Stacked-based machine learning to predict the uniaxial compressive strength of concrete materials

Computers & Structures, Journal Year: 2025, Volume and Issue: 308, P. 107644 - 107644

Published: Jan. 6, 2025

Language: Английский

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Modeling hydraulic jump roller length on rough beds: a comparative study of ANN and GEP models

Deleted Journal, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 8, 2025

Abstract Hydraulic jumps (HJs) play a vital role in energy dissipation hydraulic systems and are critical for the effective design of water management structures. This study employed Artificial Neural Network (ANN) Gene Expression Programming (GEP) models to predict roller length ratio ( L * ) HJs over rough beds. The analysis utilized dataset 367 experimental observations with 70–30 training testing split. Comprehensive data descriptions were conducted, ensuring detailed understanding inputs, including upstream Froude number F ), initial sequent HJ depth H = h 2 / 1 channel bed roughness K k s ). Descriptive statistics revealed moderate variability mostly symmetric distributions, making suitable predictive modeling. A sensitivity was conducted confirmed that had highest influence on , followed by . ANN model achieved R 0.937 0.935, RMSEs 1.737 1.719, respectively. GEP demonstrated 0.941 0.930, 1.682 1.780. Both displayed reliable capabilities, minimal bias consistent performance unseen data, supported comprehensive error distribution uncertainty evaluations. Moreover, high level agreement prior research results, highlighting importance thorough characterization validation. Thus, have been recognized as techniques predicting jump length. Graphical

Language: Английский

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Stacked-based hybrid gradient boosting models for estimating seepage from lined canals

Journal of Water Process Engineering, Journal Year: 2025, Volume and Issue: 70, P. 106913 - 106913

Published: Jan. 9, 2025

Language: Английский

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Predicting seawater intrusion wedge length in coastal aquifers using hybrid gradient boosting techniques

Earth Science Informatics, Journal Year: 2025, Volume and Issue: 18(2)

Published: Feb. 1, 2025

Language: Английский

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Enhanced energy dissipation prediction in modified semi-cylindrical weirs using machine learning techniques

Modeling Earth Systems and Environment, Journal Year: 2025, Volume and Issue: 11(2)

Published: Feb. 13, 2025

Language: Английский

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Machine learning for the prediction of the axial load‐carrying capacity of FRP reinforced hollow concrete column

Structural Concrete, Journal Year: 2025, Volume and Issue: unknown

Published: March 4, 2025

Abstract Fiber reinforced polymer (FRP) has emerged as a significant advancement in construction, with design provisions outlined by codes such GB/T 30022‐2013, CSA S806‐12 (R2017), and ACI 440:2015. While the use of FRP bars alternatives to conventional reinforcement columns been extensively studied, their application hollow concrete (HCCs) remains underexplored. This study investigates behavior FRP‐reinforced HCCs using advanced machine learning (ML) models, focusing on prediction two critical outputs: first peak load (Y1) failure (Y2), based eight input parameters. Models evaluated include extreme gradient boosting (XGB), light (LGB), categorical (CGB). A rigorous comparative analysis demonstrated that all models achieved high predictive accuracy, deviations within ±10% actual results, validating reliability. Among CGB exhibited superior generalization robustness, emerging most reliable predictor for HCC behavior. To enhance practicality, user‐friendly graphical user interface was developed allow engineers parameters instantly obtain predictions Y1 Y2. not only advances understanding but also bridges gap between computational real‐world applications, contributing robust tool structural engineering design.

Language: Английский

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Soft computing approaches for forecasting discharge over symmetrical piano key weirs

AI in Civil Engineering, Journal Year: 2025, Volume and Issue: 4(1)

Published: March 3, 2025

Abstract Piano Key Weir (PKW) is an advanced hydraulic structure that enhances water discharge efficiency and flood control through its innovative design, which allows for higher flow rates at lower upstream levels. Accurate prediction crucial PKW performance within various management systems. This study assesses the efficacy of Artificial-Neural-Network (ANN) Gene-Expression-Programming (GEP) models in improving symmetrical PKWs. A comprehensive dataset comprising 476 experimental records from previously published studies was utilized, considering a range geometric fluid parameters (PKW key widths, height, head). In training stage, ANN model demonstrated superior determination coefficient (R 2 ) 0.9997 alongside Mean Absolute Percentage Error (MAPE) 0.74%, whereas GEP yielded R 0.9971 MAPE 2.36%. subsequent testing both displayed high degree accuracy comparison to data, attaining value 0.9376. Furthermore, SHapley-Additive-exPlanations Partial-Dependence-Plot analyses were incorporated, revealing head exerted greatest influence on prediction, followed by height width. Therefore, these are recommended as reliable, robust, efficient tools forecasting Additionally, mathematical expressions associated script codes developed this made accessible, thus providing engineers researchers with means perform rapid accurate predictions.

Language: Английский

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Machine learning-based prediction of elliptical double steel columns under compression loading

Journal Of Big Data, Journal Year: 2025, Volume and Issue: 12(1)

Published: Feb. 27, 2025

Language: Английский

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Prediction of ultimate strength and strain in FRP wrapped oval shaped concrete columns using machine learning

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: March 28, 2025

The accurate prediction of the strength enhancement ratio ([Formula: see text]) and strain (εcc/εco) in FRP-wrapped elliptical concrete columns is crucial for optimizing structural performance. This study employs machine learning (ML) techniques to enhance accuracy reliability. A dataset 181 samples, derived from experimental studies finite element modeling, was utilized, with a 70:30 train-test split (127 training samples 54 testing samples). Four ML models: Decision Tree (DT), Adaptive Boosting (ADB), Stochastic Gradient (SGB), Extreme (XGB) were trained optimized using Bayesian Optimization refine their hyperparameters improve performance.Results demonstrate that SGB achieved best performance predicting [Formula: text], an R2 0.850, lowest RMSE (0.190), highest generalization capability, making it most reliable model predictions. For (εcc/εco), XGB outperformed other models, achieving 0.779 (2.162), indicating better balance between accuracy, generalization, minimal overfitting. DT ADB exhibited lower predictive performance, higher residual errors capacity. Furthermore, Shapley Additive exPlanations analysis identified FRP thickness-elastic modulus product (tf × Ef) compressive as influential features impacting both ratios. To facilitate real-world applications, interactive graphical user interface developed, enabling engineers input ten parameters obtain real-time

Language: Английский

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Experimental and Numerical Modeling of Seepage in Trapezoidal Channels

Knowledge-Based Engineering and Sciences, Journal Year: 2024, Volume and Issue: 5(3), P. 43 - 60

Published: Dec. 31, 2024

Accurately estimating seepage losses from unlined and lined trapezoidal channels is essential for effective water management, especially in water-scarce regions. This study combined experimental numerical approaches to evaluate losses, focusing on the influence of channel geometry liner properties, including hydraulic conductivity (KL) thickness (tL). Firstly, a physical model was constructed, materials were prepared, testing procedures performed estimate soil cement mixture. Secondly, five-channel geometries adjusted experiments. Finally, Slide2 results compared with data. Results revealed that accurately estimated high determination coefficient (R2) 0.99 low root-mean-squared-error (RMSE) values 2.85 0.03 cm3 s-1, respectively. For channels, increased larger bed width-to-water depth ratios due extended wetted perimeter. lining ineffective when KL exceeded 0.01, while 0.05 increase tL reduced by 15%. Furthermore, design charts equations developed considering dimensions, conductivity, thickness.

Language: Английский

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