Optimizing high-strength concrete compressive strength with explainable machine learning

Multiscale and Multidisciplinary Modeling Experiments and Design, Journal Year: 2025, Volume and Issue: 8(3)

Published: Feb. 3, 2025

Language: Английский

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

Neural Computing and Applications, Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 19, 2024

Language: Английский

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Predictive modeling for durability characteristics of blended cement concrete utilizing machine learning algorithms

Case Studies in Construction Materials, Journal Year: 2025, Volume and Issue: unknown, P. e04209 - e04209

Published: Jan. 1, 2025

Language: Английский

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Applications of machine learning methods for design and characterization of high-performance fiber-reinforced cementitious composite (HPFRCC): a review

Journal of Sustainable Cement-Based Materials, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 24

Published: Feb. 6, 2025

Language: Английский

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Advanced predictive machine and deep learning models for round-ended CFST column

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

Published: Feb. 20, 2025

Confined columns, such as round-ended concrete-filled steel tubular (CFST) are integral to modern infrastructure due their high load-bearing capacity and structural efficiency. The primary objective of this study is develop accurate, data-driven approaches for predicting the axial load-carrying (Pcc​) these columns benchmark performance against existing analytical solutions. Using an extensive dataset 200 CFST stub column tests, research evaluates three machine learning (ML) models - LightGBM, XGBoost, CatBoost deep (DL) Deep Neural Network (DNN), Convolutional (CNN), Long Short-Term Memory (LSTM). Key input features include concrete strength, length, cross-sectional dimensions, tube thickness, yield which were analysed uncover underlying relationships. results indicate that delivers highest predictive accuracy, achieving RMSE 396.50 kN R2 0.932, surpassing XGBoost (RMSE: 449.57 kN, R2: 0.906) LightGBM 0.916). less effective, with DNN attaining 496.19 0.958, while LSTM underperformed substantially 2010.46 0.891). SHapley Additive exPlanations (SHAP) identified width most critical feature, contributing positively capacity, length a significant negative influencer. A user-friendly, Python-based interface was also developed, enabling real-time predictions practical engineering applications. Comparison 10 demonstrates traditional methods, though deterministic, struggle capture nonlinear interactions inherent in thus yielding lower accuracy higher variability. In contrast, presented here offer robust, adaptable, interpretable solutions, underscoring potential transform design analysis practices ultimately fostering safer more efficient systems.

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

Results in Engineering, Journal Year: 2025, Volume and Issue: 25, P. 104542 - 104542

Published: March 1, 2025

Language: Английский

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Experimental and explainable machine learning based investigation of the coal bottom ash replacement in sustainable concrete production

Journal of Building Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 112367 - 112367

Published: March 1, 2025

Language: Английский

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Microstructural assessment and supervised machine learning-aided modeling to explore the potential of quartz powder as an alternate binding material in concrete

Case Studies in Construction Materials, Journal Year: 2025, Volume and Issue: unknown, P. e04568 - e04568

Published: March 1, 2025

Language: Английский

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Innovative hybrid machine learning models for estimating the compressive strength of copper mine tailings concrete

Case Studies in Construction Materials, Journal Year: 2024, Volume and Issue: 21, P. e03869 - e03869

Published: Oct. 16, 2024

Language: Английский

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