Analytical Model and Data-driven Approach for Concrete Moisture Prediction

Karthick Thiyagarajan, Sarath Kodagoda

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

11 Citations (Scopus)

Abstract

The advent of smart sensing technologies has opened up new avenues for addressing the billion dollar problem in the wastewater industry of H2S corrosion in concrete sewer pipes, where there is a growing interest in monitoring the environmental properties that govern the rate of corrosion. In this context, this paper proposes a methodology to predict the moisture content of concretes through data-driven approach by using Gaussian Process Regression modeling. The experimental program in this study practices measurements during wetting and drying phases of concrete. The obtained moisture data is used to train the prediction model against interpreted electrical resistivity data. The data of analytical model formulated from Archie?s Law is then analyzed with experimental and Gaussian Process prediction data.
Original languageEnglish
Title of host publication33rd International Symposium on Automation and Robotics in Construction and Mining (ISARC 2016)
PublisherThe International Association for Automation and Robotics in Construction
Pages298-306
Number of pages9
ISBN (Electronic)9781510829923
DOIs
Publication statusPublished - Jul 2016
Externally publishedYes
EventInternational Symposium on Automation and Robotics in Construction and Mining - Auburn, United States
Duration: 18 Jul 201621 Jul 2016
Conference number: 33rd

Conference

ConferenceInternational Symposium on Automation and Robotics in Construction and Mining
Abbreviated titleISARC
Country/TerritoryUnited States
CityAuburn
Period18/07/1621/07/16

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