Forecasting the Performance of Tunnel Boring Machines using Gaussian Processes
There is a vast network of buried infrastructure and services in the UK comprising water, sewer, gas and electricity which extends to well over 1.5 million km. Microtunnelling is an increasingly popular means of constructing these underground utilities compared to traditional ‘open cut’. The proliferation of data collected by tunnel boring machines poses a significant opportunity to present site engineers with meaningful information upon which to make informed and timely decisions. This talk will explore the potential for Gaussian Processes (GPs) to forecast the performance of a tunnel boring machine during microtunnelling and form part of an early warning system to avoid adverse responses on site. The GP forecasts will also be appraised through comparisons to existing empirical models currently used by industry as well as monitored data from two live UK construction sites.
Date: 14 June 2019, 10:30 (Friday, 7th week, Trinity 2019)
Venue: Wolfson College, Linton Road OX2 6UD
Venue Details: Florey Room
Speaker: Dr. Brian Sheil (University of Oxford)
Organisers: Stephen Suryasentana (University of Oxford), Yaling Hsiao (University of Oxford )
Organiser contact email address:
Part of: 1st Oxford Cross-disciplinary Applications of Machine Learning Workshop 2019
Topics: Machine learning
Booking required?: Not required
Audience: Public
Editor: Yaling Hsiao