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

Applied Soft Computing, Journal Year: 2024, Volume and Issue: 159, P. 111661 - 111661

Published: April 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.

Language: Английский

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Predicting carbonation depth of concrete using a hybrid ensemble model

Journal of Building Engineering, Journal Year: 2023, Volume and Issue: 76, P. 107320 - 107320

Published: July 12, 2023

Language: Английский

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A Review of Concrete Carbonation Depth Evaluation Models

Coatings, Journal Year: 2024, Volume and Issue: 14(4), P. 386 - 386

Published: March 26, 2024

Carbonation is one of the critical issues affecting durability reinforced concrete. Evaluating depth concrete carbonation great significance for ensuring quality and safety construction projects. In recent years, various prediction algorithms have been developed evaluating depth. This article provides a detailed overview existing models According to data processing methods used in model, can be divided into mathematical curve machine learning models. The further following categories: artificial neural network decision tree support vector combined basic idea model directly establish relationship between age by using certain function curves. advantage that only small amount experimental needed fitting, which very convenient engineering applications. limitation it consider influence some factors on concrete, accuracy cannot guaranteed. predict many considered at same time. When there are sufficient data, trained give more accurate results than model. main defect needs lot as training samples, so not A future research direction may combine with evaluate accurately.

Language: Английский

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A review on properties and multi-objective performance predictions of concrete based on machine learning models

Materials Today Communications, Journal Year: 2025, Volume and Issue: unknown, P. 112017 - 112017

Published: Feb. 1, 2025

Language: Английский

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Prediction model for calculation of the limestone powder concrete carbonation depth

Journal of Building Engineering, Journal Year: 2024, Volume and Issue: 86, P. 108776 - 108776

Published: Feb. 8, 2024

Language: Английский

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A systematic literature review of AI-based prediction methods for self-compacting, geopolymer, and other eco-friendly concrete types: Advancing sustainable concrete

Construction and Building Materials, Journal Year: 2024, Volume and Issue: 440, P. 137370 - 137370

Published: July 16, 2024

Language: Английский

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Review of Prediction Models for Chloride Ion Concentration in Concrete Structures

Buildings, Journal Year: 2025, Volume and Issue: 15(1), P. 149 - 149

Published: Jan. 6, 2025

Chloride ion concentration significantly impacts the durability of reinforced concrete, particularly regarding corrosion. Accurately assessing how this varies with age structures is crucial for ensuring their safety and longevity. Recently, several predictive models have emerged to analyze chloride over time, classified into empirical machine learning based on data processing techniques. Empirical directly relate concrete through specific functions. Their primary advantage lies in low requirements, making them convenient engineering use. However, these often fail account multiple influencing factors, which can limit accuracy. Conversely, handle various factors simultaneously, providing a more detailed understanding evolves. When adequately trained sufficient experimental data, generally offer superior prediction accuracy compared mathematical models. The downside that they necessitate larger dataset training, complicate practical application. Future research could focus combining models, leveraging respective strengths achieve precise evaluation relation structural age.

Language: Английский

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Enhance the accuracy, robustness and interpretability of asphalt mixture dynamic modulus prediction

International Journal of Pavement Engineering, Journal Year: 2025, Volume and Issue: 26(1)

Published: Feb. 7, 2025

Language: Английский

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Predictive Model for Erosion Rate of Concrete Under Wind Gravel Flow Based on K-Fold Cross-Validation Combined with Support Vector Machine

Buildings, Journal Year: 2025, Volume and Issue: 15(4), P. 614 - 614

Published: Feb. 17, 2025

In the Gobi region, concrete structures frequently suffer erosion from wind gravel flow. This notably impairs their longevity. Therefore, creating a predictive model for flow-related damage is crucial to proactively address and manage this problem. Traditional theoretical models often fail predict rate of (CER) accurately. issue arises oversimplified assumptions failure account environmental variations complex nonlinear relationships between parameters. Consequently, single traditional inadequate predicting CER under flow conditions in region. To this, study utilized machine learning (ML) more precise prediction evaluation CER. The support vector (SVM) demonstrates superior performance, evidenced by its R2 value nearing one notable reduction RMSE 1.123 1.573 less than long short-term memory network (LSTM) BP neural (BPNN) models, respectively. Ensuring that training set comprises at least 80% total data volume SVM model’s accuracy. Moreover, time identified as most significant factor affecting An enhanced model, derived Bitter Oka framework integrating strength parameters, was formulated. It showed average relative errors 22% 31.6% however, recorded minimal error just −0.5%, markedly surpassing these improved terms Theoretical rely on simplifying assumptions, such linear homogeneous material properties. practice, factors like materials, flow, climate change are non-homogeneous. significantly limits applicability real-world environments. Ultimately, algorithm highly effective developing reliable safeguarding

Language: Английский

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Prediction of Ship CO2 Emissions and Fuel Consumption Using Voting-BRL Model

Sustainability, Journal Year: 2025, Volume and Issue: 17(4), P. 1726 - 1726

Published: Feb. 19, 2025

The accurate prediction of ship carbon dioxide (CO2) emissions and fuel consumption is critical for enhancing environmental sustainability in the maritime industry. This study introduces a novel ensemble learning approach, Voting-BRL model, which integrates Bayesian Ridge Regression Lasso to improve accuracy robustness. Utilizing four years real-world data from THETIS-MRV platform managed by European Maritime Safety Agency (EMSA), proposed model first employs Analysis Variance (ANOVA) feature selection, effectively reducing dimensionality mitigating noise interference. then combines strengths handling uncertainty correlations with Regression’s capability automatic selection through voting mechanism. Experimental results demonstrate that achieves an R2 0.9981 Root Mean Square Error (RMSE) 8.53, outperforming traditional machine models such as XGBRegressor, attains 0.97 RMSE 45.03. Additionally, ablation studies confirm approach significantly enhances predictive performance leveraging complementary individual models. not only provides superior but also exhibits enhanced generalization capabilities stability, making it reliable tool predicting CO2 consumption. advancement contributes more effective emission management operational efficiency shipping sector, supporting global efforts reduce greenhouse gas emissions.

Language: Английский

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