Hamed Bolandi

PhD Student
Google Scholar

I am a dual major Ph.D. candidate in Civil Engineering and Computer Science at Michigan State University under the supervision of Dr. Nizar Lajnef and Dr. Vishnu Boddeti.

Research Interests: Machine Learning, Deep Learning, Finite Element Analysis, Structural Health Monitoring

Current Projects:

  • Bridging Finite Element and Deep Learning
  • Deep Learning Paradigm for Prediction of Stress Distribution
  • Integrating Dynamic Finite Element Analysis and Attention Transformers

Computer Skills: Python, Matlab

Deep Learning: Pytorch

Selected Research Papers:

  • Deep Learning Paradigm for Prediction of Stress Distribution in Damaged Structural Components with stress concentrations (under review)

  • Integrating Dynamic Finite Element Analysis and Deep Learning to Predict Stress Distribution in Structural Components (under review)

  • A new predictive model for compressive strength of HPC using gene expression programming

  • Development of prediction models for shear strength of SFRCB using a machine learning approach

  • Multigene genetic programming for estimation of elastic modulus of concrete

  • A novel data reduction approach for structural health monitoring systems

  • Programmable assembly of bi-walled nonuniform beams: Concept, modeling, and performance

  • Bond strength prediction of FRP-bar reinforced concrete: a multi-gene genetic programming approach

  • Towards lateral performance of CBF with unwanted eccentric connection: A finite element modeling approach

  • Tunable Postbuckling Systems of Bi-Walled Nonuniform Beams

  • Numerical Study on the Impact of Out-of-Plane Eccentricity on Lateral Behavior of Concentrically Braced Frames


Mechanics-Informed Autoencoder Enables Automated Detection and Localization of Unforeseen Structural Damage

Physics Informed Neural Network for Dynamic Stress Prediction

Neuro-DynaStress: Predicting Dynamic Stress Distributions in Structural Components

NeuralSI: Structural Parameter Identification in Nonlinear Dynamical Systems

Deep learning paradigm for prediction of stress distribution in damaged structural components with stress concentrations

Methods For The Rapid Detection Of Boundary Condition Variations in Structural Systems

Bridging Finite Element and Deep Learning: High-Resolution Stress Distribution Prediction in Structural Components