Matches in SemOpenAlex for { <https://semopenalex.org/work/W2756222784> ?p ?o ?g. }
Showing items 1 to 80 of
80
with 100 items per page.
- W2756222784 abstract "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." @default.
- W2756222784 created "2017-09-25" @default.
- W2756222784 creator A5015158455 @default.
- W2756222784 creator A5021022882 @default.
- W2756222784 creator A5032717607 @default.
- W2756222784 creator A5047165067 @default.
- W2756222784 creator A5051364953 @default.
- W2756222784 creator A5062269449 @default.
- W2756222784 date "2017-06-01" @default.
- W2756222784 modified "2023-10-17" @default.
- W2756222784 title "A Multivariate Clustering Approach for Infrastructure Failure Predictions" @default.
- W2756222784 cites W1987532879 @default.
- W2756222784 cites W1996596366 @default.
- W2756222784 cites W2021688467 @default.
- W2756222784 cites W2037536778 @default.
- W2756222784 cites W2039625353 @default.
- W2756222784 cites W2069429561 @default.
- W2756222784 cites W2080972498 @default.
- W2756222784 cites W2106053246 @default.
- W2756222784 cites W2132092465 @default.
- W2756222784 cites W2158266063 @default.
- W2756222784 cites W2279127960 @default.
- W2756222784 cites W2913048013 @default.
- W2756222784 doi "https://doi.org/10.1109/bigdatacongress.2017.42" @default.
- W2756222784 hasPublicationYear "2017" @default.
- W2756222784 type Work @default.
- W2756222784 sameAs 2756222784 @default.
- W2756222784 citedByCount "5" @default.
- W2756222784 countsByYear W27562227842019 @default.
- W2756222784 countsByYear W27562227842020 @default.
- W2756222784 countsByYear W27562227842021 @default.
- W2756222784 countsByYear W27562227842023 @default.
- W2756222784 crossrefType "proceedings-article" @default.
- W2756222784 hasAuthorship W2756222784A5015158455 @default.
- W2756222784 hasAuthorship W2756222784A5021022882 @default.
- W2756222784 hasAuthorship W2756222784A5032717607 @default.
- W2756222784 hasAuthorship W2756222784A5047165067 @default.
- W2756222784 hasAuthorship W2756222784A5051364953 @default.
- W2756222784 hasAuthorship W2756222784A5062269449 @default.
- W2756222784 hasConcept C102366305 @default.
- W2756222784 hasConcept C111919701 @default.
- W2756222784 hasConcept C119857082 @default.
- W2756222784 hasConcept C124101348 @default.
- W2756222784 hasConcept C149782125 @default.
- W2756222784 hasConcept C161584116 @default.
- W2756222784 hasConcept C162324750 @default.
- W2756222784 hasConcept C41008148 @default.
- W2756222784 hasConcept C48044578 @default.
- W2756222784 hasConcept C73555534 @default.
- W2756222784 hasConcept C77088390 @default.
- W2756222784 hasConcept C98045186 @default.
- W2756222784 hasConceptScore W2756222784C102366305 @default.
- W2756222784 hasConceptScore W2756222784C111919701 @default.
- W2756222784 hasConceptScore W2756222784C119857082 @default.
- W2756222784 hasConceptScore W2756222784C124101348 @default.
- W2756222784 hasConceptScore W2756222784C149782125 @default.
- W2756222784 hasConceptScore W2756222784C161584116 @default.
- W2756222784 hasConceptScore W2756222784C162324750 @default.
- W2756222784 hasConceptScore W2756222784C41008148 @default.
- W2756222784 hasConceptScore W2756222784C48044578 @default.
- W2756222784 hasConceptScore W2756222784C73555534 @default.
- W2756222784 hasConceptScore W2756222784C77088390 @default.
- W2756222784 hasConceptScore W2756222784C98045186 @default.
- W2756222784 hasLocation W27562227841 @default.
- W2756222784 hasOpenAccess W2756222784 @default.
- W2756222784 hasPrimaryLocation W27562227841 @default.
- W2756222784 hasRelatedWork W1837630526 @default.
- W2756222784 hasRelatedWork W1979697693 @default.
- W2756222784 hasRelatedWork W2164312800 @default.
- W2756222784 hasRelatedWork W2335589441 @default.
- W2756222784 hasRelatedWork W2529605301 @default.
- W2756222784 hasRelatedWork W2564138607 @default.
- W2756222784 hasRelatedWork W4231665652 @default.
- W2756222784 hasRelatedWork W4237896776 @default.
- W2756222784 hasRelatedWork W4243114048 @default.
- W2756222784 hasRelatedWork W4296826658 @default.
- W2756222784 isParatext "false" @default.
- W2756222784 isRetracted "false" @default.
- W2756222784 magId "2756222784" @default.
- W2756222784 workType "article" @default.