Stochastic carbon dioxide forecasting model for concrete durability applications

B. Habeeb, E. Bastidas-Arteaga*, H. Gervásio, M. Nogal Macho

*Corresponding author for this work

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

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Abstract

Over the Earth’s history, the climate has changed considerably due to natural processes affecting directly the earth. In the last century, these changes have perpetrated global warming. Carbon dioxide is the main trigger for climate change as it represents approximately up to 80% of the total greenhouse gas emissions. Climate change and concrete carbonation accelerate the corrosion process increasing the infrastructure maintenance and repair costs of hundreds of billions of dollars annually. The concrete carbonation process is based on the presence of carbon dioxide and moisture, which lowers the pH value to around 9, in which the protective oxide layer surrounding the reinforcing steel bars is penetrated and corrosion takes place. Predicting the effective retained service life and the need for repairs of the concrete structure subjected to carbonation requires carbon dioxide forecasting in order to increase the lifespan of the bridge. In this paper, short term memory process models were used to analyze a historical carbon dioxide database, and specifically to fill in the missing database values and perform predictions. Various models were used and the accuracy of the models was compared. We found that the proposed Stochastic Markovian Seasonal Autoregressive Integrated Moving Average (MSARIMA) model provides R2 value of 98.8%, accuracy in forecasting value of 89.7% and a variance in the value of the individual errors of 0.12. When compared with the CO2 database values, the proposed MSARIMA model provides a variance value of −0.1 and a coefficient of variation value of −8.0e−4.
Original languageEnglish
Title of host publication18th International Probabilistic Workshop, Guimarães (Portugal)
Subtitle of host publicationIPW 2020
EditorsJosé C. Matos, Paulo B. Lourenço, Daniel V. Oliveira, Jorge Branco, Dirk Proske, Rui A. Silva, Hélder S. Sousa
Place of PublicationCham
PublisherSpringer
Pages753-765
Number of pages13
ISBN (Electronic)9783030736163
ISBN (Print)9783030736156
DOIs
Publication statusPublished - 2021
Event18th International Probabilistic Workshop, IPW 2020 - Virtual, Online
Duration: 12 May 202114 May 2021

Publication series

NameLecture Notes in Civil Engineering
PublisherSpringer
Volume153
ISSN (Print)2366-2557
ISSN (Electronic)2366-2565

Conference

Conference18th International Probabilistic Workshop, IPW 2020
CityVirtual, Online
Period12/05/2114/05/21

Bibliographical note

Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Keywords

  • Climate change
  • Seasonal Stochastic Markovian Autoregressive Integrated Moving Average model
  • Concrete carbonation
  • Carbon dioxide forecasting
  • Infrastructure reliability

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