A novel artificial intelligence-aided system to mine historical high-performance concrete data for optimizing mixture design

Min Yuan Cheng, Minh Tu Cao*, Ngoc Mai Dao-Thi

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

Designing proper HPC mixtures is an onerous task, as many different chemical substances are used to produce HPC of differing specifications and performance characteristics. In this study, a novel, artificial-intelligence-aided system is developed to effectively minimize the cost of producing HPC of variable compressive strength, workability, and durability requirements. In the first phase, a symbiotic organism search-optimized least squares support vector machine (SOS-LSSVM) is developed using historical data to predict HPC properties to verify that these properties conform with requirements. In experimental testing conducted for this study, the SOS-LSSVRM achieved MAPE values of 17.44% 16.31%, and 22.70%, respectively, for predicting compressive strength, workability, and durability. In the second phase, symbiotic organism search (SOS) is used to identify the lowest-cost HPC mixture that attains the specified mechanical requirements. The results demonstrate that the proposed artificial intelligence-aided system is effective in guiding the economical design of HPC mixtures that comply with ACI and DMDA standards.

Original languageEnglish
Article number118605
JournalExpert Systems with Applications
Volume212
DOIs
StatePublished - Feb 2023

Keywords

  • Artificial intelligence
  • Compressive strength
  • High-performance concrete
  • Least squares support vector machine
  • Symbiotic organism search
  • Workability of concrete

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