Machine learning for durability and service-life assessment of reinforced concrete structures: Recent advances and future directions

Woubishet Taffese, Esko Sistonen

Research output: Contribution to journalArticleScientificpeer-review

96 Citations (Scopus)

Abstract

Accurate service-life prediction of structures is vital for taking appropriate measures in a time- and cost-effective manner. However, the conventional prediction models rely on simplified assumptions, leading to inaccurate estimations. The paper reviews the capability of machine learning in addressing the limitations of classical prediction models. This is due to its ability to capture the complex physical and chemical process of the deterioration mechanism. The paper also presents previous researches that proposed the applicability of machine learning in assisting durability assessment of reinforced concrete structures. The advantages of employing machine learning
for durability and service-life assessment of reinforced concrete structures are also discussed in detail. The growing trend of collecting more and more in-service data usingwireless sensors facilitates the use of machine learning
for durability and service-life assessment. The paper concludes by recommending the future directions based on examination of recent advances and current practices in this specific area
Original languageEnglish
Pages (from-to)1-14
JournalAutomation in Construction
Volume77
DOIs
Publication statusPublished - 2017
MoE publication typeA1 Journal article-refereed

Keywords

  • reinforced concrete
  • chloride
  • carbonation
  • modelling
  • machine learning
  • service life
  • durability
  • corrosion

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