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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61 to 70 of 1,261 Results
May 12, 2025 - EXC IntCDC Research Project 20 'Knowledge Representation for Multi-Disciplinary Co-Design'
Elshani, Diellza; Lombardi, Alessio; Hernández, Daniel; Staab, Steffen; Fisher, Al; Wortmann, Thomas, 2025, "Linked RDF graphs of an architectural and structural representation of a timber structure", https://doi.org/10.18419/DARUS-4360, DaRUS, V1
This dataset provides a collection of semantically enriched RDF graphs representing both architectural and structural aspects of a timber structure. The data is organized into modular Turtle (.ttl) files and one RIF rule definition (.txt) for reasoning purposes. Included in the dataset: Architectural Graph.ttl: Captures the spatial and material cha...
Turtle RDF - 4.0 MB - MD5: 679dcbc2dfd07bfa8fd58fdcc20bff5f
Turtle RDF - 463.8 KB - MD5: 6f863543d0040b2d6067b58d897024da
Neutral Building Model integrating architectural and structural data in RDF/Turtle format. All references to BHoM have been removed to ensure vendor neutrality and software independence. The model maintains essential relationships, linking architectural columns to structural bars and architectural floors to structural panels. It is fully queryable,...
Plain Text - 722 B - MD5: 636702fe448a3ab36d07cd74aca0609c
Turtle RDF - 10.2 MB - MD5: 4cb354dc580e24fc1b0578e1dc26e5be
Mar 17, 2025 - EXC IntCDC Associated Project 47 'Optimisation and Machine Learning for Climate-Friendly Design'
Zorn, Max Benjamin; Claus, Luisa; Frenzel, Christian; Wortmann, Thomas, 2024, "Replication Data for: Optimizing an expensive multi-objective building performance problem: Benchmarking model-based optimization algorithms against metaheuristics with and without surrogates.", https://doi.org/10.18419/DARUS-4532, DaRUS, V2
This dataset contains all generated samples for a multi-objective optimization benchmark on a realistic building performance simulation problem. The samples are saved in JSON files. Every file contains the results of an independent optimization run. The JSON log files are organized into two folders. AA_BPS_Benchmark contains the logs from the bench...
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