The vision of the Cluster of Excellence Integrative Computational Design and Construction for Architecture (EXC IntCDC) is to harness the full potential of digital technologies in order to rethink design, fabrication and construction based on integration and interdisciplinarity, with the goal of enabling game-changing innovation in the building sector as it can only occur through highly integrative fundamental research in an interdisciplinary, large-scale research undertaking.

The Cluster aims to lay the methodological foundations for a profound rethinking of the design and building process and related building systems by adopting an integrative computational approach based on interdisciplinary research encompassing architecture, structural engineering, building physics, engineering geodesy, manufacturing and system engineering, computer science and robotics, social sciences and humanities. We aim to bundle the internationally recognised competencies in these fields of the University of Stuttgart and the Max Planck Institute for Intelligent Systems to accomplish our research mission.

The Cluster’s Industry Consortium will ensure direct knowledge exchange, transfer and rapid impact. Taking into account the significant difference between the building industry and other industries, we will tackle the related key challenges of achieving a higher level of integration, performance and adaptability, and we will address the most important building typologies of multi-storey buildings, long-span buildings, and the densification of urban areas.

The Cluster’s broad methodological insights and interdisciplinary findings are expected to result in comprehensive approaches to harnessing digital technologies, which will help to address the ecological, economic and social challenges that current incremental approaches cannot solve.

We envision IntCDC to significantly shape the future of architecture and the building industry through a higher-level integration of computational design and engineering methods, effective cyber-physical (tightly interlinked computational and material) robotic construction processes and new forms of human-machine collaboration, efficient and sustainable next-generation building systems, and socio-cultural and ethical reflection. Thus, the Cluster will have significant impact on creating the conditions required for a liveable and sustainable future built environment, high-quality yet affordable architecture and a novel digital building culture.
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The TrueType font "WeblySleek UI Semilight" has been created by Mat Douglas and is published under Public domain/GPL/OFL: https://fontpro.com/weblysleek-ui-font-9838
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The TrueType font "WeblySleek UI Semilight" has been created by Mat Douglas and is published under Public domain/GPL/OFL: https://fontpro.com/weblysleek-ui-font-9838
application/x-font-ttf - 394.5 KB - MD5: ae2de203b798f6bbca3e697a5f9b6571
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The TrueType font "WeblySleek UI Semilight" has been created by Mat Douglas and is published under Public domain/GPL/OFL: https://fontpro.com/weblysleek-ui-font-9838
application/x-font-ttf - 608.3 KB - MD5: 7f443f2bc51d0297d05f8dce72c9e8b9
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The TrueType font "WeblySleek UI Semilight" has been created by Mat Douglas and is published under Public domain/GPL/OFL: https://fontpro.com/weblysleek-ui-font-9838
EXC IntCDC 'Research Data Management'(University of Stuttgart, Max Planck Institute for Intelligent Systems)
EXC IntCDC 'Research Data Management' logo
Sep 16, 2021
Information provided by the Research Data Management team of IntCDC.
Sep 14, 2021 - isw_kar_ puplication
Kaiser, Benjamin, 2021, "Replication data for: Planning of Curvature-Optimal Smooth Paths for Industrial Robots Using Neural Networks", https://doi.org/10.18419/DARUS-2126, DaRUS, V1, UNF:6:wNKYRgrs4Qt6BOHEvqTtyQ== [fileUNF]
This dataset contains curvature-optimal geometry parameters for linear-linear transitions for polynomial smoothing with polynomial splines 4th order. The data was generated by offline solving the optimization problem of curvature-optimal geometry parameters for corner smoothing, which is described in the related publication. Dataset_linlin.tab cont...
Tabular Data - 175.3 KB - 8 Variables, 3167 Observations - UNF:6:BBlGUuU5jyWJnJOd20wJ2g==
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