AiDAPT
AI for a sustainable and resilient built environment
Empowering decision-making processes of architects and engineers through AI, across different scales and life-cycle design phases of the built environment, is a key lever for the necessary sustainability transitions in the age of data and digitalization.
At AiDAPT, computer vision, data science, and decision optimization methods come together through the development of deep learning, reinforcement learning, and uncertainty quantification frameworks that help us analyze and synthesize decisions for architectural and structural systems. This entails operation and reasoning in the complex spaces created by the confluence of diverse imagery data, noisy sensory measurements, virtual structural simulators, and uncertain numerical models. Application themes of interest range from automatic recognition of architectural drawings in large databases and generative design recommendations (initial design phase) to predictive intervention planning for life extension, structural risk mitigation, and multi-agent optimization of built systems (life-cycle optimization phase).
Bridging fundamental and applied AI, AiDAPT aims at creating new scientific paradigms towards a more reliable and sustainable built environment.
The AiDAPT Lab is part of the TU Delft AI Labs programme.
The team relies on constructive feedback to achieve informed decisions that reflect the multi-dimensionality of the built environment. Simulation outcomes are being evaluated in reference to their real-world applications so that the necessary parameters are always being considered.
Most of the bridges we use to commute on a daily basis are older than 50 years old. This means extensive repairs to maintain safety, and this means costs. Why don’t we allow AI to take care of this planning?
Reflecting on the results of new computational pipelines in design. The new approaches enable enhanced decision-making for a more sustainable and resilient built environment.
The team
Directors
Charalampos Andriotis
Faculty of A+BE
Lab director
Seyran Khademi
Faculty of A+BE
Lab director
PhD students
Postdocs
Pablo Morato Dominguez
Postdoc
Education
Courses
2023/2024
-
Architectural Research and Design Seminar | AR2AA010
2022/2023
- Computational Intelligence for Integrated Design | AR0202
- Fundamentals of Artificial Intelligence Programme | IFEEMCS520100
- 1:1 Interactive Architecture Prototypes Workshop (2021/22 Q3) | AR0122
- Interactive Architecture Studio | AR2AA016
2021/2022
-
CORE - Computational Structural Analysis | AR3B012
Master Projects
Ongoing
- End-to-end Structural Reasoning from Floor Plan Images, Seyran Khademi, Jeroen Hofland (2023/2024)
- Solar Irradiance Prediction on 3D Urban Models, Seyran Khademi, Job de Vogel (2023/2024)
- IAmHome, Seyran Khademi, Lena Balakina (2023/2024)
- Development of a Three-Dimensional Topology Optimization Algorithm for Mass-Optimized Cast Glass Components, Charalampos Andriotis, Eva Schoenmaker (2022/2023)
- Data-efficient representation learning for topological image data, Seyran Khademi, Emanuel Kuhn (2022/2023)
- BMI for robot control using SNN, Cosimo Della santina, Costanza Pistone (2022/2023)
Finished
- Multi-objective Deep Reinforcement Learning for predictive maintenance of road networks, Charalampos Andriotis, Konstantinos Krachtopoulos (2022/2023)
- Deep Generative Design: A Deep Learning Framework for Optimized Spatial Truss Structures with Stock Constraints, Charalampos Andriotis, Amy Sterrenberg (2022/2023)
- Quantification of thermal resilience in buildings: Evaluation of Building Envelope Performance and Operational Parameters, Charalampos Andriotis, Nathanail Tzoutzidis (2022/2023)
- Incorporating climate change in I&M planning for engineering structures - A Deep Reinforcement Learning Framework, Charalampos Andriotis, Sammie Knopert (2022/2023)
- Object detection for illustrated imagery, Seyran Khademi, Haoran Wang (2022/2023)
- Machine Learning-Assisted Analysis of Energy Consumption Profiles and Efficiency in Uilenstede Campus Buildings, Charalampos Andriotis, Yu Tung (2022/2023)
- 3D Generative Adversarial Networks to Autonomously Generate Building Geometry, Charalampos Andriotis, Lisa-Marie Muller (2022/2023)
- Visual comfort l(AI)outs, Fatemeh Mostafavi, Lotte Kat (2022/2023)
- Architectural Pipeline, Seyran Khademi, Tarique Ali (2022/2023)
- Digital Ornaments, Seyran Khademi, Paulina Panus (2022/2023)
- Optimal maintenance of deteriorating systems integrating deep reinforcement learning and Bayesian inference, Charalampos Andriotis, Christos Lathourakis (2021/2022)
- Deep Generative Designs: A Deep Learning Framework for Optimized Shell Structures, Charalampos Andriotis, Stella Pavlidou (2021/2022)
- Machine learning-based assessment tool for predicting daylight and visual comfort, Charalampos Andriotis, Maryam Aboueimehrizi (2021/2022)
- Just Glass: Development of a Topology Optimization Algorithm for a Mass-Optimized Cast Glass Component., Charalampos Andriotis, Anna Maria Koniari (2021/2022)
- Integrated bio-inspired Design by AI: Using cell structure patterns to train an AI model to explore topology design ideas, Charalampos Andriotis, Namrata Baruah (2021/2022)
