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Health Indicators Construction and Remaining Useful Life Estimation for Concrete Structures Using Deep Neural Networks

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Abstract
Remaining useful life (RUL) prognosis is one of the most important techniques in concrete
structure health management. This technique evaluates the concrete structure strength through
determining the advent of failure, which is very helpful to reduce maintenance costs and extend
structure life. Degradation information with the capability of reflecting structure health can be
considered as a principal factor to achieve better prognosis performance. In traditional data-driven
RUL prognosis, there are drawbacks in which features are manually extracted and threshold is
defined to mark the specimen’s breakdown. To overcome these limitations, this paper presents
an innovative SAE-DNN structure capable of automatic health indicator (HI) construction from
raw signals. HI curves constructed by SAE-DNN have much better fitness metrics than HI curves
constructed from statistical parameters such as RMS, Kurtosis, Sknewness, etc. In the next stage, HI
curves constructed from training degradation data are then used to train a long short-term memory
recurrent neural network (LSTM-RNN). The LSTM-RNN is utilized as a RUL predictor since its
special gates allow it to learn long-term dependencies even when the training data is limited. Model
construction, verification, and comparison are performed on experimental reinforced concrete (RC)
beam data. Experimental results indicates that LSTM-RNN generally estimates more accurate RULs
of concrete beams than GRU-RNN and simple RNN with the average prediction error cycles was less
than half compared to those of the simple RNN.
Author(s)
짜 윁웬 뚜언 카이김철홍김종면
Issued Date
2021
Type
Article
Keyword
concrete structuresdeep neural network (DNN)long short-term memory (LSTM)remaining useful life (RUL)stacked autoencoder (SAE)
DOI
10.3390/app11094113
URI
https://oak.ulsan.ac.kr/handle/2021.oak/9143
https://ulsan-primo.hosted.exlibrisgroup.com/primo-explore/fulldisplay?docid=TN_cdi_doaj_primary_oai_doaj_org_article_0e169cac49f442dda9b2f51013750bb9&context=PC&vid=ULSAN&lang=ko_KR&search_scope=default_scope&adaptor=primo_central_multiple_fe&tab=default_tab&query=any,contains,Health%20Indicators%20Construction%20and%20Remaining%20Useful%20Life%20Estimation%20for%20Concrete%20Structures%20Using%20Deep%20Neural%20Networks&offset=0&pcAvailability=true
Publisher
APPLIED SCIENCES-BASEL
Location
스위스
Language
영어
ISSN
2076-3417
Citation Volume
11
Citation Number
9
Citation Start Page
4113
Citation End Page
4113
Appears in Collections:
Engineering > IT Convergence
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