ANLY482 AY2017-18T2 Group02 Findings & Insights

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DATA CLEANING

Thesis — Algorithm-driven architectural design and procedural generation of 3D digital environments were the inspiration for the lofty initial goal of this project: to perform, aid, or serve to validate structural engineering design for underground train stations. On the premise that geological features were likely to be primary considerations, we hoped to build a predictive model to accomplish this. However, through further dialogue with the engineers, and examination of the geological data, it’s become clear that much of the structural design is not related to geological features; rather, there are other more significant hidden variables influencing design.

Limiting factors — Soil stratum data from borehole excavation serves as an informing factor to engineers designing retaining and load-bearing walls for stations, but the relationship between strata and design parameters remains obscure as a form of domain knowledge. Further insight into what considerations are made by engineers when looking over geological data is necessary, and additional data on the relationship between boreholes and station design features, i.e. walls, would be necessary to move toward predictive capability.

Reframing goals and the pathways to them — Given the limited predictive capacity of just borehole data, we will instead seek to build better understanding of the geological information through visualization, for the sake of future predictive efforts.