Enhancing Discharge Prediction over Type-A Piano Key Weirs: An Innovative Machine Learning Approach

Flow Measurement and Instrumentation, Год журнала: 2024, Номер 100, С. 102732 - 102732

Опубликована: Ноя. 4, 2024

Язык: Английский

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

Computers & Structures, Год журнала: 2025, Номер 308, С. 107644 - 107644

Опубликована: Янв. 6, 2025

Язык: Английский

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

Journal of Water Process Engineering, Год журнала: 2025, Номер 70, С. 106913 - 106913

Опубликована: Янв. 9, 2025

Язык: Английский

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

Deleted Journal, Год журнала: 2025, Номер unknown

Опубликована: Янв. 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

Язык: Английский

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

Earth Science Informatics, Год журнала: 2025, Номер 18(2)

Опубликована: Фев. 1, 2025

Язык: Английский

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

Structural Concrete, Год журнала: 2025, Номер unknown

Опубликована: Март 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.

Язык: Английский

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

Modeling Earth Systems and Environment, Год журнала: 2025, Номер 11(2)

Опубликована: Фев. 13, 2025

Язык: Английский

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Soft computing approaches for forecasting discharge over symmetrical piano key weirs

AI in Civil Engineering, Год журнала: 2025, Номер 4(1)

Опубликована: Март 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.

Язык: Английский

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Machine learning and interactive GUI for estimating roller length of hydraulic jumps

Neural Computing and Applications, Год журнала: 2024, Номер unknown

Опубликована: Дек. 19, 2024

Язык: Английский

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Hydraulic assessment of different types of piano key weirs

ISH Journal of Hydraulic Engineering, Год журнала: 2024, Номер unknown, С. 1 - 24

Опубликована: Окт. 17, 2024

Piano Key Weir (PKW) is a non-linear weir with small foundation footprint that allows large discharges through narrow channel. The presence of overhangs classifies it into A, B, C, and D. For different PKW types, this study aims to assess the discharge, hydraulic characteristics (flow regimes, water surface profile, nappes interference), energy dispersion. This employs FLOW-3D software validated by comparing experimental types A D numerical simulations. Experimental simulation results agreed well, lower MAPE values for both types. After that, eight simulations each type were run, headwater ratios (Ht/P) from 0.13 0.85 (Ht: total upstream head above crest, P: height). Regarding discharge performance, type-B was superior all other at heads (Ht/P ≤0.40) due longer overhangs. While higher > 0.40), type-A became highest type. Since PKWs disperse more effectively than linear weirs, they acquire new performance as dissipators. Type-C had dispersion rate, followed type-A, type-D, type-B. Finally, an empirical equation provided predict rates over function coefficient.

Язык: Английский

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