубликована: Март 17, 2025

Язык: Английский

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Hybrid machine learning models for predicting compressive strength of self-compacting concrete: an integration of ANFIS and Metaheuristic algorithm

Nondestructive Testing And Evaluation, Год журнала: 2025, Номер unknown, С. 1 - 33

Опубликована: Март 25, 2025

Self-compacting concrete (SCC) has become increasingly popular due to its superior workability, segregation resistance, and compressive strength. As the traditional methods for strength prediction are costly time-intensive, this study explores machine learning (ML) techniques as efficient alternatives SCC prediction. Three state-of-the-art hybrid Adaptive Neuro-Fuzzy Inference System (ANFIS) models, optimised using Firefly Algorithm (FA), Particle Swarm Optimization (PSO) Genetic (GA). For purpose, a robust dataset of 366 instances 7 input parameters is taken from literature. After data analysis pre-processing, hyperparameters models tuned best-fit model tested on unforeseen data. ANFIS-FF stands out best-performing (RTR2 = 0.945 RTS2 0.9395) in both training testing phases, closely followed by ANFIS-GA. All outperform ANFIS model, outlining significance hybridisation, however, ANFIS-PSO lags behind other two models. The highlights importance integrating with metaheuristic algorithms tackling complex engineering problems like design optimal mix design, minimising material waste ensuring cost-effectiveness. It serves benchmark future research comparing hybridisation starting point ANFIS.

Язык: Английский

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