A Survey of Ensemble Learning: Concepts, Algorithms, Applications, and Prospects

IEEE Access, Год журнала: 2022, Номер 10, С. 99129 - 99149

Опубликована: Янв. 1, 2022

Ensemble learning techniques have achieved state-of-the-art performance in diverse machine applications by combining the predictions from two or more base models. This paper presents a concise overview of ensemble learning, covering three main methods: bagging, boosting, and stacking, their early development to recent algorithms. The study focuses on widely used algorithms, including random forest, adaptive boosting (AdaBoost), gradient extreme (XGBoost), light (LightGBM), categorical (CatBoost). An attempt is made concisely cover mathematical algorithmic representations, which lacking existing literature would be beneficial researchers practitioners.

Язык: Английский

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Prediction of compressive strength of rice husk ash concrete based on stacking ensemble learning model

Journal of Cleaner Production, Год журнала: 2022, Номер 382, С. 135279 - 135279

Опубликована: Ноя. 22, 2022

Язык: Английский

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Optimization design for alkali-activated slag-fly ash geopolymer concrete based on artificial intelligence considering compressive strength, cost, and carbon emission

Journal of Building Engineering, Год журнала: 2023, Номер 75, С. 106929 - 106929

Опубликована: Май 25, 2023

Язык: Английский

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Interpretable machine learning for predicting the strength of 3D printed fiber-reinforced concrete (3DP-FRC)

Journal of Building Engineering, Год журнала: 2023, Номер 72, С. 106648 - 106648

Опубликована: Апрель 25, 2023

Язык: Английский

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Machine learning and interactive GUI for concrete compressive strength prediction

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

Опубликована: Июль 19, 2024

Concrete compressive strength (CS) is a crucial performance parameter in concrete structure design. Reliable prediction reduces costs and time design prevents material waste from extensive mixture trials. Machine learning techniques solve structural engineering challenges such as CS prediction. This study used Learning (ML) models to enhance the of CS, analyzing 1030 experimental data ranging 2.33 82.60 MPa previous research databases. The ML included both non-ensemble ensemble types. were regression-based, evolutionary, neural network, fuzzy-inference-system. Meanwhile, consisted adaptive boosting, random forest, gradient boosting. There eight input parameters: cement, blast-furnace-slag, aggregates (coarse fine), fly ash, water, superplasticizer, curing days, with output. Comprehensive evaluations include visual quantitative methods k-fold cross-validation assess study's reliability accuracy. A sensitivity analysis using Shapley-Additive-exPlanations (SHAP) was conducted understand better how each variable affects CS. findings showed that Categorical-Gradient-Boosting (CatBoost) model most accurate during testing stage. It had highest determination-coefficient (R

Язык: Английский

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Metaheuristic optimization based- ensemble learners for the carbonation assessment of recycled aggregate concrete

Applied Soft Computing, Год журнала: 2024, Номер 159, С. 111661 - 111661

Опубликована: Апрель 23, 2024

This study addresses the enhanced prevalence of carbonation, a process accelerating steel reinforcement corrosion, in recycled aggregate concrete (RAC) compared to natural concrete. Traditional carbonation depth assessment methods RAC are noted for being labor-intensive, costly, and requiring specialized expertise. There's deficiency application machine learning techniques accurately predicting RAC, gap this aims fill. Utilizing extreme gradient boosting (XGBoost) technique, recognized its efficacy ensemble learning, innovates modeling RAC. It emphasizes criticality hyperparameter optimization XGBoost algorithm maximizing model accuracy. To achieve this, three novel metaheuristic algorithms, including reptile search (RSA), Aquila optimizer (AO), arithmetic (AOA), were introduced as global optimizers tunning hyperparameters. The was underpinned by comprehensive database compiled from extensive literature, facilitating development an accurate model. Through rigorous evaluations, sensitivity analyses, Wilcoxon signed-rank test, runtime comparisons, synthesized models demonstrated exceptional accuracy, with coefficients determination exceeding 0.95. XGBoost-AO algorithm, particular, showcased superior performance, XGBoost-RSA providing efficient predictions considering runtime. SHapley Additive exPlanations (SHAP) interpretation highlighted environmental conditions significant influencers. A user-friendly graphical user interface developed, enhancing practical utility findings progression over time. research significantly advances predictive accuracy contributing sustainable management infrastructures emphasizing integration advanced structural engineering advancements.

Язык: Английский

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Sustainable foam glass property prediction using machine learning: A comprehensive comparison of predictive methods and techniques

Results in Engineering, Год журнала: 2025, Номер unknown, С. 104089 - 104089

Опубликована: Янв. 1, 2025

Язык: Английский

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Leveraging machine learning to evaluate the effect of raw materials on the compressive strength of ultra-high-performance concrete

Results in Engineering, Год журнала: 2025, Номер 25, С. 104542 - 104542

Опубликована: Март 1, 2025

Язык: Английский

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An Ultrasonic-AI Hybrid Approach for Predicting Void Defects in Concrete-Filled Steel Tubes via Enhanced XGBoost with Bayesian Optimization

Case Studies in Construction Materials, Год журнала: 2025, Номер unknown, С. e04359 - e04359

Опубликована: Фев. 1, 2025

Язык: Английский

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Efficient creep prediction of recycled aggregate concrete via machine learning algorithms

Construction and Building Materials, Год журнала: 2022, Номер 360, С. 129497 - 129497

Опубликована: Ноя. 3, 2022

Язык: Английский

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