Computational Intelligence

> Overview

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Computational Intelligence encompasses theory and application of computational methods, techniques and tools that have the ability to learn based on given datasets, models and tasks. It includes AI comprising machine learning, bringing together concepts from probability and statistics to programming and optimisation. It is increasingly applied in the building sector, both to help understand the current status of built environment and to make informed (design) decisions based on predicted future responses. It mines data and translates them into actionable information. It harnesses and helps understanding information to turn it into applicable knowledge. This course will focus especially on the potential of Computational Intelligence for Integral Design in architecture and engineering, intended as a process of integration across disciplines.

In this course you will learn about the current state-of-the-art of Computational Intelligence applied to architectural design and engineering, and about the theory and fundamental knowledge required to understand how to critically use (and eventually develop) your own Computational Intelligence tools. Topics of optimisation, probabilistic analysis, and machine learning will be covered, from distribution fitting and sampling, to regression, neural networks, and evolutionary algorithms, among others. You will also experience a design process where you will apply such techniques to a small-scale project, developing your design process with Computational Intelligence methods and tools.

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> Study material

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A basic level of programming knowledge and, particularly, Python is anticipated at the start of the course. Related content can be found in:

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> Basic Python knowledge

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• https://pythonnumericalmethods.berkeley.edu/notebooks/Index.html

      (4 first chapters)

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> Advanced Python knowledge

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• Grasshopper to CSV using a Support Vector Machines Example (Sebastian Stripp)

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Additional literature sources that provide an indication of relevant general content are:

- Wortmann, T., 2018. Efficient, Visual, and Interactive Architectural Design Optimization with Model-based Methods
- Wortmann, T., Cichocka, J. and Waibel, C., 2022. Simulation-based Optimization in Architecture and Building Engineering - Results from an International User Survey in Practice and Research. Energy and Buildings, p.111863.
- Ekici, B., Turkcan, O.F., Turrin, M., Sariyildiz, I.S. and Tasgetiren, M.F., 2022. Optimising High-Rise Buildings for Self-Sufficiency in Energy Consumption and Food Production Using Artificial Intelligence: Case of Europoint Complex in Rotterdam. Energies, 15(2), p.660.
- Pan, W., Sun, Y., Turrin, M., Louter, C. and Sariyildiz, S., 2020. Design exploration of quantitative performance and geometry typology for indoor arena based on self-organizing map and multi-layered perceptron neural network. Automation in Construction, 114, p.103163.
- Andriotis, C., 2019. Data driven decision making under uncertainty for intelligent life-cycle control of the built environment.

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> Other material / Examples

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 - Integrated bio-inspired design by AI | Namrata Baruah

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- Deep Generative Design: A Deep Learning Framework for Optimized Spatial Truss Structures with Stock Constraints | Amy Sterrenberg

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- Deep Generative Designs: A Deep Learning Framework for Optimized Shell Structures | Stella Pavlidou

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- ClimAIte Control  | Sebastian Stripp

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- Visual Analytics for Generative Design Exploration: An interactive 3D data environment for a computational design system facilitating the performance-driven design process of a nearly Zero-Energy sports hall | Jamal van Kastel

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- Automated rooftop solar panel detection through Convolutional Neural Networks | Simon Pena Pereira

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- Crosswalk detection for the outdoor navigation of people with visual impairment | Odyssefs Karatzaferis 

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> Course Division

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The students will be acquainted with and understand the state-of-the-art through lectures and self-study. Theory and basic application of methods, techniques and tools will be introduced through lectures, practical workshops and self-study. Specific material related to each workshop can be found in the following pages.