Concrete compressive strength classification using hybrid machine learning models and interactive GUI

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

Hybrid machine learning modelling and feature interpretation of load-carrying capacity of PVC tube-confined concrete columns

Asian Journal of Civil Engineering, Journal Year: 2025, Volume and Issue: unknown

Published: May 8, 2025

Language: Английский