iCity 2: IQG4iCity project

Climate change will continue to affect well-being and public health in cities through high pollution and noise emissions. Significant amounts of energy are wasted through building envelopes and heat networks. Facades also contribute to the uncontrolled distribution and absorption of noise emissions. In the context of further digitalization of urban life, new future information networks, and densification of urban areas, additional requirements for cities are emerging.

In the transdisciplinary project "Buildings, Neighborhoods and Infrastructure", new strategies and technologies of an "intelligent city" are developed with focus on holistic approaches in the context of energy efficiency, sustainability, climate protection, livability and health.

WP1: Digitization of heating networks for AI-based operational optimization - Digitalization can reduce increased heat losses in the networks (inefficient operation, too low spread between supply and return, high power consumption of network pumps).

WP2: Multifunctional, modular building envelopes for the urban space of the future - the aim is to contribute to increased energy efficiency and sustainability of buildings, neighborhoods and an increase in the quality of life.

WP3: Facade materials/facade condition of buildings - through image analysis, acoustics and photogrammetry, adaptations and efficiency improvements for facade materials and the facade condition of buildings will be investigated.

WP4: Sound and vibration protection of balconies and stairs - the sound and vibration protection of thermally separated balconies and decoupled concrete stairs will be integrated into a holistic concept to increase the quality of life in cities. The perception at low frequencies (<100Hz) are in the foreground.

Development of planning tools and methods with new technologies to holistic problem-solving approaches, which are standardized by suitable simplification for further practical use. Real-time data sets (laboratory) and potential analyses (case studies) are merged in databases and analyzed via AI processes. Simulation models for forecasting will be developed and validated.