Estimating the Values of Missing Data Related to Infrastructure Condition States Using Their Spatial Correlation
Open access
Date
2023-03Type
- Journal Article
Abstract
Infrastructure managers consistently monitor the condition of their assets to predict their deterioration speed and determine the optimal time to execute preventive interventions. However, despite the recent progress in more frequent and accurate monitoring of assets and storage of the related results, in practice, real-world data often contains errors and discrepancies such as missing data or faulty entries. This problem can happen owing to collection errors during routine inspections or inconsistency of data storage formats in different years. Because the quality of data plays a significant role in the accuracy of deterioration prediction and the resulting intervention programs, it is important to improve condition state predictions by imputing the values of missing information. This paper examines the efficiency of different models that use the spatial correlation of infrastructure assets in predicting the value of missing data. The models include univariate and multivariate Kriging, a hybrid artificial neural network (ANN)-Kriging model, and the bidirectional long short term memory (bi-LSTM) neural network, which can model the data with spatial correlation or a sequential relationship. The results confirm that the condition indicator values can be estimated with reasonably low levels of error Show more
Permanent link
https://doi.org/10.3929/ethz-b-000580329Publication status
publishedExternal links
Journal / series
Journal of Infrastructure SystemsVolume
Pages / Article No.
Publisher
American Society of Civil EngineersSubject
Kriging model; bidirectional long short term memory neural network; artificial neural network-Kriging model; Insufficient or missing dataOrganisational unit
03859 - Adey, Bryan T. / Adey, Bryan T.
02261 - Center for Sustainable Future Mobility / Center for Sustainable Future Mobility
02655 - Netzwerk Stadt u. Landschaft ARCH u BAUG / Network City and Landscape ARCH and BAUG
Funding
769373 - Future proofing strategies FOr RESilient transport networks against Ectreme Events (EC)
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