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 123 Results
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...
EXC IntCDC Associated Project 36 'Development of Self-Forming Curved Wood Furniture'(University of Stuttgart, Max Planck Institute for Intelligent Systems)
EXC IntCDC Associated Project 36 'Development of Self-Forming Curved Wood Furniture' logo
Feb 20, 2026
AP36: Material driven computational design and manufacturing for self-forming curved wood furniture
Feb 20, 2026 - EXC IntCDC Associated Project 47 'Optimisation and Machine Learning for Climate-Friendly Design'
Zorn, Max Benjamin; Paule, Robin; Akbar, Zuardin; Wortmann, Thomas, 2026, "Opossum", https://doi.org/10.18419/DARUS-5685, DaRUS, V1
Opossum Opossum is an optimization plug-in for Grasshopper (for Rhino) that implements model-based and evolutionary optimization algorithms for single- and multi-objective problems. The plugin integrates GUI components into Grasshopper to configure runs, visualize results, and persist optimizer state inside Grasshopper documents. This repository co...
EXC IntCDC Associated Project 21 'AI-based Timber Construction Process Planing'(University of Stuttgart, Max Planck Institute for Intelligent Systems)
EXC IntCDC Associated Project 21 'AI-based Timber Construction Process Planing' logo
Feb 12, 2026
AP21: Innovative AI-based sustainable timber construction process planning
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
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