Research Portfolio

Research Projects

Our projects span four research pillars, from AI-driven structural modeling to advanced materials and lifecycle management. Click any project to explore its architecture, methodology, and results.

5 projects

📊

GeoPAVE-AI: AI-Powered Assessment & Intelligent Maintenance of Georgia’s Concrete Pavements

PI: Dr. Tadesse Wakjira💰 $220,000 · Georgia DOT (GDOT)AI-Driven Structural PerformanceActive

Developing an AI-powered computer vision framework for automated concrete pavement condition assessment and intelligent maintenance planning for GDOT.

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Key Results

$220K

Funding

Georgia DOT sponsored

Decision-support prototype

Novel Contributions

End-to-End AI Pipeline

GDOT Decision-Support Tool

Georgia-Specific Dataset

Computer VisionDeep LearningAI/MLPCI / ASTM D6433Decision Support

Pillar 1

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🧠

PITCH: Physics-Informed Transformer for Hysteretic Response Prediction

PI: Dr. Tadesse WakjiraAI-Driven Structural PerformanceActive

A physics-informed transformer with token classification for hysteretic response prediction and uncertainty quantification of RC columns.

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Key Results

Novel Contributions

Soft-Decode Bridge

FiLM Conditioning

Native Uncertainty Quantification

JAX/XLATransformerGBMPhysics-Informed LossToken Classification

Pillar 1

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🛡️

VulnCAST: Vulnerability & Capacity Assessment using Smart Transformers

PI: Dr. Tadesse WakjiraMulti-Hazard ResilienceActive

AI-powered seismic vulnerability and capacity assessment framework for reinforced concrete structures.

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Key Results

Seismic

Application

Multi-hazard assessment

Portfolio

Scale

Regional risk analysis

Transformer

Method

Attention-based models

Fragility

Output

Damage probability curves

Novel Contributions

Transformer for Fragility

Multi-Scale Assessment

Interpretable Risk Models

TransformerXGBoostFragility AnalysisSHAPOpenSees

Pillar 2

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👁️

AI-StructVision: Teaching AI to Recognize Dangerous Structural Damage

PI: Dr. Tadesse Wakjira💰 Internally Funded · KSU (Internal)AI-Driven Structural PerformanceActive

Can AI learn what ‘dangerous damage’ looks like in critical infrastructure? A first-year research experience in computer vision for structural safety.

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