Research
My research focuses on structural health monitoring, damage identification and intelligent assessment of steel structures using experimental sensing, physics-based modelling, optimization and machine learning.
Structural Health Monitoring
Development of sensing and data-analysis techniques for detecting, localizing and assessing structural damage.
Damage Identification
Identification of structural damage using strain, vibration, impedance and physics-based response information.
Electromechanical Impedance
Piezoelectric sensing and impedance-based methods for local monitoring of steel structures and connections.
Structural System Identification
Estimation of structural parameters and damage characteristics using experimental measurements and numerical models.
Machine Learning for SHM
Statistical learning and deep-learning approaches for structural damage classification and localization.
Physics-Informed Machine Learning
Integration of structural mechanics with data-driven learning for robust structural condition assessment.
Steel Structures and Connections
Experimental and numerical investigation of damage in steel frames, welded connections and critical components.
Digital Twin
Integration of sensing, numerical modelling and intelligent algorithms for continuously updated structural representations.
Research Methodology
Experimental Sensing
Strain, vibration and electromechanical impedance measurements from structural systems.
Mechanics & Optimization
Structural modelling, sensitivity analysis, regularization and optimization-based inverse identification.
Intelligent Learning
Machine learning, deep learning and physics-informed techniques for structural condition assessment.