Welcome
Welcome to Dr. Simwanda's AI 4 Risk, Reliability & Resilience (AI4R³) Lab!
We advance the safety, sustainability, and resilience of civil and structural infrastructure by integrating artificial intelligence (AI), probabilistic modelling, and data-driven design. Our work bridges cutting-edge computational methods with practical engineering to enable smarter decision-making under uncertainty.
Our mission is to quantify risk, enhance reliability, and build resilience into the next generation of infrastructure and materials — from ultra-high-performance concrete (UHPC) and innovative steel systems to bridges, cooling towers, and complex industrial structures.
Our Research Vision
Modern infrastructure faces increasingly complex demands — multi-hazard exposure, climate variability, material innovations, and sustainability targets — creating challenges such as:
High-dimensional data and complex input–output relationships
Uncertainty in materials, loads, and degradation processes
Interacting multi-hazard effects and cascading failures
Need for transparent, interpretable AI for safety-critical systems
Trade-offs between performance, carbon footprint, and cost
Limited experimental data for novel materials and systems
Real-time updating of models using monitoring data
We address these challenges by developing robust, scalable, and interpretable AI-driven methods that empower engineers to make risk-informed decisions.
Research Areas
We design and apply advanced methods to improve risk and resilience assessment, including:
Advanced Uncertainty Quantification — probabilistic modeling, Bayesian updating, and reliability analysis
Generative & Explainable AI — data augmentation, design optimization, and transparent predictions
Structural & Material Reliability — risk-based assessment of UHPC, cold-formed steel, and hybrid systems
Resilience & Sustainability Assessment — lifecycle design, carbon minimization, and climate adaptation
Intelligent Simulation & Surrogate Modeling — efficient alternatives to expensive finite element and multi-physics simulations
See our Research page for more.
