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Prediction of compressive strength of high-performance concrete using multi-layer perceptron

dc.contributor.authorXu Wu
dc.contributor.authorGuifeng Yan
dc.contributor.authorWei Zhang
dc.contributor.authorYuping Bao
dc.contributor.otherDepartment of BIM Research, Nantong Institute of Technology, Nantong 226002, Jiangsu, China
dc.contributor.otherDepartment of BIM Research, Nantong Institute of Technology, Nantong 226002, Jiangsu, China
dc.contributor.otherDepartment of BIM Research, Nantong Institute of Technology, Nantong 226002, Jiangsu, China
dc.contributor.otherDepartment of BIM Research, Nantong Institute of Technology, Nantong 226002, Jiangsu, China
dc.date.accessioned2024-07-17T06:26:35Z
dc.date.accessioned2025-10-08T09:23:21Z
dc.date.available2025-10-08T09:23:21Z
dc.date.issued01-07-2024
dc.identifier.urihttp://digilib.fisipol.ugm.ac.id/repo/handle/15717717/40189
dc.description.abstractThe correlations between the mechanical properties of HPCs and their mixture compositions are complex, non-linear, and complex to characterize employing standard statistical methods. This paper aimed to estimate HPC’s compressive strength using a machine learning algorithm including Multi-layer Perceptron (MLP) with an HPC mixed collection of 168 samples via eight input variables. In addition, three meta-heuristic optimizers have been used for improving the efficiency and accuracy of MLP, which are included Dandelion Optimization (DO), Aquila Optimizer (AO), and Sooty Tern Optimization Algorithm (STOA). After fitting the presented models, the developed models’ predictive generalization and efficiency ability is evaluated against a set of performance parameters. All models used were found to perform as suitable in predicting outcomes, which can be employed for saving time and energy. As a result, Aquila’s optimization had the most accurate by MLP compared to other hybrid models. MLAO3 obtained R 2 = 0.994 and RMSE = 1.27(MPa), which are the most suitable result compared to other models.
dc.language.isoEN
dc.publisherTamkang University Press
dc.subject.lccEngineering (General). Civil engineering (General)
dc.titlePrediction of compressive strength of high-performance concrete using multi-layer perceptron
dc.typeArticle
dc.description.keywordshigh-performance concrete
dc.description.keywordscompressive strength
dc.description.keywordsmulti-layer perceptron
dc.description.keywordsdandelion optimization
dc.description.keywordsaquila optimizer
dc.description.keywordssooty tern optimization algorithm
dc.description.pages2789-2803
dc.description.doi10.6180/jase.202407_27(7).0004
dc.title.journalJournal of Applied Science and Engineering
dc.identifier.e-issn2708-9975
dc.identifier.oaioai:doaj.org/journal:23e230cf89d14219abb106d9029f3f0d
dc.journal.infoVolume 27, Issue 7


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