TY - JOUR
T1 - A novel artificial intelligence-aided system to mine historical high-performance concrete data for optimizing mixture design
AU - Cheng, Min Yuan
AU - Cao, Minh Tu
AU - Dao-Thi, Ngoc Mai
N1 - Publisher Copyright:
© 2022 Elsevier Ltd
PY - 2023/2
Y1 - 2023/2
N2 - 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.
AB - 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.
KW - Artificial intelligence
KW - Compressive strength
KW - High-performance concrete
KW - Least squares support vector machine
KW - Symbiotic organism search
KW - Workability of concrete
UR - http://www.scopus.com/inward/record.url?scp=85137274457&partnerID=8YFLogxK
U2 - 10.1016/j.eswa.2022.118605
DO - 10.1016/j.eswa.2022.118605
M3 - Article
AN - SCOPUS:85137274457
SN - 0957-4174
VL - 212
JO - Expert Systems with Applications
JF - Expert Systems with Applications
M1 - 118605
ER -