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

*此作品的通信作者

研究成果: Article同行評審

9 引文 斯高帕斯(Scopus)

摘要

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.

原文English
文章編號118605
期刊Expert Systems with Applications
212
DOIs
出版狀態Published - 2月 2023

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