AI for Resilient Infrastructure and System Engineering
Intelligent, resilient and environmentally efficient infrastructure
AI4RISE Lab at Kennesaw State University develops artificial intelligence, advanced materials and systems engineering methods that make bridges, buildings and pavements safer, longer-lasting and environmentally efficient.
- Digital twin · live deflection
- Strain
- AR/VR inspection
- Neural network inference
Recognition
- Most Popular in Applied Soft Computing since 2024
- 2025 ASCE ExCEEd Fellow
- NEST Innovator, Fall 2025
About the lab
Engineering-informed artificial intelligence for civil infrastructure
Founding director
Dr. Tadesse G. Wakjira
Assistant Professor of Civil and Environmental Engineering, Kennesaw State University
View profileAI4RISE Lab develops artificial intelligence methods grounded in engineering principles, experimental evidence and numerical simulation to support the design, assessment and management of civil infrastructure.
Research output
Citation metrics from Google Scholar
- Peer-reviewed publications
- 69
- Citations
- 2,001
- h-index
- 27
- i10-index
- 34
Research team
9 current researchers · 8 alumni · 6 collaborators
+4Research program
Four connected research pillars
AI-Powered System Engineering
7 projectsAdvanced Materials
1 projectMulti-Hazard Resilience
1 projectEnvironmental Efficiency
Learn moreMethods and themes across all pillars
- AI/ML
- Physics-informed AI
- Structural and community resilience
- Performance-based design
- 3D computer vision
- Multi-objective optimization
Current projects
Active research projects
- 98.5%R25
NeRF-UAVeL
Unified attention-driven volumetric learning framework for state-of-the-art NeRF-based 3D object detection
AI-Powered System Engineering · 3D Computer Vision
- $220KFunding
GeoPAVE-AI
Developing an AI-powered computer vision framework for automated concrete pavement condition assessment and intelligent maintenance planning.
AI-Powered System Engineering
- 0.055NRMSE
PITCH
A physics-informed transformer with token classification for hysteretic response prediction and uncertainty quantification of RC columns.
AI-Powered System Engineering
SA-EgoGS
Sharpness-aware dynamic 3D Gaussian Splatting for robust egocentric scene reconstruction with anti-aliasing and uncertainty-aware densification.
AI-Powered System Engineering · 3D Computer Vision
- 6-ClassCrack Types
ConceptCrack
Autonomous AI pipeline fusing YOLO26, SAM 3, and LLM for real-time crack detection and structural risk reporting.
AI-Powered System Engineering
VulnCAST
AI-powered seismic vulnerability and capacity assessment framework for reinforced concrete structures.
Multi-Hazard Resilience
- 5Students
AI-StructVision
Can AI learn what ‘dangerous damage’ looks like in critical infrastructure?
AI-Powered System Engineering
- 5+Publications
UHPC-AI
A multi-scale AI framework spanning UHPC mix optimization, constitutive modeling, performance-based seismic design, and bridge resilience assessment.
AI-Powered System Engineering · Advanced Materials
Highlights
Research impact
01
Multi-hazard resilience
AI and systems engineering methods that improve the resilience of bridges and buildings to earthquakes, floods and other hazards.
1 related project02
Environmental efficiency
Resource-efficient materials and infrastructure across the life cycle, through AI-enabled life-cycle analysis and management.
Learn more03
Intelligent infrastructure
Advanced materials, AI-driven analytics and systems engineering, integrated to support data-informed infrastructure decisions.
7 related projects
News
Latest updates
$183K ITD Grant to Protect Idaho's Bridge Decks
AI4RISE Lab is part of a multi-university team funded through the Idaho Transportation Department (ITD) Research Program ($183,139, three years) to evaluate protective overlays for Idaho's bridge decks. The lab will develop machine learning models that predict how each overlay performs over time, informing a practical decision guide for ITD engineers.
Read on isu.eduASCE Chief Editor's Choice
Paper on seismic performance of metallic dissipaters selected as Chief Editor’s Choice by the ASCE Journal of Structural Engineering
More news
CompMoE: Compositional Mixture-of-Experts Neural Network for Sustainable Mix Optimization of Fiber-Reinforced Ultra-High-Performance Concrete
Materials Today Advances
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