Estimation of the Compressive Strength of Self-Compacting Concrete (SCC) by a Machine Learning Technique Coupling with Novel Optimization Algorithms
Self-compacting concrete (SCC), as a liquid aggregate, is suitable for use in reinforced constructions with no need for vibration. SCC utilization has been found in a wide range of projects. Nevertheless, those applications are often limited due to lacking the knowledge about such mixed materials, e...
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Bilijipub publisher
2023-03-01
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| Series: | Advances in Engineering and Intelligence Systems |
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| Online Access: | https://aeis.bilijipub.com/article_169079_f18ae755db20bf111558015abb48f2c0.pdf |
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| author | Ling Chen Wengang Jiang |
| author_facet | Ling Chen Wengang Jiang |
| author_sort | Ling Chen |
| collection | DOAJ |
| description | Self-compacting concrete (SCC), as a liquid aggregate, is suitable for use in reinforced constructions with no need for vibration. SCC utilization has been found in a wide range of projects. Nevertheless, those applications are often limited due to lacking the knowledge about such mixed materials, especially from experimental testing. The factor of Compressive Strength (CS), which is one of the vital mechanical variables in structure immunization, can be computed either through costly tests or predictive models. Intelligent systems can appraise CS based on ingredients’ data fed to the models. This research aims to model the CS of SCC via a machine learning technique of Support Vector Regression (SVR). The Particle Swarm Optimization (PSO) and Henry’s Gas Solubility Optimization (HGSO) have been utilized to optimize the SVR in finding some internal parameters. Different metrics were chosen to evaluate the performance of models. Consequently, the R2 in the testing stage for SVR-HGSO was computed at 0.90 and for SVR-PSO, 0.93. In the calibration phase, the correlation rate was computed at 0.93 for SVR-HGSO with a 3% difference from the SVR-PSO with 0.90. |
| format | Article |
| id | doaj-art-5ded6af1773740708a1bdd33d457ccd6 |
| institution | Kabale University |
| issn | 2821-0263 |
| language | English |
| publishDate | 2023-03-01 |
| publisher | Bilijipub publisher |
| record_format | Article |
| series | Advances in Engineering and Intelligence Systems |
| spelling | doaj-art-5ded6af1773740708a1bdd33d457ccd62025-02-12T08:47:02ZengBilijipub publisherAdvances in Engineering and Intelligence Systems2821-02632023-03-0100201384910.22034/aeis.2023.383263.1069169079Estimation of the Compressive Strength of Self-Compacting Concrete (SCC) by a Machine Learning Technique Coupling with Novel Optimization AlgorithmsLing Chen0Wengang Jiang1Department of Logistics, Taizhou Vocational and Technical College, Taizhou, Zhejiang, 318000, ChinaDepartment of Logistics, Taizhou Vocational and Technical College, Taizhou, Zhejiang, 318000, ChinaSelf-compacting concrete (SCC), as a liquid aggregate, is suitable for use in reinforced constructions with no need for vibration. SCC utilization has been found in a wide range of projects. Nevertheless, those applications are often limited due to lacking the knowledge about such mixed materials, especially from experimental testing. The factor of Compressive Strength (CS), which is one of the vital mechanical variables in structure immunization, can be computed either through costly tests or predictive models. Intelligent systems can appraise CS based on ingredients’ data fed to the models. This research aims to model the CS of SCC via a machine learning technique of Support Vector Regression (SVR). The Particle Swarm Optimization (PSO) and Henry’s Gas Solubility Optimization (HGSO) have been utilized to optimize the SVR in finding some internal parameters. Different metrics were chosen to evaluate the performance of models. Consequently, the R2 in the testing stage for SVR-HGSO was computed at 0.90 and for SVR-PSO, 0.93. In the calibration phase, the correlation rate was computed at 0.93 for SVR-HGSO with a 3% difference from the SVR-PSO with 0.90.https://aeis.bilijipub.com/article_169079_f18ae755db20bf111558015abb48f2c0.pdfself-compacting concreteparticle swarm optimizationsupport vector regressionhenry’s gas solubility optimizationcompressive strength |
| spellingShingle | Ling Chen Wengang Jiang Estimation of the Compressive Strength of Self-Compacting Concrete (SCC) by a Machine Learning Technique Coupling with Novel Optimization Algorithms Advances in Engineering and Intelligence Systems self-compacting concrete particle swarm optimization support vector regression henry’s gas solubility optimization compressive strength |
| title | Estimation of the Compressive Strength of Self-Compacting Concrete (SCC) by a Machine Learning Technique Coupling with Novel Optimization Algorithms |
| title_full | Estimation of the Compressive Strength of Self-Compacting Concrete (SCC) by a Machine Learning Technique Coupling with Novel Optimization Algorithms |
| title_fullStr | Estimation of the Compressive Strength of Self-Compacting Concrete (SCC) by a Machine Learning Technique Coupling with Novel Optimization Algorithms |
| title_full_unstemmed | Estimation of the Compressive Strength of Self-Compacting Concrete (SCC) by a Machine Learning Technique Coupling with Novel Optimization Algorithms |
| title_short | Estimation of the Compressive Strength of Self-Compacting Concrete (SCC) by a Machine Learning Technique Coupling with Novel Optimization Algorithms |
| title_sort | estimation of the compressive strength of self compacting concrete scc by a machine learning technique coupling with novel optimization algorithms |
| topic | self-compacting concrete particle swarm optimization support vector regression henry’s gas solubility optimization compressive strength |
| url | https://aeis.bilijipub.com/article_169079_f18ae755db20bf111558015abb48f2c0.pdf |
| work_keys_str_mv | AT lingchen estimationofthecompressivestrengthofselfcompactingconcretesccbyamachinelearningtechniquecouplingwithnoveloptimizationalgorithms AT wengangjiang estimationofthecompressivestrengthofselfcompactingconcretesccbyamachinelearningtechniquecouplingwithnoveloptimizationalgorithms |