Improved tunneling knowledge through robust machine learning
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Earth Pressure Balance Machines (EPBMs) are essential equipment for excavating and constructing underground tunnels in urban environments with soft ground conditions. As examples, EPBMs are used for subways, underground highways, and water conduits. Our work utilizes data collected from hundreds of sensors on an EPBM to understand which systems affect the EPBM's performance. The ultimate goal is to optimize these systems in future tunneling projects, reducing project costs and construction time. We apply machine learning techniques to two data sets from EPBM excavated tunnels in the Seattle, WA, ...