Innovative hybrid machine learning models for estimating the compressive strength of copper mine tailings concrete

Case Studies in Construction Materials, Год журнала: 2024, Номер 21, С. e03869 - e03869

Опубликована: Окт. 16, 2024

Язык: Английский

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Research on Innovative Models of New Media Art and Personalized Education

Опубликована: Янв. 12, 2025

This paper delves into the synergy between New Media Art and personalized education, assessing how fusion of digital art educational practices can revolutionize learning experiences. Art, characterized by its use technology interactivity, offers a dynamic platform for engaging students in more immersive environment. The study underscores significance education 21st century, where demand creativity, critical thinking, problem-solving skills is escalating. It examines cultural impact which extends beyond world to influence societal values norms, it challenges traditional definitions foster inclusive dialogue. Technological advancements such as augmented reality, virtual artificial intelligence are reshaping landscape providing new avenues artistic expression engagement. discusses potential solutions implementing settings, emphasizing need overcoming technological pedagogical barriers. In conclusion, integration models posited promising direction innovation, capable transforming experiences be engaging, relevant, effective, preparing world.

Язык: Английский

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Modeling the impact of SiO2, Al2O3, CaO, and Fe2O3 on the compressive strength of cement modified with nano-silica and silica fume

Multiscale and Multidisciplinary Modeling Experiments and Design, Год журнала: 2025, Номер 8(2)

Опубликована: Янв. 31, 2025

Язык: Английский

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Design of sustainable mortar incorporating construction and demolition waste through adaptive experiments accelerated by machine learning

Results in Engineering, Год журнала: 2025, Номер unknown, С. 104264 - 104264

Опубликована: Фев. 1, 2025

Язык: Английский

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Optimizing Strength Prediction for Cemented Paste Backfills with Various Fly Ash Substitution: Computational Approach with Machine Learning Algorithms

Minerals, Год журнала: 2025, Номер 15(3), С. 234 - 234

Опубликована: Фев. 26, 2025

In cemented paste backfill (CPB), fly ash (FA) can reduce cement costs. However, the chemical compositions of FA and tailings used in CPB vary significantly, affecting strength values CPBs, which be determined through laboratory tests play a crucial role design operations. Therefore, developing predictive model would advantageous terms time cost. The most critical aspect this study is that machine learning (ML) models demonstrate high accuracy performance prediction experimental studies, especially nonlinear complex data structures, even presence uncertainty geochemical geophysical parameters. Among ML algorithms, random forest (RF), artificial neural network (ANN), linear regression (LR), voting, extreme gradient boosting (XGBoost) algorithms were study. According to results obtained, XGBoost exhibited robust performance, evidenced by highest correlation coefficient (R) (0.922) lowest mean absolute error (0.666). also demonstrated its durability stability achieving relative (18.81%) root square (41.10%). it has been understood significant resource savings achieved important projects eliminating need for tests.

Язык: Английский

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A Comparative Exploration of Machine Learning Techniques for Compressive Strength Prediction in Copper Mine Tailing Concretes

Mining Metallurgy & Exploration, Год журнала: 2025, Номер unknown

Опубликована: Апрель 25, 2025

Язык: Английский

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Sustainability-oriented construction materials for traditional residential buildings: from material characteristics to environmental suitability

Case Studies in Construction Materials, Год журнала: 2024, Номер 21, С. e03820 - e03820

Опубликована: Окт. 6, 2024

Язык: Английский

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Flotation Tailings from Cu-Au Mining (Bor, Serbia) as a Potential Secondary Raw Material for Valuable Metals Recovery

Minerals, Год журнала: 2024, Номер 14(9), С. 905 - 905

Опубликована: Сен. 3, 2024

The increased exploitation of ores leads to the generation mining waste, which has a negative impact on environment and human health. For this reason, it is necessary take care in an adequate way by applying some possible treatments. In addition protecting appropriate treatment, there also possibility making profit valorizing useful elements from waste. order choose most perform characterization This paper contains detailed flotation tailings deposited at Old Flotation Tailings eastern Serbia, originating copper ore processing. Characterization includes physico-chemical analysis, polarizing microscope X-ray Diffraction analysis (XRD) Scanning Electron Microscopy with Energy Dispersive Spectroscopy (SEM-EDS) analysis. obtained results indicate that investigated can be used as secondary raw material for metal recovery, case primarily (whose content about 0.24%), gold (with 0.43 ppm) silver 1.7 ppm). Considering valuable quite low, suggested apply hydrometallurgical treatment their recovery.

Язык: Английский

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Machine Learning Driven Fluidity and Rheological Properties Prediction of Fresh Cement-Based Materials

Materials, Год журнала: 2024, Номер 17(22), С. 5400 - 5400

Опубликована: Ноя. 5, 2024

Controlling workability during the design stage of cement-based material mix ratios is a highly time-consuming and labor-intensive task. Applying artificial intelligence (AI) methods to predict optimize materials can significantly enhance efficiency design. In this study, experimental testing was conducted create dataset 233 samples, including fluidity, dynamic yield stress, plastic viscosity materials. The proportions cement, fly ash (FA), silica fume (SF), water, superplasticizer (SP), hydroxypropyl methylcellulose (HPMC), sand were selected as inputs. Machine learning (ML) employed establish predictive models for these three early indicators. To improve prediction capability, optimized hybrid models, such Particle Swarm Optimization (PSO)-based CatBoost XGBoost, adopted. Furthermore, influence individual input variables on each indicator examined using Shapley Additive Explanations (SHAP) Partial Dependence Plot (PDP) analyses. This study provides novel reference achieving rapid accurate control workability.

Язык: Английский

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Metaheuristic-based machine learning approaches of compressive strength forecasting of steel fiber reinforced concrete with SHapley Additive exPlanations

Multiscale and Multidisciplinary Modeling Experiments and Design, Год журнала: 2024, Номер 8(1)

Опубликована: Ноя. 15, 2024

Язык: Английский

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