Treatment prediction using machine learning techniques. Sean Rainsford, Fulton Hogan


This presentation will describe efforts to utilise machine learning techniques, coupled with high speed condition data to construct a prediction model for future treatments.
The approach adopted has used treatment assignment outcomes from previous field inspections and detailed investigations. These assigned treatments were used as a labelled training set for the machine learning algorithm to test.
Several machine learning techniques are being explored, such as Nearest Neighbour (kNN), Decision Tree and Naïve Bayes models.
At this early stage of development, the Naïve Bayes model is providing the most consistent results.
The work is being carried out within the JunoViewer framework, and will be incorporated into the software after testing and calibration of the findings.

Sean Rainsford works for Fulton Hogan as the Technical Asset Manager within the National Asset Management support team. Sean has been involved with data and pavement modelling for over 15 years, Sean was a part of the implementation of the dTIMS software into NZ. Sean’s passion is data and applicability to the real world. After driving many of the roads of New Zealand for over 20 years, and seeing the results of many forecasting outcomes, Sean has learnt that data is always the key!