Concrete compressive strength classification using hybrid machine learning models and interactive GUI DOI Creative Commons
Mostafa M. Alsaadawi, Mohamed Kamel Elshaarawy, Abdelrahman Kamal Hamed

et al.

Innovative Infrastructure Solutions, Journal Year: 2025, Volume and Issue: 10(5)

Published: April 28, 2025

Abstract Concrete Compressive Strength (CCS) is a critical parameter in structural engineering, influencing durability, safety, and load-bearing capacity. This study explores the classification of CCS using hybrid Machine Learning (ML) techniques an interactive Graphical User Interface (GUI). Advanced ML algorithms: Random Forest (RF), Adaptive-Boosting (AdaBoost), Extreme-Gradient-Boosting (XGBoost), Light-Gradient Boosting (LightGBM), Categorical-Boosting (CatBoost) were applied to categorize strength into Low, Normal, High classes. The dataset, comprising 1298 samples, was split 80% training 20% testing for evaluation. Hyperparameter tuning Bayesian Optimization with fivefold stratified cross-validation, resulting greatly improved model’s performance. Results showed that LightGBM achieved highest accuracy, scores 0.931 (Low), 0.865 (Normal), 0.935 (High), corresponding area under curve values 0.967, 0.938, 0.981. CatBoost also performed well, particularly Normal classes, while XGBoost slight overfitting class. RF AdaBoost had acceptable performance but struggled boundary cases. To interpret model predictions, SHapley-Additive-exPlanations (SHAP) analysis used. Curing duration cement content most influential factors across all water superplasticizer played secondary roles. Coarse aggregate became more significant High-Strength (HSC). A GUI developed allow practitioners input data receive real-time classifications, bridging gap between machine learning practical applications concrete design.

Language: Английский

Predicting seawater intrusion wedge length in coastal aquifers using hybrid gradient boosting techniques DOI Creative Commons
Mohamed Kamel Elshaarawy, Asaad M. Armanuos

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

Published: Feb. 1, 2025

Language: Английский

Citations

3
Deep learning-based modelling of polyvinyl chloride tube-confined concrete columns under different load eccentricities DOI

Li Shang,

Haytham F. Isleem, Mostafa M. Alsaadawi

et al.

Engineering Applications of Artificial Intelligence, Journal Year: 2025, Volume and Issue: 145, P. 110217 - 110217

Published: Feb. 13, 2025

Language: Английский

Citations

1
Prediction of ultimate strength and strain in FRP wrapped oval shaped concrete columns using machine learning DOI Creative Commons

Li Shang,

Haytham F. Isleem,

Walaa J K Almoghayer

et al.

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

Citations

1
Metaheuristic-driven CatBoost model for accurate seepage loss prediction in lined canals DOI Creative Commons
Mohamed Kamel Elshaarawy

Multiscale and Multidisciplinary Modeling Experiments and Design, Journal Year: 2025, Volume and Issue: 8(5)

Published: March 25, 2025

Language: Английский

Citations

0
An interpretable deep learning model for the accurate prediction of mean fragmentation size in blasting operations DOI Creative Commons

Baoqian Huan,

Xianglong Li, Jian-Guo Wang

et al.

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

Published: April 3, 2025

Language: Английский

Citations

0
Predicting axial load capacity in elliptical fiber reinforced polymer concrete steel double skin columns using machine learning DOI Creative Commons

F. T. S. Yu,

Haytham F. Isleem,

Walaa J K Almoghayer

et al.

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

Published: April 15, 2025

The current study investigates the application of artificial intelligence (AI) techniques, including machine learning (ML) and deep (DL), in predicting ultimate load-carrying capacity strain ofboth hollow solid hybrid elliptical fiber-reinforced polymer (FRP)-concrete-steel double-skin tubular columns (DSTCs) under axial loading. Implemented AI techniques include five ML models - Gene Expression Programming (GEP), Artificial Neural Network (ANN), Random Forest (RF), Adaptive Boosting (ADB), eXtreme Gradient (XGBoost) one DL model Deep (DNN).Due to scarcity experimental data on DSTCs, an accurate finite element (FE) was developed provide additional numerical insights. reliability proposed nonlinear FE validated against existing results. then employed a parametric generate 112 points.The examined impact concrete strength, cross-sectional size inner steel tube, FRP thickness both DSTCs.The effectiveness assessed by comparing models' predictions with results.Among models, XGBoost RF achieved best performance training testing respect determination coefficient (R2), Root Mean Square Error (RMSE), Absolute (MAE) values. provided insights into contributions individual features using SHapley Additive exPlanations (SHAP) approach. results from SHAP, based prediction model, indicate that area core has most significant effect followed unconfined strength total multiplied its elastic modulus. Additionally, user interface platform streamline practical DSTCs.

Language: Английский

Citations

0
A Comparative Exploration of Machine Learning Techniques for Compressive Strength Prediction in Copper Mine Tailing Concretes DOI
Eka Oktavia Kurniati,

Kudzai Musarandega,

Sefiu O. Adewuyi

et al.

Mining Metallurgy & Exploration, Journal Year: 2025, Volume and Issue: unknown

Published: April 25, 2025

Language: Английский

Citations

0
Concrete compressive strength classification using hybrid machine learning models and interactive GUI DOI Creative Commons
Mostafa M. Alsaadawi, Mohamed Kamel Elshaarawy, Abdelrahman Kamal Hamed

et al.

Innovative Infrastructure Solutions, Journal Year: 2025, Volume and Issue: 10(5)

Published: April 28, 2025

Abstract Concrete Compressive Strength (CCS) is a critical parameter in structural engineering, influencing durability, safety, and load-bearing capacity. This study explores the classification of CCS using hybrid Machine Learning (ML) techniques an interactive Graphical User Interface (GUI). Advanced ML algorithms: Random Forest (RF), Adaptive-Boosting (AdaBoost), Extreme-Gradient-Boosting (XGBoost), Light-Gradient Boosting (LightGBM), Categorical-Boosting (CatBoost) were applied to categorize strength into Low, Normal, High classes. The dataset, comprising 1298 samples, was split 80% training 20% testing for evaluation. Hyperparameter tuning Bayesian Optimization with fivefold stratified cross-validation, resulting greatly improved model’s performance. Results showed that LightGBM achieved highest accuracy, scores 0.931 (Low), 0.865 (Normal), 0.935 (High), corresponding area under curve values 0.967, 0.938, 0.981. CatBoost also performed well, particularly Normal classes, while XGBoost slight overfitting class. RF AdaBoost had acceptable performance but struggled boundary cases. To interpret model predictions, SHapley-Additive-exPlanations (SHAP) analysis used. Curing duration cement content most influential factors across all water superplasticizer played secondary roles. Coarse aggregate became more significant High-Strength (HSC). A GUI developed allow practitioners input data receive real-time classifications, bridging gap between machine learning practical applications concrete design.

Language: Английский

Citations

0