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Jul 2026
An investigation of flexural resistance of high-performance steel fiber-reinforced cementitious composites: data-driven prediction and experimental verification
ABSTRACT This study establishes four popular data-driven techniques – Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), and Gradient Boosting (GB) for predicting the flexural strength (FS) of high-performance steel fiber-reinforced cementitious composites (FRCCs). A database containing 156 experimen...
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· Journal of Structural Integr... · 0 citations
2026
Machine learning-based prediction of pore characteristics in laser powder bed fusion-fabricated 316L stainless steel
Precise prediction and control of porosity in laser powder bed fusion (L-PBF) directly enhances the performance of additively manufactured components. This study addresses the need for comprehensive machine learning (ML) analysis of pore characteristics through comparative evaluation of multiple ML models based on ac...
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Jul 2026
Prediction of compressive and flexural strength of modified concrete using machine learning
Results suggest that, within the present five-fold cross-validation setting and limited-sample dataset, RBF kernel ridge regression captures the nonlinear relationships more effectively than conventional linear models; however, broader generalization should be verified using larger datasets and additional validation.
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Open access
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Machine Learning-Based Prediction of Compressive Strength in Basalt Fiber Reinforced Concrete
Accurate prediction of the mechanical strength of Basalt Fiber Reinforced Concrete (BFRC) is critical for structural design, safety assessment, and the advancement of sustainable infrastructure in civil engineering. Traditional prediction methods often fail to capture the nonlinear relationships between BFRC mix propor...
R. R. Khasani, Ferry Hermawan, Yuliana Usman
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Open access
Jul 2026
Machine Learning Prediction of Concrete Compressive Strength: Model Comparison, CatBoost Optimization, and SHAP Interpretation
A comparative framework evaluating nine regression algorithms using the UCI Concrete Compressive Strength dataset, jointly integrating correlation-corrected statistical validation, multi-model Bayesian optimization, and domain-informed feature engineering with SHAP interpretation, rarely combined in prior concrete-stre...
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Machine Learning-Assisted Prediction and Multi-Objective Optimisation of Rice Husk Ash and Micro Fine Slag Stabilised Black Cotton Soil for Sustainable Pavement Subgrades
Black cotton soil is a highly expansive geomaterial whose high plasticity, low soaked bearing capacity and moisture-driven volume change frequently cause pavement distress, subgrade deformation and serviceability loss. Sustainable soil stabilisation using rice husk ash (RHA) and Micro Fine Slag (MFS) is promising becau...
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