Forschungsprojekte

Das TUM Georg Nemetschek Institut fördert zahlreiche Forschungsprojekte, die an der Schnittstelle zwischen der grundlagenorientierten KI-Forschung der Informatik und der Anwendung für die bebaute Umwelt liegen. Zur Zeit werden die folgenden sechs Projekte gefördert:
 

AI4TWINNING
Artificial Intelligence for the automated creation of multi-scale digital twins of the built world

AICC
Artificial Intelligence for smart design and testing of cement and concrete

INFRA.RELEARN
Intelligent infrastructure maintenance with deep reinforcement learning

DeepMonitor
Deep physics based structural health monitoring

RADELN
Bicycle infrastructure & network design – a human-centric, data-driven approach using spatio-temporal machine learning

SPAICR
Spatial AI for cooperative construction robotics

AI4BuildingESG
Breaking into the black box of ESG in the building sector: a machine learning approach

OBACHT
Smart public transpOrt infrastructure control system - an AI-Based ApproaCH to ensure safety, accessibility & efficiency of public Transport

FORWARD
Pedestrian dynamics prediction for safe and flow-efficient building design

CODA-AI
Early stage damage detection in concrete using coda signals and AI

AIDABI
Artificial Intelligence for the automated creation of a digital archive of bridge infrastructure

CoCoRo
Collaborative Construction Robots

NERF2BIM
AI-Driven Detailing-on-Demand through Sustainable Point Cloud Surveys and Semantic 3D Understanding for Advanced Modeling of Existing Buildings

VAULT-AI
Computational design and fabrication of self-supporting VAULTed structures through AI-driven human-robot cooperation

AUROrA
AI-Driven Urban Flood Resilience: Integrating Earth Observation and Architectural Innovation

InsAlderKnowledge
Spatial Structure Estimation of Buildings from Outer Building Envelopes

MITNAND
Participatory Mobility Innovations Through Networked and Adaptive Traffic Infrastructure Design

SAFEMAP
Structural Assessment and Optimization using Fast and Efficacious Multi-data AI Approaches with Hybrid Multi-Physics under Natural Hazards

THERMOSTAT
Developing Statistical Methods for Analyzing Time-Series Thermal Physiological Data to Increase Accuracy in Occupant Behavior and Thermal Comfort Modeling