Spatial Computing in Architectural Design

The new generation of architects will be expected to improve the quality and the performance of existing and new buildings in face of new environmental, social and economic challenges. This often requires formulating and solving multi-disciplinary complex design and decision-making problems in a collaborative setting. However, the fundamental question is: “How do we know if our interventions or designs will yield better results?” In other words, can we model, analyse, simulate, and evaluate the functioning of buildings? How can we improve sustainability and quality of buildings in quantifiable ways, and validate our design assumptions? To effectively deal with complex multi-disciplinary problems, computational approaches need to be utilized to automate analysis, synthesis & evaluation procedures required for optimization and systematic decision-making in the design process.  

The 15 ECs minor Spatial Computing offers a set of courses providing the fundamentals of computing in spatial (geometrical, topological, and/or graph theoretical) design and decision-making. This minor consists of a computational design studio and three methodological courses, all of which cover the applied mathematics and computation topics necessary for algorithmic design, modelling, analysis, simulation and evaluation of buildings.

Spatial Computing in Architectural Design deals with formulating and solving complex design problems in architecture. It involves developing computational procedures and models for formulation of design requirements and rules, algorithmic generation of designs, analysis, simulation, and evaluation of building performance using Building Information Models (BIM), for design optimization of complex buildings. By visualizing and processing BIM, user-behaviour and context data using simulation models, it is aimed to predict the effect of design decisions on various performance aspects of buildings such as efficiency of use, safety, security, energy-use, comfort, constructability, material-use, configurational aspects etc. To be able to make better decisions, besides the algoritmic and scientific approach, students make use of advanced data visualization techniques and virtual reality. 

For whom?

  • The minor is primarily intended for students of Architecture and the Built Environment; in addition, it is relevant for students of Technology, Policy & Management (TBM), and Computer Science (EWI), Civil Engineering (CITG), and Industrial Engineering (IO).
  • This minor is suitable for those interested in rational and collaborative approaches to design and decision making as well as mathematical/computational modelling; and it has a mathematics & programming oriented approach, however, prior knowledge is not considered a prerequisite. 
  • For students who are NOT from Delft University of Technology, Leiden University or Erasmus University a separate selection procedure applies. 

What will you learn?

Having followed the minor program, the students will be expected to have learnt:

  • to formulate a Program of Requirements based on the needs of a client
  • to set up a systematic computational design process for achieving high-performance
  • to obtain an overview of the simulation methodologies relevant for architectural design and decision-making
  • to conduct a participatory decision-making process for multi-actor, multi-objective decision problems
  • to systematically design and underpin the decisions made for designing a complex building project; specifically:

    • to obtain an overview on existing computational models and methods in Architectural Design;
    • to perform spatial data visualization using existing software tools or programming;
    • to formulate design problems and draft algorithms for procedural design;
    • to distinguish and identify data modelling, analysis, simulation, evaluation and optimization approaches and methods;
    • to use Building Information Modelling (BIM) to assess the design decisions.
    • to identify spatial decision-making and optimization problems and recollect relevant methods of problem-formulation and problem-solving;
    • to formulate design or decision alternatives procedurally;
    • to show by models or simulations how a design or an intervention is supposed to work;
    • to compare design or decision alternatives according to evaluation criteria;
    • and to scientifically underpin their designs or decisions;
    • to understand the potential benefits of Artificial Intelligence techniques in general for this purpose, and in particular the benefit of automated negotiating agents using a multi-party negotiation protocol

Course overview

The minor is offered in quarter 2 and consists of three methodological courses and an intensive computational design studio where the students work on a real-world design challenge and practice design using what they learn  in the four courses.

  • Briefing & Spatial Management (2 ECs)
  • Computational Design Studio (9 ECs)
  • Computational Simulations (2 ECs)
  • Collaborative Decision Making (2ECs)

All courses include lectures and hands-on [programming] workshops, group-study, and seminars. For the seminars, the experts on the specific topics will be invited to broaden the scope of students. Virtual Reality and Rapid-Prototyping facilities will be openly available for experimentation to all students. 

For course descriptions, please visit the study guide.

Minor code: BK-MI-197 

Language: English

Participating Faculties: Faculty of Architecture and Built Environment and Electrical Engineering, Mathematics and Computer Science

Maximum participants: 40

Education methods: research-based learning in design studios, lectures combined with hands-on workshops, working on real world problems, seminars, and study groups.

Contact Sevil Sariyildiz

Dr. Pirouz Nourian