Fig. 12 — Skills

Instruments & methods

Machine Learning & Modelling

Predictive AlgorithmsRegression & ensemble methods

Gradient Boosting XGBoost CatBoost Random Forest AdaBoost Extra Trees Decision Trees KNN SVR MARS

Neural NetworksDeep & hybrid architectures

Artificial Neural Networks (ANN) Deep Learning Hybrid Metaheuristic-ANN Models

InterpretabilityExplaining model behaviour

SHAP Analysis Feature Importance Sensitivity Analysis Cross-Validation (k-fold, LOOCV)

Civil Engineering Domains

Structural MaterialsConcrete & composites

Ultra-High Performance Concrete GGBS & Silica Fume Systems Fibre-Reinforced Concrete FRP-Confined Columns Self-Compacting Concrete

Geotechnical EngineeringGround improvement

Subgrade Soil Reinforcement Geogrid Systems Bearing Capacity Prediction

Pavement & SustainabilityMaterials with recycled content

Stone Mastic Asphalt Recycled-Waste Materials Durability Assessment

Fig. 13 — Working Proficiency

Experimental Materials Testing

Machine Learning Model Development

Statistical & Explainability Analysis

Technical Writing & Publication

Teaching & Project Supervision

NOTE

These reflect a qualitative sense of day-to-day working proficiency across core research activities, not a formal certification scale.