Utilization of metaheuristic-based regression analysis to calculate the modified high-performance concrete's compressive strength
Different regression analytics were used to provide a unique approach to testing the compressive strength (CS) of high-performance concrete (HPC) made with blast furnace slag and fly ash. In this study, it was employed the equilibrium optimizer (EO) and the arithmetic optimization algorithm (AOA) to...
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Tamkang University Press
2025-01-01
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| Series: | Journal of Applied Science and Engineering |
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| Online Access: | http://jase.tku.edu.tw/articles/jase-202508-28-08-0012 |
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| author | Liming Mu |
| author_facet | Liming Mu |
| author_sort | Liming Mu |
| collection | DOAJ |
| description | Different regression analytics were used to provide a unique approach to testing the compressive strength (CS) of high-performance concrete (HPC) made with blast furnace slag and fly ash. In this study, it was employed the equilibrium optimizer (EO) and the arithmetic optimization algorithm (AOA) to identify key regression method variables (i.e., Support vector regression (SVR)) which could be adjusted to improve performance. The suggested approaches were created utilizing 1030 tests, eight inputs (aggregates, primary mix designs,
admixtures, and curing age), and the CS as the forecasting objective. The results were then compared to those in the corpus of already published scientific literature. Estimation outcomes point to the potential benefit of combining EO-SVR with AOA-SVR analysis. The AOA-SVR displayed significantly better R2 (0.9874 and 0.993) and lower RMSE values as compared to the EO-SVR. Comparing the data demonstrates how much better the created AOA-SVR is than anything that has previously been reported. Overall, the suggested technique for determining the CS of HPC augmented with fly ash and blast furnace slag may be used using the AOA-SVR analysis. |
| format | Article |
| id | doaj-art-96aa9a4314a5423c9fb1e6fae8f904ed |
| institution | Kabale University |
| issn | 2708-9967 2708-9975 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Tamkang University Press |
| record_format | Article |
| series | Journal of Applied Science and Engineering |
| spelling | doaj-art-96aa9a4314a5423c9fb1e6fae8f904ed2025-01-08T14:32:05ZengTamkang University PressJournal of Applied Science and Engineering2708-99672708-99752025-01-012881745175810.6180/jase.202508_28(8).0012Utilization of metaheuristic-based regression analysis to calculate the modified high-performance concrete's compressive strengthLiming Mu0Department of Architectural Engineering, Shijiazhuang College of Applied Technology, Shijiazhuang 050000, ChinaDifferent regression analytics were used to provide a unique approach to testing the compressive strength (CS) of high-performance concrete (HPC) made with blast furnace slag and fly ash. In this study, it was employed the equilibrium optimizer (EO) and the arithmetic optimization algorithm (AOA) to identify key regression method variables (i.e., Support vector regression (SVR)) which could be adjusted to improve performance. The suggested approaches were created utilizing 1030 tests, eight inputs (aggregates, primary mix designs, admixtures, and curing age), and the CS as the forecasting objective. The results were then compared to those in the corpus of already published scientific literature. Estimation outcomes point to the potential benefit of combining EO-SVR with AOA-SVR analysis. The AOA-SVR displayed significantly better R2 (0.9874 and 0.993) and lower RMSE values as compared to the EO-SVR. Comparing the data demonstrates how much better the created AOA-SVR is than anything that has previously been reported. Overall, the suggested technique for determining the CS of HPC augmented with fly ash and blast furnace slag may be used using the AOA-SVR analysis.http://jase.tku.edu.tw/articles/jase-202508-28-08-0012compressive strengthblast furnace slaghigh-performance concretesupport vector regressionfly ashartificial intelligence |
| spellingShingle | Liming Mu Utilization of metaheuristic-based regression analysis to calculate the modified high-performance concrete's compressive strength Journal of Applied Science and Engineering compressive strength blast furnace slag high-performance concrete support vector regression fly ash artificial intelligence |
| title | Utilization of metaheuristic-based regression analysis to calculate the modified high-performance concrete's compressive strength |
| title_full | Utilization of metaheuristic-based regression analysis to calculate the modified high-performance concrete's compressive strength |
| title_fullStr | Utilization of metaheuristic-based regression analysis to calculate the modified high-performance concrete's compressive strength |
| title_full_unstemmed | Utilization of metaheuristic-based regression analysis to calculate the modified high-performance concrete's compressive strength |
| title_short | Utilization of metaheuristic-based regression analysis to calculate the modified high-performance concrete's compressive strength |
| title_sort | utilization of metaheuristic based regression analysis to calculate the modified high performance concrete s compressive strength |
| topic | compressive strength blast furnace slag high-performance concrete support vector regression fly ash artificial intelligence |
| url | http://jase.tku.edu.tw/articles/jase-202508-28-08-0012 |
| work_keys_str_mv | AT limingmu utilizationofmetaheuristicbasedregressionanalysistocalculatethemodifiedhighperformanceconcretescompressivestrength |