Vagelis Plevris

Vagelis Plevris's research focuses on the development and application of computational and intelligent methods for the analysis, design, optimization, and assessment of engineering structures. His work integrates numerical simulation, structural mechanics, optimization, reliability analysis, and artificial intelligence, with applications in earthquake engineering, structural health monitoring, and civil infrastructure.

Research Themes

• Computational Structural Engineering

  Development and application of numerical and computational methods for modeling, analyzing, and simulating the behavior of engineering structures under various loading conditions.

  • Computational Mechanics
  • Finite Element Methods (FEM)
  • Structural Analysis
  • Nonlinear Structural Analysis
  • Numerical and Symbolic Methods
  • Computational Modeling and Simulation

• Structural Optimization, Reliability & Uncertainty

  Development of optimization-based approaches for efficient structural design, incorporating mathematical algorithms, computational intelligence, reliability analysis, and uncertainty in engineering decision-making.

  • Structural Optimization and Optimum Design
  • Metaheuristic and Evolutionary Algorithms
  • Multi-objective Optimization
  • Topology Optimization
  • Reliability-Based and Robust Design
  • Uncertainty Quantification

• Earthquake Engineering & Structural Dynamics

  Computational analysis and assessment of structural response under dynamic and seismic loading, with emphasis on earthquake-resistant design, seismic vulnerability, and structural performance.

  • Structural Dynamics
  • Seismic Analysis
  • Earthquake-Resistant Design
  • Performance-Based Seismic Assessment
  • Seismic Vulnerability and Risk
  • Dynamic Response of Structures

• Artificial Intelligence & Machine Learning in Engineering

  Development and application of intelligent computational and data-driven methods for engineering analysis, prediction, modeling, optimization, and decision support.

  • Artificial Intelligence and Machine Learning
  • Artificial Neural Networks
  • Deep Learning
  • Computer Vision and Image Processing
  • Surrogate Modeling and Predictive Methods
  • AI-Assisted Engineering Analysis

• Structural Health Monitoring & Digital Infrastructure

  Development and application of data-driven methods and digital technologies for monitoring, damage identification, condition assessment, and improved management of civil infrastructure.

  • Structural Health Monitoring (SHM)
  • Structural Damage Detection
  • Image-Based Inspection and Assessment
  • Digital Twins
  • Bridge and Infrastructure Monitoring
  • Infrastructure Resilience and Condition Assessment