The vision of the Cluster of Excellence Integrative Computational Design and Construction for Transformative 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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1 to 10 of 131 Results
Aug 11, 2026 - EXC IntCDC Associated Project 58 'NeuralWood'
Akbar, Zuardin; Gambarelli, Serena; Wortmann, Thomas, 2026, "NeuralWood Spruce Boards: Raw Dataset", https://doi.org/10.18419/DARUS-5683, DaRUS, V1
Overview This dataset is part of the project Neuralwood: Neural Networks for the Prediction and Utilization of Natural Material Variations in Timber Construction by the Department for Computing in Architecture, Institute for Computational Design and Construction (ICD/CA) and Materials Testing Institute (MPA), University of Stuttgart. The dataset co...
Jul 21, 2026 - EXC IntCDC Associated Project 47 'Optimisation and Machine Learning for Climate-Friendly Design'
Claus, Luisa; Joller, Simon; Yang, Xiliu; Zorn, Max Benjamin; Wortmann, Thomas, 2026, "Code for: Towards Flexible Building Energy Surrogate Models: A Novel Feature Representation Method", https://doi.org/10.18419/DARUS-6306, DaRUS, V1
This code provides the data preprocessing framework used in the publication "Towards Flexible Building Energy Surrogate Models: A Novel Feature Representation Method", designed to generate flexible, physically meaningful feature representations for surrogate modeling of building energy performance. The framework extracts fixed-length feature vector...
Jun 25, 2026 - EXC IntCDC Research Project 36 'AI as Co-designer'
Elshani, Diellza; Marsillo, Laura; Wortmann, Thomas, 2026, "Semantic Timber Construction and Robotic Fabrication Knowledge Graph Dataset", https://doi.org/10.18419/DARUS-6131, DaRUS, V1, UNF:6:6EGQ+0zxmwTet5HhPVBWlA== [fileUNF]
This dataset accompanies the paper A Semantic Framework for Linking Building Design, Material Data, and Robotic Fabrication in Timber Construction. The dataset supports the reproducibility of a semantic framework that links building geometry, timber material properties, and robotic fabrication information through an ontology-driven knowledge graph....
EXC IntCDC Research Project 36 'AI as Co-designer'(University of Stuttgart, Max Planck Institute for Intelligent Systems)
EXC IntCDC Research Project 36 'AI as Co-designer' logo
Jun 19, 2026
RP36-1: AI as Co-Designer For Integrative Computational Design And Engineering Harnessing Biological Variability And Pre-Use Features Of Timber
Jun 9, 2026 - Analytic Computing
Seifer, Philipp; Hernández, Daniel; Lämmel, Ralf; Staab, Steffen, 2024, "Code for From Shapes to Shapes", https://doi.org/10.18419/DARUS-3977, DaRUS, V2
From Shapes to Shapes (s2s): a tool and library for inferring shape constraints that validate the result graphs of graph-transformation queries. Version 1.0 accompanies "From Shapes to Shapes: Inferring SHACL Shapes for Results of SPARQL CONSTRUCT Queries" (WWW 2024, doi:10.1145/3589334.3645550); it archives the v1.0.0 source tree. Version 2.0 acco...
May 26, 2026 - EXC IntCDC Research Project 12 'Computational Co-Design Framework for Fibre Composite Building Systems'
Grünvogel, Nicolai Hans Pascal; Mindermann, Pascal; Baruah, Angshuman C.; Sychterz, Ann; Bischoff, Manfred, 2026, "Replication Data for: Methodological Positioning of Fiberoptic Strain Sensors in Coreless Filament-Wound Lattice Composite Structures", https://doi.org/10.18419/DARUS-6107, DaRUS, V1, UNF:6:ma4kEP4vKnlM8QckneR4QQ== [fileUNF]
This data set contains: 7 input files to generate the mechanical model based on the direct stiffness method of the specific structures, described in detail in the related publication. 2 Excel files for the color-coding of the 4 sample structures for post-processing of the different schemes (qualitative/quantitative), described in detail in the rela...
Apr 20, 2026 - EXC IntCDC Associated Project 47 'Optimisation and Machine Learning for Climate-Friendly Design'
Renner, Markus; Zorn, Max Benjamin; Dai, Anni; Wortmann, Thomas, 2026, "Supplemental Materials for: Evaluating Interactive Visualizations of Multi-Objective Optimization Results for Architectural Design Decision-Making", https://doi.org/10.18419/DARUS-5694, DaRUS, V1
This software provides an interactive visualization framework designed to support architects and engineers in exploring and making decisions from high-dimensional data generated by multi-objective optimization (MOO) in the Architecture, Engineering, and Construction (AEC) industry. The framework integrates multiple coordinated views (MCVs), includi...
Apr 20, 2026 - EXC IntCDC Research Project 26 'AI-supported Collaborative Control and Trajectory Generation of Mobile Manipulators for Indoor Construction Tasks'
Hierholz, Alice; Röhm, Emil; Gienger, Andreas; Sawodny, Oliver, 2026, "Replication Data for: Distributed Trajectory Generation based on ADMM for Collaborative Workpiece Transport with Mobile Robots", https://doi.org/10.18419/DARUS-5722, DaRUS, V1
MATLAB code to replicate the results of the paper titled "Distributed Trajectory Generation based on ADMM for Collaborative Workpiece Transport with Mobile Robots". Tested with MATLAB23a. Requirements: CasADi Version 3.7.2 (Installation guide: https://web.casadi.org/get/) Inputs: Scenario description (number of cases, beam length, number of obstacl...
Apr 15, 2026 - EXC IntCDC Research Project 14 'Extension of the Cyber-Physical Prefabrication Platform'
Hildebrandt, Harrison; Fischer, Oliver; Zechmeister, Christoph; Menges, Achim, 2026, "Replication Data for: Fibre Fabrication of Hybrid Flax Pavilion", https://doi.org/10.18419/DARUS-5841, DaRUS, V1
This dataset contains fabrication data for the production of fibre-composite building components for the Hybrid Flax Pavilion . It includes: LFW_Sim Workflow Description.docx: A description and the pseudocode of the tool (LFW_Sim), which was used to generate tool paths and data exchange files (DataFile) for use on a 4-axis lathe-type filament windi...
Mar 20, 2026 - EXC IntCDC Associated Project 42 'Universal Timber Slab'
Zorn, Max Benjamin; Wortmann, Thomas, 2026, "Universal Timber Slab: Disciplinary Surrogate Models", https://doi.org/10.18419/DARUS-5801, DaRUS, V1
This dataset contains 9 trained surrogate models across all four disciplines predicting the performance of UTS bay elements and a demo Python script. Model Artifacts Each surrogate is saved as a .joblib file which stores: { 'model': <trained sklearn model>, # Trained model 'scaler': <StandardScaler or None>, # Feature scaler 'f...
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