Case-based reasoning approach to estimating the strength of sustainable concrete

Choongwan Koo, Ruoyu Jin, Bo Li, Seunghyun Cha, Dariusz Wanatowski

Research output: Journal article publicationJournal articleAcademic researchpeer-review

3 Citations (Scopus)

Abstract

Continuing from previous studies of sustainable concrete containing environmentally friendly materials and existing modeling approach to predicting concrete properties, this study developed an estimation methodology to predicting the strength of sustainable concrete using an advanced case-based reasoning approach. It was conducted in two steps: (i) establishment of a case database and (ii) development of an advanced case-based reasoning model. Through the experimental studies, a total of 144 observations for concrete compressive strength and tensile strength were established to develop the estimation model. As a result, the prediction accuracy of the A-CBR model (i.e., 95.214% for compressive strength and 92.448% for tensile strength) performed superior to other conventional methodologies (e.g., basic case-based reasoning and artificial neural network models). The developed methodology provides an alternative approach in predicting concrete properties and could be further extended to the future research area in durability of sustainable concrete.
Original languageEnglish
Pages (from-to)645-654
Number of pages10
JournalComputers and Concrete
Volume20
Issue number6
DOIs
Publication statusPublished - 1 Dec 2017

Keywords

  • Advanced case-based reasoning
  • Concrete mixture design
  • Concrete strength prediction
  • Environmentally friendly concrete materials
  • Optimization process
  • Sustainable concrete

ASJC Scopus subject areas

  • Computational Mechanics

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