The project aims to develop a prognosis and assistance system that is embedded in digital building modeling with respective detailing processes in early design phases. For instant performance prognosis, a component-based approach of machine learning (ML) replaces time-consuming and labor-intensive simulations as they are required for heating and cooling or for daylight in buildings. The approach models building construction and technology according to a paradigm of systems engineering and captures the dynamic behavior by coupled ML components. The component approach also integrates static calculations as they are required for lifecycle analysis (LCA) and lifecycle cost (LCC). Sampling methods disclose uncertainties in dynamic as well as static calculations. A knowledge base stores the developed components and performance models to provide instant access for immediate application in digital design and detailing processes. An assistance system based on these components will provide recommendations in the form of detailing options and strategies for the development of the design that show outstanding performance. For this purpose, graph transformations describe detailing operations related to the particular decision situation. Sampling and optimization methods explore the design space that is described in this way and identify options with high potential that are returned as strategies for informed decisions.

 
Projektlaufzeit:
seit 2016

Fördergeber:
Deutsche Forschungsgemeinschaft

Bearbeitung:

Professor Dr.-Ing. Markus König, Ruhr-Universität Bochum

Fakultät für Bau- und Umweltingenieurwissenschaften

Lehrstuhl für Informatik im Bauwesen

Professor Dr.-Ing. Frank Petzold, Technische Universität München (TUM)

TUM School of Engineering and Design

Lehrstuhl für Architekturinformatik

Professorin Dr.-Ing. Martina Schnellenbach-Held, Universität Duisburg-Essen

Abteilung Bauwissenschaften

Institut für Massivbau

Professor Dr.-Ing. André Borrmann, Technische Universität München

TUM School of Engineering and Design

Lehrstuhl für Computergestützte Modellierung und Simulation

Kooperationspartner:

Professor Dr.-Ing. Werner Lang, Technische Universität München (TUM)

TUM School of Engineering and Design

Lehrstuhl für energieeffizientes und nachhaltiges Planen und Bauen