Research

The research strategy of the professorship Computational Architecture is closely linked with the context of a framework of Cognitive Computing in Design (CCD). CCD is the basis for a new kind of intelligent digital design method that makes it possible to transform data into information and knowledge, enabling us to plan our built environment in a smarter way to make it more liveable, sustainable and resilient.

You can find CCD project on the following websites:

The description of the main research streams for CCD and how it can be combined with recent trends in smart buildings and cities, big data, and spatial simulation is outlined in my research profile.


Research Projects


DigiWo

Digital methods for integrated and scale independent generation, analysis and exploration of residential building variants.
Nov. 2018 – Dec. 2020

Abstract

The planning of a multi-family-residential building can be characterized as a multi-objective optimization problem, as multiple criteria must be considered and negotiated with each other. The output of this process - the building design - has a great impact on the social, economic, and ecological performance of the building for a long time. Therefore, it is crucial to find the optimal solution with the best trade-off between the performance criteria. The challenges that the planner faces during the planning process are (a) a large number of the design options and (b) that the fitness criteria (goals) vary from project to project and even during the work at one design. Currently, the process of the manual solution search is highly time-consuming, and hence, it is usually not possible to guarantee that the "true" optimal solution is found. The goal of this project is to develop a method for efficient automatic generation of design options for multi-family-residential buildings. The focus of the project is the cross-scale generation of designs, from the building envelopes to the detailed floorplans.

In order to enable the consideration of qualitative aspects during the generation, various ways for user interaction are introduced. Thus, the user can direct the generation into the wished direction, avoiding the creation of the variants, that do not meet the user's needs. Furthermore, after generating multiple design options, there comes the challenge of choosing the best options among them. In order to find the optimal solution, the designs must be analyzed and then compared to each other. Therefore, another stage of this project is the development of the tools for performing analysis for various criteria, such as floor area, daylight simulation, energy efficiency, visibility measurements, etc. At last, the analyzed designs can be compared with each other, using the multi-stage design exploration framework, developed during the ILCO project.

Funding by

BBSR (Zukunft Bau). Aktenzeichen: SWD—10.08.18.7-18.15
DIPLAN Gesellschaft für Digitales Planen & Bauen GmbH
Decoding Spaces GbR

Project Lead

Jun.-Prof. Dr. Reinhard König (Junior.Professur Computational Architecture)
Vertr. Prof. Dr. Sven Schneider (Informatik in der Architektur)

Research Team

Iuliia Osintseva and Andreas Berst

DigiWo Demonstration Video

Automated parametric building block generation

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Further Information
Project documentation and downloads of the developed software components:
https://toolbox.decodingspaces.net/digiwo/



DiPlanner

2017-2018 // funded by DIPLAN.

Abstract

The DiPlanner research project in a collaboration between the Junior-Professorship Computational Architecture and the companies DIPLAN and DeCodingSpaces.

In this project, we develop algorithms for the generation of building volumes and floorplans for the semi-automated design of residential building. On the one hand, this involves the implementation and testing of new generative algorithms for the three scale levels of building volume, residential units, apartment floor plans and, on the other hand, the exploration of procedures with regard to concrete application to practical examples. The system for prefabricated concrete elements developed by DIPLAN is also taken into account.

Research Team

Iuliia Osintseva, Reinhard Koenig, Martin Bielik, Sven Schneider

Demonstration Video

DiPlanner

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