Curriculum Vitae

Thesis: Computer vision-based lateral strength and stiffness loss estimation for seismically damaged RC columns using graph theory and machine learning. Research integrates computer vision, machine learning, and image processing.

Strong foundation in structural analysis, mechanics of materials, RC design, steel structures, soil mechanics, and numerical methods.

Thesis research on computer vision-based seismic damage assessment of RC columns using graph theory and machine learning. Developed Python and C# tools for image processing.

Published: Signal processing methods for structural health monitoring of bridges: A comprehensive review of classical, data-driven, and hybrid approaches — Journal of Advances in Bridge Engineering, Springer Nature.
Under Review: Performance-based ML-aided seismic design methodology for RC moment frames (ASCE 41-23).
Submitted: Computer vision-based post-earthquake damage evaluation of RC columns using graph theory and deep learning.
Submitted: Computer vision-based multi-criteria damage assessment for seismically damaged RC columns using graph theory and machine learning.
Submitted: Digital twin–enabled seismic resilience of civil infrastructure: integrating structural health monitoring, predictive modeling, and lifecycle decision support

Assisted with course instruction, graded assignments, and held office hours for graduate-level structural dynamics.

Supported undergraduate course delivery, graded coursework, and led review sessions.

Graded problem sets and conducted tutorial sessions for undergraduate mathematics course.

P. Khosravi Hajivand, S.M.M Emami, A. Safaeimehr, H. Hosseini, S. Mahboubi