Choo Ee Pin Portfolio 22-24

Portfolio 2024 Selected works

Choo Ee Pin Research Assistant, CITA

Master of Arts (MA) in architecture

BUILDINGS OF THESEUS A cyclical assembly and disassembly of non-standard timber structures with timber joints 2021- 2022 Choo Ee Pin Master thesis Denmark : KADK : CITA

This research explores a novel computational design method for the multiple reconfigurations of timber structures and components to extend its material life cycle.

Objectives: (1) explore the use of computer vision to track, evaluate and machine timber elements (2) explore timber joints that are reconfigurable and multi-functional.

Prototype 4. Using computer vision to detect and identify joints and its orientation in real time.

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[1]: Prototype 2. [2,3,4]: Prototype 1. [5]Prototype 4. Markings that informs dis/assembly of parts. [6] Computer vision identifying key features and matching it on another image of the same part.

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[1]: Prototype 3. Old parts are re-machined and combined to form more complex joints [2]: Render. [3,4]: Joints hold information of where it came from. [5]: Complexity and modularity of joints allow these parts to stay relevant, extending its potential life cycle.

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[6, 7]: Rendering. As the site changes, so does the building by reconfiguring, re-machining existing parts. By allowing elements to evolve, we can change their function, extending its life span. [8] Prototype 1.

Timber structure built with fiber oriented glue laminated timber. Structure was built with timber timber joints that could be disassembled. This structure was used to study the effects of weathering on joints and fiber oriented timber.

[1, 4] : New elements added to the structure using 3D scanning methods to obtain fabrication data of the joint. [2]: Simple assembly with only timber connections. [3,5] Open ended joints to anticipate structure extension.

Planning and cutting of timber boards for lamella used in the Fiber oriented glue laminated timber.

Planning fiber orientation within the GLT based on simulations and structural requirements.

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Feature detection using Computer vision and machine learning. Segmentation was performed to identify regions of interest. These areas are masked and send to a model to be identified, values shown above are the machines' confidence in that feature.