Delft AI Lab Lunch with AiBLE & AiDAPT

About AiBLE Lab

Activating Intelligence in Building Lasting and Liveable Environments

The built environment faces major societal challenges. Planners, architects, and citizens using or living in these spaces may all have different views and priorities – so any strategy for dealing with the challenges involved must incorporate different preferences into the design process. The AiBLE Lab will investigate how AI can be developed and used in complex real-world scenarios. Our aim is to help reach effective, transparent and lasting decisions and agreements. This means incorporating human feedback into the loop, iteratively improving decision-making and driving behaviour changes. We focus on two important built environment challenges: energy transition and circularity. By using AI to augment human intelligence and support negotiation continuously – while adapting to and driving human behaviour – the AiBLE Lab will close the policy-practice gap responsibly and inclusively. We also aim to accelerate the adoption and acceptance of AI.

About AiDAPT Lab

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.