The past few years a lot of discussion has been sparked on AI in the Architecture, Construction, and Facility Management (ACFM) industry. Despite advancements in this interdisciplinary field, we still have not answered fundamental questions about adopting and adapting AI technology for ACFM. In order to achieve this, we need to be equipped with rudimentary knowledge of how this technology works and what are essential points to consider when applying AI to this specific domain.
In addition, the availability of sensors that collect visual data in commodity hardware (e.g., mobile phone and tablet), is creating an even bigger pressure in identifying ways that new technology can be leveraged to increase efficiency and decrease risk in this trillion-dollar industry. However, cautious and well-thought steps need to be taken in the right direction, in order for such technologies to thrive in an industry that showcases inertia in technological adoption.
The course will unfold as two parallel storylines that intersect in multiple places:
1) The first storyline will introduce fundamentals in computer vision and machine learning technology, as building blocks that one should consider when developing related applications. These blocks will be discussed with respect to latest developments (e.g., deep neural networks), pointing out their impact in the final solution.
2) The second storyline consists of 3 ACFM processes, namely architectural design, construction renovation, and facility management. These processes will serve as application examples of the technological storyline.
In the points of connection students will see the importance of taking into account the application requirements when designing an AI system, as well as their impact on the building blocks. Guest speakers from both the AI and ACFM domains will complement the lectures.
By the end of the course students will develop computational thinking related to visual machine perception applications for the ACFM domain. Specifically, they will:
Gain fundamental understanding on how this technology works and the impact it can have in the ACFM industry.
Identify limitations, pitfalls, and bottlenecks in these applications.
Develop critical thinking on solutions for the above issues.
Acquire hands-on experience in creatively thinking and designing an application.
Use this course as a “stepping-stone” to Machine Learning-intensive courses offered in D-BAUG and D-ARCH.
(Subject to change)
Drawing lines, surfaces, and primitives in visual data
As-is geometric model: From pixels to 3D recostruction
Assignment 1 is due
Making sense of visual data: Segmentation and clustering
1st Project Milestone is due
What is this that I see?: Visual data classification
Toward a "digital-twin": Detection and Semantic Segmentation
Assignment 2 is due
Visual data representation: 3D scene graph, BIM, and more
The machine Designer: Generating new visual data
2nd Project Milestone
Guest Talk, TBD, by Prof. Burcu Akinci, CEE, Carnegie Mellon University
Keeping track of mobile elements in construction sites: Object and people tracking
Assignment 3 is due
Construction worker productivity and safety: Activity recognition
Guest Talk, "Mixed Reality and HoloLens", by Prof. Martin Oswald, CS, University of Amsterdam
Guest Talk, "Human-Robot Collaboration", by Dr. Claudia Pérez D'Arpino, Research Scientist at NVIDIA
Final Project Presentations