Bayesian-optimized explainable boosting models for compressive strength of non-circular LRS-FRP-confined concrete

Interpretable and Uncertainty-Aware Machine Learning for Shear Strength Prediction of FRCM-Strengthened RC Beams

An interpretable and uncertainty-aware machine-learning framework for estimating the shear capacity of FRCM-strengthened beams enables accurate, transparent, and uncertainty-aware assessment of shear capacity in FRCM-strengthened concrete beams.

Machine Learning Models for Predicting Mechanical Properties of FRP-Confined Concrete Columns Across Low- to Ultra-High-Strength Concrete

Among all ML models, eXtreme gradient boosting demonstrated superior predictive performance, effectively capturing the nonlinear and multivariate interactions governing confinement effectiveness.

From finite element to practical prediction: an explainable ML-GUI framework for PVC tube-confined concrete columns

Interpretable machine learning for predicting shear capacity of ultra-high-performance concrete beams

Accurate prediction of shear capacity in reinforced concrete beams is crucial for structural safety assessment. Conventional theoretical methods exhibit significant variability due to the complexity of shear failure mechanisms. This study presents an interpretable machine learning (ML) framework to enhance shear capacity prediction. A comprehensive database of 1175 beam specimens was developed, including normal concrete (NC) and ultra-high-performance concrete (UHPC) beams across three distinct cross-sectional geometries. The ML algorithms–support vector regression, artificial neural network, K-Nearest neighbors, decision tree, random forest, gradient boosting machine, light gradient boosting machine, adaptive boosting, categorical boosting, and extreme gradient boosting (XGBoost)–were optimized using 10-fold cross-validation and random search. The XGBoost algorithm demonstrated superior performance, achieving an R2 of 0.986 on the aggregated data set. Interpretability analysis with Shapley additive explanations identified beam depth (h), shear-span ratio (m), cross-sectional area (Ac) and fibre factor (λf) as critical features, highlighting their individual and interactive contributions. Moreover, a unified ML-based shear strength prediction model was developed that simultaneously captures the shear behaviour of both NC and UHPC beams, incorporating physically meaningful input features derived from the data set, thereby overcoming the limitations of separate empirical formulations. The proposed ML-based model significantly improved the accuracy of shear strength predictions compared to traditional empirical methods, enhancing reliability in structural design.

Experiment-driven machine learning framework for compressive strength prediction of locally sourced fiber-reinforced concrete

This study proposes an experiment-driven machine learning (ML) framework to address experimental limitations in estimating compressive strength within a controlled experimental domain of locally sourced fiber-reinforced concrete (FRC). Concrete mixes incorporating 1, 2, and 3% lathe scrap steel fibers were prepared using a standard 1:2:4 mix with brick chips as coarse aggregate. Due to practical constraints, intermediate fiber contents (e.g., 1.5% and 2.5%) were not experimentally cast. To overcome this limitation, supervised ML models were employed to interpolate compressive strength at fractional fiber dosages, thereby reducing experimental cost and time. A total of 48 experimental observations obtained from laboratory-tested cylindrical specimens were used to develop and evaluate four ML algorithms—Gaussian Process Regression (GPR), Artificial Neural Network (ANN), Support Vector Machine (SVM) and Random Forest (RF)—under a five-fold cross-validation framework. Among these, GPR exhibited the best performance among the evaluated models (R² = 0.884, RMSE = 1.647 MPa), effectively capturing the nonlinear relationship between curing age, fiber content, and compressive strength. SHAP-based interpretability analysis indicated that curing age is the primary governing factor in strength development, while fiber content contributes through secondary nonlinear effects. The results further indicate that moderate fiber inclusion improves compressive strength, whereas excessive fiber content may reduce performance due to workability loss, fiber clustering, and increased matrix heterogeneity. These findings are consistent with established concrete mechanics, where fiber reinforcement plays a more dominant role in tensile behavior than in compressive strength. The proposed framework demonstrates the capability of probabilistic ML models for accurate interpolation within controlled experimental conditions using high-quality laboratory data. This study provides a practical, data-driven approach for strength estimation and mix optimization of low-cost, locally sourced FRC, supporting sustainable construction practices and efficient utilization of industrial waste materials.

Explainable ensemble learning framework for bond strength prediction in 3D printed concrete structures

A comprehensive data-driven framework integrating ensemble machine learning models with systematic hyperparameter sensitivity analysis and explainable artificial intelligence techniques is proposed, demonstrating that the XGB model significantly outperforms the other approaches, achieving superior accuracy and robust generalization.