Mixture optimisation for cement-soil mixtures with embedded GFRP tendons
The glass fiber-reinforced polymer (GFRP) rebar reinforced cemented soil is widely employed to solve the weak foundation problem led by sludge particularly. The robustness of this structure is highly dependent on the interface bond strength between the GFRP tendon and cemented soils. However, its ap...
| Main Authors: | , , , , , , , , |
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| Format: | Journal Article |
| Language: | English |
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ELSEVIER
2022
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| Online Access: | http://purl.org/au-research/grants/arc/LP180100222 http://hdl.handle.net/20.500.11937/90920 |
| _version_ | 1848765460951072768 |
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| author | Zhang, G. Chen, C. Sun, J. Li, K. Xiao, F. Wang, Yufei Chen, M. Huang, J. Wang, Xiangyu |
| author_facet | Zhang, G. Chen, C. Sun, J. Li, K. Xiao, F. Wang, Yufei Chen, M. Huang, J. Wang, Xiangyu |
| author_sort | Zhang, G. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | The glass fiber-reinforced polymer (GFRP) rebar reinforced cemented soil is widely employed to solve the weak foundation problem led by sludge particularly. The robustness of this structure is highly dependent on the interface bond strength between the GFRP tendon and cemented soils. However, its application is obstructed owing to the deficient studies on the influence factors. Therefore, this study investigates the effects of water content (Cw: 50%–90%), cement proportion (Cc: 6%–30%), and curing period (Tc: 28–90 days) on peak and residual interface bond strengths (Tp and Tt), as well as the unconfined compression strength (UCS). Results indicated that mechanical properties were positively responded to Tc and Cc, while negatively correlated to Cw. Besides, Random Forest (RF), one of the machine learning (ML) models, was developed with its hyperparameters tuned by the firefly algorithm (FA) based on the experimental dataset. The pullout strength was predicted by the ML model for the first time. High correlation coefficients and low root-mean-square errors verified the accuracy of established RF-FA models in this study. Subsequently, a coFA-based multi-objective optimisation firefly algorithm (MOFA) was introduced to optimise tri-objectives between UCS, Tp (or Tt), and cost. The Pareto fronts were successfully acquired for optimal mixture designs, which contributes to the application of GFRP tendon reinforced cemented soil in practice. In addition, the sensitivity of input variables was evaluated and ranked. |
| first_indexed | 2025-11-14T11:35:37Z |
| format | Journal Article |
| id | curtin-20.500.11937-90920 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T11:35:37Z |
| publishDate | 2022 |
| publisher | ELSEVIER |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-909202023-05-10T03:00:21Z Mixture optimisation for cement-soil mixtures with embedded GFRP tendons Zhang, G. Chen, C. Sun, J. Li, K. Xiao, F. Wang, Yufei Chen, M. Huang, J. Wang, Xiangyu Science & Technology Technology Materials Science, Multidisciplinary Metallurgy & Metallurgical Engineering Materials Science Cemented soil Interface bond strength <p>Glass fiber reinforced polymer & nbsp;reinforcement & nbsp;</p> Machine learning Multi-objective optimisation CONCRETE STRENGTH BEHAVIOR PREDICTION ALGORITHM COLUMNS The glass fiber-reinforced polymer (GFRP) rebar reinforced cemented soil is widely employed to solve the weak foundation problem led by sludge particularly. The robustness of this structure is highly dependent on the interface bond strength between the GFRP tendon and cemented soils. However, its application is obstructed owing to the deficient studies on the influence factors. Therefore, this study investigates the effects of water content (Cw: 50%–90%), cement proportion (Cc: 6%–30%), and curing period (Tc: 28–90 days) on peak and residual interface bond strengths (Tp and Tt), as well as the unconfined compression strength (UCS). Results indicated that mechanical properties were positively responded to Tc and Cc, while negatively correlated to Cw. Besides, Random Forest (RF), one of the machine learning (ML) models, was developed with its hyperparameters tuned by the firefly algorithm (FA) based on the experimental dataset. The pullout strength was predicted by the ML model for the first time. High correlation coefficients and low root-mean-square errors verified the accuracy of established RF-FA models in this study. Subsequently, a coFA-based multi-objective optimisation firefly algorithm (MOFA) was introduced to optimise tri-objectives between UCS, Tp (or Tt), and cost. The Pareto fronts were successfully acquired for optimal mixture designs, which contributes to the application of GFRP tendon reinforced cemented soil in practice. In addition, the sensitivity of input variables was evaluated and ranked. 2022 Journal Article http://hdl.handle.net/20.500.11937/90920 10.1016/j.jmrt.2022.02.076 English http://purl.org/au-research/grants/arc/LP180100222 http://creativecommons.org/licenses/by/4.0/ ELSEVIER fulltext |
| spellingShingle | Science & Technology Technology Materials Science, Multidisciplinary Metallurgy & Metallurgical Engineering Materials Science Cemented soil Interface bond strength <p>Glass fiber reinforced polymer & nbsp;reinforcement & nbsp;</p> Machine learning Multi-objective optimisation CONCRETE STRENGTH BEHAVIOR PREDICTION ALGORITHM COLUMNS Zhang, G. Chen, C. Sun, J. Li, K. Xiao, F. Wang, Yufei Chen, M. Huang, J. Wang, Xiangyu Mixture optimisation for cement-soil mixtures with embedded GFRP tendons |
| title | Mixture optimisation for cement-soil mixtures with embedded GFRP tendons |
| title_full | Mixture optimisation for cement-soil mixtures with embedded GFRP tendons |
| title_fullStr | Mixture optimisation for cement-soil mixtures with embedded GFRP tendons |
| title_full_unstemmed | Mixture optimisation for cement-soil mixtures with embedded GFRP tendons |
| title_short | Mixture optimisation for cement-soil mixtures with embedded GFRP tendons |
| title_sort | mixture optimisation for cement-soil mixtures with embedded gfrp tendons |
| topic | Science & Technology Technology Materials Science, Multidisciplinary Metallurgy & Metallurgical Engineering Materials Science Cemented soil Interface bond strength <p>Glass fiber reinforced polymer & nbsp;reinforcement & nbsp;</p> Machine learning Multi-objective optimisation CONCRETE STRENGTH BEHAVIOR PREDICTION ALGORITHM COLUMNS |
| url | http://purl.org/au-research/grants/arc/LP180100222 http://hdl.handle.net/20.500.11937/90920 |