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

PhD students

Postdocs

Education

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