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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1 to 10 of 119 Results
Jan 22, 2026 - EXC IntCDC Research Project 21 'Modular Data Architecture for Preparation, Annotation and Exchange for Conceptual Design'
Skoury, Lior; Wortmann, Thomas, 2026, "Twico Control Engine", https://doi.org/10.18419/DARUS-5649, DaRUS, V1
Twico Control Engine Twico Control Engine Twico Control is a Flask-based orchestration engine for digital materialisation / digital twin workflows. It coordinates virtual actors (software representations) and their paired physical actors (robots, tools, sensors), executes tasks in a controlled order, forward information to external services, and op...
Dec 17, 2025 - EXC IntCDC Research Project 1 'Functionally Graded Concrete Building System – Design, Optimisation, Digital Production and Reuse'
Teichmann, Alexander, 2025, "Replication Data for: Learning from Mixing Power Curves: An AI-based Approach for Online Assessment of Fresh Concrete Consistency", https://doi.org/10.18419/DARUS-4780, DaRUS, V2, UNF:6:Fx8JPE3/7Uwt7VZOwG2dfQ== [fileUNF]
Overview This dataset contains a synthetic benchmark for studying data-driven assessment of fresh concrete consistency from mixer power consumption time series ("mixing curves"). It was created in the context of the manuscript Learning from Mixing Power Curves: An AI-based Approach for Online Assessment of Fresh Concrete Consistency (submitted to t...
Dec 16, 2025 - EXC IntCDC Associated Project 58 'NeuralWood'
Akbar, Zuardin; Gambarelli, Serena; Wortmann, Thomas, 2025, "Thin Maple Bilayer Dataset", https://doi.org/10.18419/DARUS-5529, DaRUS, V1, UNF:6:USte3nYdxTofohjUbu3ZRw== [fileUNF]
This dataset contains measured and derived features of 64 small-scale mapple wood bilayer samples. A wood bilayer consists of two bonded layers with differing material or anatomical properties. When exposed to changes in moisture, the layers expand or contract at different rates, producing curvature. This makes bilayers a fundamental system for stu...
Nov 24, 2025 - EXC IntCDC Research Project 12 'Computational Co-Design Framework for Fibre Composite Building Systems'
Mindermann, Pascal; Gil Pérez, Marta; Lee, Hyosang; Gresser, Götz Theodor; Knippers, Jan, 2025, "Fiber-optic Strain Sensor Data Collected from Loop Specimens under Various Loading Conditions", https://doi.org/10.18419/DARUS-5448, DaRUS, V1
This dataset includes strain and cross-sectional data from two CFRP loop specimens fabricated using coreless filament winding (CFW). Specimen 1 features both anchors positioned in the same plane, resulting in a “straight” configuration, while specimen 2 has one anchor elevated, creating an “angled” configuration. Both specimens contain fiber-optic...
Nov 19, 2025 - EXC IntCDC Research Project 20 'Knowledge Representation for Multi-Disciplinary Co-Design'
Elshani, Diellza; Hernández, Daniel; Nakhaee, Ali; Arrascue Ayala, Victor Anthony; Staab, Steffen; Wortmann, Thomas, 2025, "Dataset for geof3D: 3D SPARQL Geometry Functions, Test Artefacts, Queries, and Evaluation Results", https://doi.org/10.18419/DARUS-5535, DaRUS, V1, UNF:6:bib2aj4ariTAlxIRIW8MnA== [fileUNF]
This dataset accompanies the paper “geof3D: SPARQL Geometrical Functions for Co-Designing Buildings” and contains all resources required to reproduce the experiments, evaluations, and use cases presented in the work. It includes the full set of SPARQL extension functions used for 3D geometric operations, a collection of 3D geometries in Well-Known...
EXC IntCDC Associated Project 58 'NeuralWood'(University of Stuttgart, Max Planck Institute for Intelligent Systems)
EXC IntCDC Associated Project 58 'NeuralWood' logo
Oct 20, 2025
AP58: NeuralWood: Neural Networks for the Prediction and Utilization of Natural Material Variations in Timber Construction
EXC IntCDC Associated Project 43 'RAW – Computation for a New Age of Resource Aware Architecture'(University of Stuttgart, Max Planck Institute for Intelligent Systems)
EXC IntCDC Associated Project 43 'RAW – Computation for a New Age of Resource Aware Architecture' logo
Oct 20, 2025
AP43: RAW – Computation for a New Age of Resource Aware Architecture: Waste-sourced and fast-growing bio-based materials
Oct 16, 2025 - EXC IntCDC Research Project 12 'Computational Co-Design Framework for Fibre Composite Building Systems'
Mindermann, Pascal; Ravic, Jelena, 2025, "Microscopic Image Dataset of Coated Carbon Fiber/Epoxy Composites", https://doi.org/10.18419/DARUS-5391, DaRUS, V1
This dataset includes microscopic image data of cross-sectional cuts prepared from fiber-composite samples composed of carbon fiber and epoxy matrix, coated with a polymer. The fiber composite did not undergo compaction during curing of the matrix, and the polymer coating was applied after curing. The cuts were made orthogonal to the fiber orientat...
Oct 14, 2025 - EXC IntCDC Associated Project 42 'Universal Timber Slab'
Alvarez, Martin; Wagner, Hans Jakob; Stark, Tim; Menges, Achim; Prandini, Renan; Neubauer, Gregor; Knippers, Jan; Müller, Theresa; Panik, Simon; Müller, Matthias; Leistner, Philip, 2025, "Universal Timber Slab: Simulated Multidisciplinary Performance Data for UTS Solid Timber Slab in Triangular Slab Bays at 1m Resolution", https://doi.org/10.18419/DARUS-5369, DaRUS, V1
Overview This dataset includes preliminary results for Computational Design, Structural Design, Acoustics + Building Physics, and Life Cycle Analysis in relation to the default benchmark defined in the Slab Building Blocks dataset, "1. Triangular Slabs every 1.0m – Default". The slabs included were computed through various disciplinary layers, capt...
Oct 2, 2025 - EXC IntCDC Research Project 1 'Functionally Graded Concrete Building System – Design, Optimisation, Digital Production and Reuse'
Teichmann, Alexander, 2025, "Replication Data for: Effects of a Two-Stage Mixing Process on the Characteristics of Concrete: Part II - Fresh Concrete Rheology and Measurement Protocols", https://doi.org/10.18419/DARUS-5351, DaRUS, V1, UNF:6:fqyI9xwkg2mlLJ+/urjFVg== [fileUNF]
Addendum to the corresponding paper. The data was originally used to explore the effects of a two-stage mixing process on the properties of fresh concrete. The data set represents experimental measurements taken with a modified ICAR Rheometer on three different concrete proportions with two different mixing configurations. The data includes yield s...
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