Analyzing the compressive performance of lightweight foamcrete and parameter interdependencies using machine intelligence strategies
As an alternate to regular concrete, foam concrete, also called foamcrete, has several useful applications. It saves money on transportation and production costs as well as dead weight on buildings and foundations, which helps with energy efficiency. Nevertheless, there is still a lack of practical...
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| Format: | Article |
| Language: | English |
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De Gruyter
2025-07-01
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| Series: | Reviews on Advanced Materials Science |
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| Online Access: | https://doi.org/10.1515/rams-2025-0126 |
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| author | Wang Guoyuan Fan Wenbo Shi Qingbin Luo Yingqi |
| author_facet | Wang Guoyuan Fan Wenbo Shi Qingbin Luo Yingqi |
| author_sort | Wang Guoyuan |
| collection | DOAJ |
| description | As an alternate to regular concrete, foam concrete, also called foamcrete, has several useful applications. It saves money on transportation and production costs as well as dead weight on buildings and foundations, which helps with energy efficiency. Nevertheless, there is still a lack of practical applications, which calls for more research, especially in strength studies, to increase its use in the actual world. For this purpose, the compressive strength (C-S) of foamcrete was assessed using two machine learning algorithms: gene expression programming (GEP) and multi-expression programming (MEP). A sensitivity analysis was conducted to determine how important certain aspects were. For predicting foamcrete’s compressive strength, MEP was better than GEP. By comparison, the MEP model had an R
2 value of 0.970, while the GEP models only managed 0.94. This is further supported by the findings of the statistical analysis and the ML models’ cross-validation using Taylor’s diagram. The sensitivity analysis results indicated that density (28.0%), cement content (11.0%), and age (8.5%) were the three most significant criteria influencing overall strength. The generated models can determine the compressive strength of foamcrete for different input parameter values, hence enhancing its practical uses and saving time and financial resources compared to laboratory testing. |
| format | Article |
| id | doaj-art-3bd7741999df40fa9f0cd68b65c46873 |
| institution | DOAJ |
| issn | 1605-8127 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | De Gruyter |
| record_format | Article |
| series | Reviews on Advanced Materials Science |
| spelling | doaj-art-3bd7741999df40fa9f0cd68b65c468732025-08-20T03:13:23ZengDe GruyterReviews on Advanced Materials Science1605-81272025-07-01641pp. 47548010.1515/rams-2025-0126Analyzing the compressive performance of lightweight foamcrete and parameter interdependencies using machine intelligence strategiesWang Guoyuan0Fan Wenbo1Shi Qingbin2Luo Yingqi3China Coal Construction Group Co., LTD., Beijing, 102218, ChinaChina Coal Construction Group Co., LTD., Beijing, 102218, China49th Engineering Department, China Coal Construction Co., LTD., Handan, Hebei, 056003, China49th Engineering Department, China Coal Construction Co., LTD., Handan, Hebei, 056003, ChinaAs an alternate to regular concrete, foam concrete, also called foamcrete, has several useful applications. It saves money on transportation and production costs as well as dead weight on buildings and foundations, which helps with energy efficiency. Nevertheless, there is still a lack of practical applications, which calls for more research, especially in strength studies, to increase its use in the actual world. For this purpose, the compressive strength (C-S) of foamcrete was assessed using two machine learning algorithms: gene expression programming (GEP) and multi-expression programming (MEP). A sensitivity analysis was conducted to determine how important certain aspects were. For predicting foamcrete’s compressive strength, MEP was better than GEP. By comparison, the MEP model had an R 2 value of 0.970, while the GEP models only managed 0.94. This is further supported by the findings of the statistical analysis and the ML models’ cross-validation using Taylor’s diagram. The sensitivity analysis results indicated that density (28.0%), cement content (11.0%), and age (8.5%) were the three most significant criteria influencing overall strength. The generated models can determine the compressive strength of foamcrete for different input parameter values, hence enhancing its practical uses and saving time and financial resources compared to laboratory testing.https://doi.org/10.1515/rams-2025-0126compressive strengthfoamed concretemachine learning |
| spellingShingle | Wang Guoyuan Fan Wenbo Shi Qingbin Luo Yingqi Analyzing the compressive performance of lightweight foamcrete and parameter interdependencies using machine intelligence strategies Reviews on Advanced Materials Science compressive strength foamed concrete machine learning |
| title | Analyzing the compressive performance of lightweight foamcrete and parameter interdependencies using machine intelligence strategies |
| title_full | Analyzing the compressive performance of lightweight foamcrete and parameter interdependencies using machine intelligence strategies |
| title_fullStr | Analyzing the compressive performance of lightweight foamcrete and parameter interdependencies using machine intelligence strategies |
| title_full_unstemmed | Analyzing the compressive performance of lightweight foamcrete and parameter interdependencies using machine intelligence strategies |
| title_short | Analyzing the compressive performance of lightweight foamcrete and parameter interdependencies using machine intelligence strategies |
| title_sort | analyzing the compressive performance of lightweight foamcrete and parameter interdependencies using machine intelligence strategies |
| topic | compressive strength foamed concrete machine learning |
| url | https://doi.org/10.1515/rams-2025-0126 |
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