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MSc Defense — Computer Vision-Based Seismic Damage Assessment Using Graph Theory and ML
Successfully defended my Master's thesis at Shahid Beheshti University on computer vision, graph theory, and machine learning for post-earthquake RC column assessment.
Which Steel System Survives Best? FEMA P-695 Collapse Performance
First statistical comparison of SMF, SCBF, and EBF collapse margin ratios — 108 archetypes analyzed with ANOVA.
Beyond Pixel Counting: What Cracks Actually Tell Us About Structural Safety
How computer vision and machine learning read crack patterns to quantify post-earthquake damage.
Graph Theory for Post-Earthquake Damage Assessment: Why It Works
Converting crack patterns on RC columns into graph representations lets us quantify damage in ways pixel-based methods miss.
Signal Processing for Bridge SHM: Lessons from Our Review Paper
What we learned surveying classical, data-driven, and hybrid approaches to structural health monitoring.