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A multivariate clustering approach for infrastructure failure predictions
conference contribution
posted on 2023-05-23, 13:10 authored by Luo, S, Chu, VW, Zhou, J, Chen, F, Wong, RK, Huang, WInfrastructure 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.
History
Publication title
Proceedings from the 2017 IEEE 6th International Congress on Big DataPagination
274-281ISBN
9781538619964Department/School
School of Information and Communication TechnologyPublisher
IEEE Computer SocietyPlace of publication
United StatesEvent title
6th International Congress on Big DataEvent Venue
Honolulu, HawaiiDate of Event (Start Date)
2017-06-25Date of Event (End Date)
2017-06-30Rights statement
Copyright 2017 IEEERepository Status
- Restricted