A hybrid data analytics approach for high-performance concrete compressive strength prediction

Serhat Simsek, Mehmet Gumus, Mohamed Khalafalla, Tahir Bachar Issa

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Contrary to the popular belief cited in the literature, the proposed data analytics technique shows that multiple linear regression (MLR) can achieve as high a predictive power as some of the black box models when the necessary interventions are implemented pertaining to the regression diagnostic. Such an MLR model can be utilised to design an optimal concrete mix, as it provides the explicit and accurate relationships between the HPC components and the expected compressive strength. Moreover, the proposed study offers a decision support tool incorporating the Extreme Gradient Boosting (XGB) model to bridge the gap between black-box models and practitioners. The tool can be used to make faster, more data-driven, and accurate managerial decisions without having any expertise in the required fields, which would reduce a substantial amount of time, cost, and effort spent on measurement procedures of the compressive strength of HPC.

Original languageEnglish
Pages (from-to)158-168
Number of pages11
JournalJournal of Business Analytics
Volume3
Issue number2
DOIs
StatePublished - 2020

Keywords

  • decision support tool
  • high-performance concrete
  • regression diagnostic
  • sensitivity analysis
  • Statistical and Machine Learning

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