Estimation of compressive strength of ultra-high performance lightweight concrete (UHPLC) using neural network.
High strength and lightweight are key trends in concrete development. Achieving a balance between these properties to produce high structural efficiency (strength-to-weight ratio) concrete is challenging due to the complex relationship between compressive strength and material components. In this st...
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2025-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0326652 |
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| author | Yan Zhao Ziyan Huang Huilong Zhao Zhen Xu Wei Chang Bai Liu |
| author_facet | Yan Zhao Ziyan Huang Huilong Zhao Zhen Xu Wei Chang Bai Liu |
| author_sort | Yan Zhao |
| collection | DOAJ |
| description | High strength and lightweight are key trends in concrete development. Achieving a balance between these properties to produce high structural efficiency (strength-to-weight ratio) concrete is challenging due to the complex relationship between compressive strength and material components. In this study, two artificial neural network (ANN) models-the BP and Elman networks were used to predict the compressive strength of ultra-high-performance lightweight concrete (UHPLC), based on a robust database of 115 test datasets from previous studies. The investigated parameters included the cement grade (Grade 42.5 and Grade 52.5), cement content (352 kg/m3-938 kg/m3), silica fume content (0 kg/m3-350 kg/m3), fly ash content (0 kg/m3-220 kg/m3), microsphere content (0 kg/m3-624 kg/m3), lightweight sand types (pottery sand, expanded perlite sand, and expanded shale lightweight sand), lightweight sand content (0 kg/m3-769 kg/m3), sand type (quartz sand, river sand), sand content (0 kg/m3-1314 kg/m3), water (90 kg/m3-395 kg/m3), water reduce (0 kg/m3-42.8 kg/m3), steel fiber content (0 kg/m3-234 kg/m3). Correlation analysis and sensitive analysis indicated that lightweight sand content and sand content had the most significant effects on UHPLC compressive strength, followed by water content. Conversely, fly ash content and lightweight sand type had minimal impact. The developed ANN models for UHPLC compressive strength demonstrated high predictive accuracy for both training and testing datasets, which the RMSE of BP network and Elman network were 0.226 and 0.160, respectively, while R2 of both two developed models were more than 0.98. Additionally, UHPLC exhibited a higher compressive strength-to-density ratio than high-strength concrete, ultra-high-performance concrete, and even Q235 steel. Three strategies were proposed for creating ultra-high-performance lightweight composites: optimizing packing density and lowering the water-binder ratio, along with careful selection of lightweight aggregates. |
| format | Article |
| id | doaj-art-72876de6d5da426ba31b07e696db358f |
| institution | Kabale University |
| issn | 1932-6203 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-72876de6d5da426ba31b07e696db358f2025-08-20T03:28:48ZengPublic Library of Science (PLoS)PLoS ONE1932-62032025-01-01207e032665210.1371/journal.pone.0326652Estimation of compressive strength of ultra-high performance lightweight concrete (UHPLC) using neural network.Yan ZhaoZiyan HuangHuilong ZhaoZhen XuWei ChangBai LiuHigh strength and lightweight are key trends in concrete development. Achieving a balance between these properties to produce high structural efficiency (strength-to-weight ratio) concrete is challenging due to the complex relationship between compressive strength and material components. In this study, two artificial neural network (ANN) models-the BP and Elman networks were used to predict the compressive strength of ultra-high-performance lightweight concrete (UHPLC), based on a robust database of 115 test datasets from previous studies. The investigated parameters included the cement grade (Grade 42.5 and Grade 52.5), cement content (352 kg/m3-938 kg/m3), silica fume content (0 kg/m3-350 kg/m3), fly ash content (0 kg/m3-220 kg/m3), microsphere content (0 kg/m3-624 kg/m3), lightweight sand types (pottery sand, expanded perlite sand, and expanded shale lightweight sand), lightweight sand content (0 kg/m3-769 kg/m3), sand type (quartz sand, river sand), sand content (0 kg/m3-1314 kg/m3), water (90 kg/m3-395 kg/m3), water reduce (0 kg/m3-42.8 kg/m3), steel fiber content (0 kg/m3-234 kg/m3). Correlation analysis and sensitive analysis indicated that lightweight sand content and sand content had the most significant effects on UHPLC compressive strength, followed by water content. Conversely, fly ash content and lightweight sand type had minimal impact. The developed ANN models for UHPLC compressive strength demonstrated high predictive accuracy for both training and testing datasets, which the RMSE of BP network and Elman network were 0.226 and 0.160, respectively, while R2 of both two developed models were more than 0.98. Additionally, UHPLC exhibited a higher compressive strength-to-density ratio than high-strength concrete, ultra-high-performance concrete, and even Q235 steel. Three strategies were proposed for creating ultra-high-performance lightweight composites: optimizing packing density and lowering the water-binder ratio, along with careful selection of lightweight aggregates.https://doi.org/10.1371/journal.pone.0326652 |
| spellingShingle | Yan Zhao Ziyan Huang Huilong Zhao Zhen Xu Wei Chang Bai Liu Estimation of compressive strength of ultra-high performance lightweight concrete (UHPLC) using neural network. PLoS ONE |
| title | Estimation of compressive strength of ultra-high performance lightweight concrete (UHPLC) using neural network. |
| title_full | Estimation of compressive strength of ultra-high performance lightweight concrete (UHPLC) using neural network. |
| title_fullStr | Estimation of compressive strength of ultra-high performance lightweight concrete (UHPLC) using neural network. |
| title_full_unstemmed | Estimation of compressive strength of ultra-high performance lightweight concrete (UHPLC) using neural network. |
| title_short | Estimation of compressive strength of ultra-high performance lightweight concrete (UHPLC) using neural network. |
| title_sort | estimation of compressive strength of ultra high performance lightweight concrete uhplc using neural network |
| url | https://doi.org/10.1371/journal.pone.0326652 |
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