A multivariate clustering approach for infrastructure failure predictions

Infrastructure failures have severe consequences which often have a negative impact on the society and the economy. In this paper, we propose a machine learning model to assist in risk management to minimise the cost of infrastructure maintenance. Due to the vast volume and complexity of infrastructure datasets, such problem is often computationally expensive to compute. A Bayesian nonparametric approach has been selected for this problem, as it is highly scalable. We propose a two-stage approach to model failures, such as water pipe failures. The first stage uses an Infinite Gamma-Poisson Mixture Model to group water pipes with similar characteristics together based on the number of failures. The second stage uses the groups created in the first stage as an input to the Hierarchical Beta Process (HBP) to rank water pipes based on their probability of failure. The proposed method is applied to a metropolitan water supply network of a major city. The experiment results have shown that the proposed approach is able to adapt to the complexity of tge large multivariate dataset and there is a double-digit improvement from the grouping created by domain experts.