Our foundation
AI4RISE Lab is dedicated to driving intelligent, resilient, and environmentally efficient infrastructure through artificial intelligence, advanced materials, and systems engineering. Our focus is on delivering practical, high-impact solutions that enhance structural performance, improve multi-hazard resilience, and minimize environmental impact.
Lead global innovation in resilient infrastructure engineering by harnessing AI, advanced materials, and systems engineering to create infrastructure systems that are smarter, safer, and more durable through interdisciplinary collaboration.
The overarching goal of AI4RISE Lab is to advance the resilience and adaptability of civil infrastructure systems under extreme events, such as earthquakes, floods, and climate-driven hazards, by synergistically integrating traditional engineering approaches (experimental investigations, analytical modeling, and high-fidelity numerical simulations) with advanced materials research and cutting-edge AI techniques (machine learning, deep learning, transfer learning, generative AI, physics-informed machine learning, explainable AI, and multi-objective optimization) within a systems engineering framework.
Engineering methods
Advanced materials
Artificial intelligence
Systems engineering framework
Links data, models and decisions across the infrastructure life cycle
Resilient, adaptable infrastructure
Reliable performance under earthquakes, floods and climate-driven hazards, with environmentally efficient materials and designs
Research program
Explainable and physics-informed machine learning, foundation and generative models, and surrogate modeling for structural analysis, design and health monitoring.
See projectsUltra-high-performance concrete, fiber- and steel-reinforced composites, shape memory alloys and titanium alloys for stronger and longer-lasting structures.
See projectsPerformance-based seismic design, fragility and resilience assessment, retrofitting and post-disaster evaluation of concrete structures and bridges.
See projectsEnvironmentally efficient concrete, valorization of industrial and agricultural byproducts, life-cycle assessment and multi-objective optimization of sustainable materials.
Browse publicationsWhat we study
01
Performance-based design frameworks, powered by AI and built on a systems engineering approach, that improve the efficiency and adaptability of structural systems.
02
Systems engineering frameworks and AI-driven analyses of multi-hazard scenarios that quantify vulnerabilities and guide risk-informed decisions.
03
Environmentally efficient materials and life-cycle strategies, optimized with AI-guided multi-objective algorithms without sacrificing durability or performance.
04
AI-enabled platforms, grounded in systems engineering, that help engineers and policymakers select materials, assess risks and plan resilient infrastructure under uncertainty.
05
Systems engineering methods and predictive analytics that evaluate FRP, FRCM, SRG, shape memory alloy and titanium alloy systems for strengthening and extending the life of existing infrastructure.
Milestones
$183K
Research grant
Multi-university study, funded by the Idaho Transportation Department, of protective overlays for Idaho’s bridge decks. The lab develops machine learning models of long-term overlay performance.
2,299
Growing body of highly cited research across AI, structural engineering, and advanced materials.
69
Published in top-tier journals including Engineering Structures, Applied Soft Computing, and Composites Part B.
#1
Paper on confined UHPC stress-strain modeling recognized as the #1 most popular article in Applied Soft Computing (Elsevier).
Paper on seismic performance of metallic dissipaters selected as Chief Editor’s Choice by the ASCE Journal of Structural Engineering (Feb 2026).
Selected for ASCE’s ExCEEd program focused on advancing excellence in civil engineering education.
Received the Expanding the NEST Innovator award from Kennesaw State University for Fall 2025.
People
Lab director
Assistant Professor, Department of Civil and Environmental Engineering
Kennesaw State University, Georgia, USA
Dr. Wakjira leads AI4RISE Lab. His research combines explainable and physics-informed machine learning with advanced materials, performance-based seismic design and 3D computer vision to improve the safety, resilience and environmental efficiency of civil infrastructure.
Research team
PhD, MSc and undergraduate researchers work alongside faculty collaborators at partner universities and national laboratories.
What drives us
Rigorous methods, careful validation and reproducible results that advance civil and computational engineering.
Ethical conduct, transparency and accountability in research, mentoring and professional practice.
An inclusive, multidisciplinary group in which students, researchers and partners work together on real infrastructure problems.
Research designed to inform engineering practice and agency decisions, leading to safer and longer-lasting infrastructure.
Partners
