Data-driven machine learning approach for predicting volumetric moisture content of concrete using resistance sensor measurements

Karthick Thiyagarajan, Sarath Kodagoda, Nalika Ulapane

Research output: Chapter in Book / Conference PaperConference Paperpeer-review

26 Citations (Scopus)

Abstract

In sewerage industry, hydrogen sulphide induced corrosion of reinforced concretes is a global problem. To achieve a comprehensive knowledge of the propagation of concrete corrosion, it is vital to monitor the critical factors such as moisture. In this context, this paper investigates the use of resistance measuring and processing for estimating the concrete moisture content. The behavior of concrete moisture with resistance and surface temperature are studied and the effects of pH concentration on concrete are analyzed. Gaussian Process regression modeling is carried out to predict volumetric moisture content of concrete, where the results from experimental data are used to train the prediction model.
Original languageEnglish
Title of host publicationProceedings of the 2016 IEEE 11th Conference on Industrial Electronics and Applications (ICIEA), 5 - 7 June 2016, Hefei, China
Place of PublicationU.S.
PublisherIEEE
Pages1288-1293
Number of pages6
ISBN (Electronic)9781509026050
DOIs
Publication statusPublished - Jun 2016
Externally publishedYes
EventIEEE Conference on Industrial Electronics and Applications - Hefei, China
Duration: 5 Jun 20167 Jun 2016
Conference number: 11th

Conference

ConferenceIEEE Conference on Industrial Electronics and Applications
Abbreviated titleICIEA
Country/TerritoryChina
CityHefei
Period5/06/167/06/16

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