Next Generation AI Research Laboratory
Powering the Next Generation Structural Digital Twin Ecosystem
The World's First Open Ecosystem and Repository for Structural AIs.
A unified cloud interface to access and deploy physics-informed models. Browse the active registry below or contribute your own research to the ecosystem.
Deploy Your Intelligence
Have a trained structural model? Deploy it on REALM Ecosystem to gain global reach. Join the waitlist for The REALM Impact Challenge.
Select Predictor
Choose an active model from the REALM registry.
Target Parameter
Select the variable to predict.
Engine Status
Connected: Standby
Input Matrix
Ongoing Research & Development
REALM AI LAB is continuously evolving. The following modules are currently in the active development phase, designed to realize a comprehensive "Digital Twin Ecosystem" for the autonomous monitoring and performance-based health assessment of infrastructure assets subjected to diverse stressors—ranging from environmental aging and marine corrosion to seismic hazards.
Interoperable API Gateway
A RESTful cloud-to-client architecture designed to bridge the gap between web-based AI and desktop simulation. This module will allow the REOS software to programmatically fetch constitutive parameters, enabling fully automated "Digital Twin" model updates.
Smart Vision Pipeline
Integration of Convolutional Neural Networks (CNNs) for autonomous damage assessment. This module focuses on smart crack detection and quantification on concrete surfaces from site inspection images.
Physics-Informed Corrosion
Next-generation degradation models using Physics-Informed Machine Learning (PIML). This research aims to improve long-term corrosion propagation predictions by embedding physical laws directly into the neural network loss functions.
Workflow Composer & Chaining
An orchestration engine designed to link isolated predictive models into a seamless, end-to-end pipeline. This architecture supports Model Chaining—allowing outputs from one prediction model (e.g., vision-based crack quantification) to automatically populate the input matrices of another prediction model (e.g., structural behavioral predictors).