Shapeshift is a machine learning experiment that aims to optimize the construction of earthquake-resistant buildings in third world countries.
The approach uses Fourier analysis on earthquake waveforms in three dimensions to model how seismic frequencies decay from its epicentre. The goal was to identify the multi-dimensional ground movements a building should be optimized for, maximizing its earthquake-resistance in a geographical area at the lowest cost of construction. Using LSTMs and Attention Mechanisms to evaluate risk in geographical regions, we can then construct MLPs to model the seismic frequencies and identify the expected ground movements in those areas.
My work was accelerated by the RippleX Fellowship & RBCx, under the guidance of Dominic Lau & advisors, alongside various micro-grants. I spent my 2023 summer identifying and researching ways to better construct buildings. Recently, I decided to improve previous work, as well as train the models on larger datasets.
