Ensemble Extreme Gradient Boosting based models to predict the bearing capacity of micropile group

Applied Ocean Research, Journal Year: 2024, Volume and Issue: 151, P. 104149 - 104149

Published: Aug. 2, 2024

Language: Английский

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Estimating Axial Bearing Capacity of Driven Piles Using Tuned Random Forest Frameworks

Geotechnical and Geological Engineering, Journal Year: 2024, Volume and Issue: unknown

Published: Sept. 28, 2024

Language: Английский

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Improving the salt frost resistance of recycled aggregate concrete modified by air-entraining agents and nano-silica under sustained compressive loading

Case Studies in Construction Materials, Journal Year: 2024, Volume and Issue: 20, P. e03170 - e03170

Published: April 17, 2024

This paper systematically investigated the combined effects of sustained compressive loading and salt frost resistance for recycled aggregate concrete (RAC) incorporating air-entraining agents (AEA) nano silica (NS). The different RCA replacement ratios, freeze-thaw cycles, AEA dosage stress level on damage properties including apparent morphology, mass loss rate relative dynamic elastic modulus (RDEM) were discussed. microstructure evolution RAC samples before after cycles characterized analyzed through SEM observation, microhardness measurement X-CT tests. synergistic modification mechanism NS under action investigated, link between macroscopic microscopic air-entrained NS-modified was further explored. results showed that mixed with a certain amount dosages exhibited stronger compared to control group. effect similar changing trends RAC. When adding moderate applying loading, RDEM reduced so partly improved. Similarly, can reduce deterioration at ITZs compound use proved be as an efficient method strengthen RAC, improving coupled actions chloride attack. study provides feasible approach improve which is expected promote application extension in complex environmental regions.

Language: Английский

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Development of machine learning models for forecasting the strength of resilient modulus of subgrade soil: genetic and artificial neural network approaches

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: Aug. 6, 2024

Accurately predicting the Modulus of Resilience (MR) subgrade soils, which exhibit non-linear stress–strain behaviors, is crucial for effective soil assessment. Traditional laboratory techniques determining MR are often costly and time-consuming. This study explores efficacy Genetic Programming (GEP), Multi-Expression (MEP), Artificial Neural Networks (ANN) in forecasting using 2813 data records while considering six key parameters. Several Statistical assessments were utilized to evaluate model accuracy. The results indicate that GEP consistently outperforms MEP ANN models, demonstrating lowest error metrics highest correlation indices (R2). During training, achieved an R2 value 0.996, surpassing (R2 = 0.97) 0.95) models. Sensitivity SHAP (SHapley Additive exPlanations) analysis also performed gain insights into input parameter significance. revealed confining stress (21.6%) dry density (26.89%) most influential parameters MR. corroborated these findings, highlighting critical impact on predictions. underscores reliability as a robust tool precise prediction applications, providing valuable performance significance across various machine-learning (ML) approaches.

Language: Английский

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Hybrid and optimized neural network models to estimate the elastic modulus of recycled aggregate concrete

Journal of Asian Architecture and Building Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 20

Published: Feb. 11, 2025

Language: Английский

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Enhancing rutting depth prediction in asphalt pavements: A synergistic approach of extreme gradient boosting and snake optimization

Construction and Building Materials, Journal Year: 2024, Volume and Issue: 421, P. 135726 - 135726

Published: March 1, 2024

Language: Английский

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Compressive strength resistance coefficient of sustainable concrete in sulfate environments: Hybrid machine learning model and experimental verification

Materials Today Communications, Journal Year: 2024, Volume and Issue: 39, P. 108667 - 108667

Published: March 19, 2024

Language: Английский

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Utilizing ensemble machine learning and gray wolf optimization to predict the compressive strength of silica fume mixtures

Structural Concrete, Journal Year: 2024, Volume and Issue: 25(5), P. 4048 - 4074

Published: April 24, 2024

Abstract The concrete compressive strength is essential for the design and durability of infrastructure. Silica fume (SF), as a cementitious material, has been shown to improve mechanical properties concrete. This study aims predict containing SF by dual‐objective optimization determine best balance between accurate prediction model simplicity. A comprehensive dataset 2995 samples was collected from 36 peer‐reviewed studies ranging 5% 30% cement weight. Input variables included curing time, content, water‐to‐cement ratio, aggregates, superplasticizer levels, slump characteristics in modeling process. gray wolf (GWO) algorithm applied create that balances parsimony with an acceptable error threshold. determination coefficient ( R 2 ) 0.973 demonstrated CatBoost emerged superior predictive tool within boosting ensemble context. sensitivity analysis confirmed robustness model, identifying time predominant influence on SF‐containing To further enhance applicability this research, authors proposed web application facilitates users estimate using optimized following link: https://sf-concrete-cs-prediction-by-javid-toufigh.streamlit.app/ .

Language: Английский

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Sustainable Freshwater/Energy Supply through Geothermal-Centered Layout Tailored with Humidification-Dehumidification Desalination Unit; Optimized by Regression Machine Learning Techniques

Energy, Journal Year: 2024, Volume and Issue: 303, P. 131919 - 131919

Published: June 3, 2024

Language: Английский

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Optimization of centrifugal pump performance and excitation force based on machine learning and enhanced non-dominated sorting genetic algorithm III

Engineering Applications of Artificial Intelligence, Journal Year: 2025, Volume and Issue: 143, P. 110036 - 110036

Published: Jan. 13, 2025

Language: Английский

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