There’s been a great buzz around Artificial Intelligence (AI) the past couple of years that penetrated numerous domains. Construction couldn’t stay unaffected; the need to increase efficiency and decrease risk in this trillion-dollar industry is perceived as a good fit for AI-based solutions. Inspired by recent advances in Machine Learning, Computer Vision, Natural Language Processing and other fields, academic and industrial organizations are looking into AI-fying processes such as design optimization, scheduling, documentation, safety and construction project monitoring. However, as with every new trend, there exists misconception around abilities, usage and limitations. AI technology can without doubt empower several processes in the Construction domain; what it can’t do is perform magic.
In this course we will explore these processes and corresponding AI directions to cultivate a critical point of view of the technologies, feasibility, effort and impact. Students will be guided to identify and address the perceptional gap between what AI in AEC is doing, what it can do and what is advertised doing. They will be engaged in a threefold manner via readings, critical discussions and hands-on course-long projects. In the latter they are asked to evaluate AEC processes and structure AI solutions that show an in-depth understanding of the underlying phenomena and models that drive these processes. Lectures by AI and AEC experts will be also help in acquiring this understanding. Our ultimate goal is for students to start building a toolkit toward driving innovation in their career. We encourage students of all backgrounds to enroll (no AI/Construction background necessary). Artificial Intelligence Applications in the AEC Industry (CEE329) is offered as a 2-unit course for up to 12 graduate students.
Check out our 2018 class here
Through this course, students will:
Build knowledge on the potential of using AI to empower construction.
Develop critical thinking to evaluate AI in construction technologies, in terms of technology, feasibility, effort and impact.
Identify and address the perceptional gap between what AI in AEC is doing, what it can do and what is advertised of doing.
Start building a toolkit toward driving innovation in their career.
Subscribe to the class Google Calendar
This session is an introduction to the class. The speakers will offer their perspective on AI from the theoretical and technological perspective and set the pace for the rest of the discussions.
In this class students will present details and opinions on the 1st round of AI in AEC readings, followed by an in-depth discussion and explanation of them.
1st Assignment due
In this class students will present details and opinions on the 2nd round of AI in other industries readings, followed by an in-depth discussion of them, including their transferrability to AEC.
2nd Assignment due
In this class we will have a more focused look on AI technologies and explain terms and methodologies that came up in the readings.
Project Proposals due
In this class we will discuss and set foundations for the course projects.
Invited AEC experts on the selected course projects will give lectures and engage in discussion with students.
Project check-in due
In this class we will further focus on the projects with groups presenting their progress, followed by whole-class discussion.
Expert lecture followed by in-class discussion.
In this class the students will present their team projects. Discussion and Closing Notes will follow the presentations.
Students will be evaluated based on the following assignments. Late assignments will be marked down 25% per day late, with zero credit if more than two days late. All assignments are required to be submitted except if prior arrangement with the instructors.
Units: 2
Grading Criteria: Letter Grade
Enrollment Cap: 12 students (CEE329)
Readings: There are no textbooks required. Any readings will be posted on Canvas.
Martin Fischer, Y2E2 297, By appointment
Iro Armeni, Gates 128, Mondays, 2-3PM
Attendance is required at all sessions unless there is an excused absence, which requires prior notification and approval from the instructor. Students are not allowed to miss more than one sessions. Regardless of any missed sessions, students are required to submit coursework on time.
Students may submit a regrade request within 3 days after the grades are released, if they think they deserved a better grade on an assignment. The request should briefly summarize why the student think the original grade was unfair. We will reevaluate your assignment as soon as possible, and issue a decision.
Students who may need an academic accommodation based on the impact of a disability must initiate the request with the Office of Accessible Education (OAE). Professional staff will evaluate the request with required documentation, recommend reasonable accommodations, and prepare an Accommodation Letter for faculty dated in the current quarter in which the request is being made. Students should contact the OAE as soon as possible since timely notice is needed to coordinate accommodations. The OAE is located at 563 Salvatierra Walk (phone: 723-1066, URL: http://studentaffairs.stanford.edu/oae).
Students are expected to adhere to Stanford’s Honor Code and are responsible for understanding the University rules regarding academic integrity. In brief, conduct prohibited by the Honor Code includes all forms of academic dishonesty, among them copying from another’s exam, unpermitted collaboration, and representing another’s work as one’s own. To view the honor code and receive tips for how to adhere to it, visit: http://studentaffairs.stanford.edu/communitystandards
