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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TAR Archive - 24.3 KB - MD5: 33db881cf3f39b29359cc578579ed327
Python scripts used to generate the tables and plots presented in the paper based on the experimental data. View README.md file for more detailed steps to reproduce the analysis.
Tabular Data - 1.3 KB - 3 Variables, 92 Observations - UNF:6:Cyou1bO+QPyzfhZGLR+QBA==
Data corresponding to the measured bond line thickness over the horizontal wide finger surface located at the compression side of the connection. Coordinates (x,y) and bond line thickness in [mm].
Tabular Data - 1.3 KB - 3 Variables, 92 Observations - UNF:6:DBoSP/EsBuxWL6yvBfhELg==
Data corresponding to the measured bond line thickness over the horizontal wide finger surface located at the tension side of the connection. Coordinates (x,y) and bond line thickness in [mm].
Tabular Data - 1.1 KB - 3 Variables, 79 Observations - UNF:6:e1lsDOV2X/SQrrgLNPvbFg==
Data corresponding to the measured bond line thickness over the inclined wide finger surface located at the compression side of the connection. Coordinates (x,y) and bond line thickness in [mm].
Tabular Data - 1.1 KB - 3 Variables, 79 Observations - UNF:6:7VjP97MXwJwPSfyLBYlIdQ==
Data corresponding to the measured bond line thickness over the inclined wide finger surface located at the tension side of the connection. Coordinates (x,y) and bond line thickness in [mm].
Tabular Data - 12.6 KB - 11 Variables, 211 Observations - UNF:6:0kyC8KaeRBn+OcJ4LPX2ow==
Shear strength (Fv) and fiber breakage (FB) data for specimens corresponding to all specimens. Strength in MPa and fiber breakage in percent (%). Definition of variables: - 'surf_type': type of surface, i.e. either "upper" (U), "lower" (L) or "vertical" (V). - 'surface_num': number of the wide side finger surface (only applicable to surfaces of typ...
Tabular Data - 5.1 KB - 11 Variables, 80 Observations - UNF:6:uU7oOJnqPUSdF956iTJCRw==
Shear strength (Fv) and fiber breakage (FB) data for specimens corresponding to the horizontal surfaces of alternative A. Strength in MPa and fiber breakage in percent (%). Definition of variables: - 'surf_type': type of surface, i.e. either "upper" (U), "lower" (L) or "vertical" (V). - 'surface_num': number of the wide side finger surface. - 'surf...
Tabular Data - 1.4 KB - 6 Variables, 43 Observations - UNF:6:yJ+IZvnsQz6jI5xRHGruww==
Shear strength (Fv) and fiber breakage (FB) data for specimens corresponding to the vertical surfaces of alternative A. Strength in MPa and fiber breakage in percent (%). Definition of variables: - 'surf_type': type of surface, i.e. either "upper" (U), "lower" (L) or "vertical" (V). - 'side': side from which the block shear specimens were extracted...
Tabular Data - 5.6 KB - 11 Variables, 88 Observations - UNF:6:ynTDasRZTZ90YDSQOzEiSw==
Shear strength (Fv) and fiber breakage (FB) data for specimens corresponding to the horizontal surfaces of alternative B. Strength in MPa and fiber breakage in percent (%). Definition of variables: - 'surf_type': type of surface, i.e. either "upper" (U), "lower" (L) or "vertical" (V). - 'surface_num': number of the wide side finger surface. - 'surf...
Sep 20, 2021 - EXC IntCDC 'Research Data Management'
Braun, Matthias, 2021, "EXC IntCDC - Data Management Plan Template for Research Projects", https://doi.org/10.18419/DARUS-2116, DaRUS, V1, UNF:6:Al3ljb79iWiURj4OdonArw== [fileUNF]
This Data Management Plan describes the data management life cycle for the data, a Research Project of EXC IntCDC will collect, process and/or generate. Moreover, it describes whether and how this data is being used and/or made publicly available for verification and re-use and how the data will be curated and preserved after the end of the project...
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