Empowering knowledge graphs with hybrid retrieval-augmented generation for the intelligent mix scheme of mass concrete
To achieve precise multi-objective mix design of dam concrete-targeting strength, durability, and crack resistance-this study proposes a Dam Concrete Mix Proportioning intelligent generation System (DCMPS) based on a Retrieval-Augmented Generation (RAG) framework. Firstly, a total of 1723 mix propor...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-12-01
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| Series: | Case Studies in Construction Materials |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2214509525007776 |
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| author | Yuqing Shang Zhijiang Ke Peng Lin Qianhui Ren Wenshan Zhang Xiongwu Wang Xiaotao Li Fuyuan Gong Shiqi Wang Baofa Wang Zhengkui Xu Minglun Sun Shunli Tan |
| author_facet | Yuqing Shang Zhijiang Ke Peng Lin Qianhui Ren Wenshan Zhang Xiongwu Wang Xiaotao Li Fuyuan Gong Shiqi Wang Baofa Wang Zhengkui Xu Minglun Sun Shunli Tan |
| author_sort | Yuqing Shang |
| collection | DOAJ |
| description | To achieve precise multi-objective mix design of dam concrete-targeting strength, durability, and crack resistance-this study proposes a Dam Concrete Mix Proportioning intelligent generation System (DCMPS) based on a Retrieval-Augmented Generation (RAG) framework. Firstly, a total of 1723 mix proportioning records collected from 15 hydropower stations were structurally extracted using a customized data transformation tool. A knowledge graph (KG) was then constructed on the Neo4j platform, encompassing entities such as environmental conditions, material compositions, and performance indicators. Secondly, the Moka Massive Mixed Embedding (M3E) model was employed to encode the KG into a vector database, enabling accurate retrieval of relevant mix proportioning cases via cosine similarity calculation. Finally, based on a hierarchical reasoning architecture driven by prompt engineering, DCMPS retrieves relevant cases from the vector database using a semantic search mechanism. A large language model (LLM) conducts comparative analysis on the retrieved cases to generate mix design schemes tailored to specific requirements. The DCMPS’s performance was validated using a test dataset, The results demonstrate that the system outperforms the larger-parameter native LLM in overall evaluation on the test dataset, confirming that knowledge augmentation significantly enhances the performance of smaller models. Case studies indicate that the recommended mix designs closely align with the target performance requirements, and the comparison of relevant cases along with the analysis of material influence mechanisms enhances the interpretability of the results. This DCMPS provides effective technical support for intelligent analysis and engineering decision-making in concrete mix proportioning. |
| format | Article |
| id | doaj-art-75f1c968fef44cfaba6b8525f5e4ecb0 |
| institution | OA Journals |
| issn | 2214-5095 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Case Studies in Construction Materials |
| spelling | doaj-art-75f1c968fef44cfaba6b8525f5e4ecb02025-08-20T02:35:19ZengElsevierCase Studies in Construction Materials2214-50952025-12-0123e0497910.1016/j.cscm.2025.e04979Empowering knowledge graphs with hybrid retrieval-augmented generation for the intelligent mix scheme of mass concreteYuqing Shang0Zhijiang Ke1Peng Lin2Qianhui Ren3Wenshan Zhang4Xiongwu Wang5Xiaotao Li6Fuyuan Gong7Shiqi Wang8Baofa Wang9Zhengkui Xu10Minglun Sun11Shunli Tan12School of Civil Engineering and Water Resources, Qinghai University, Xining 810016, China; Sichuan Energy Internet Research Institute, Tsinghua University, Chengdu 610213, ChinaSchool of Civil Engineering and Water Resources, Qinghai University, Xining 810016, China; Sichuan Energy Internet Research Institute, Tsinghua University, Chengdu 610213, ChinaSchool of Civil Engineering and Water Resources, Qinghai University, Xining 810016, China; Department of Hydraulic Engineering, Tsinghua University, Beijing 100084, China; Sichuan Energy Internet Research Institute, Tsinghua University, Chengdu 610213, China; Corresponding author at: School of Civil Engineering and Water Resources, Qinghai University, Xining 810016, China.School of Civil Engineering and Water Resources, Qinghai University, Xining 810016, ChinaSinohydro Engineering Bureau No.4 Co., Ltd., Xining 810007, ChinaSinohydro Engineering Bureau No.4 Co., Ltd., Xining 810007, ChinaSinohydro Engineering Bureau No.4 Co., Ltd., Xining 810007, ChinaCollege of Architectural Engineering, Zhejiang University, Hangzhou 310058, ChinaCollege of Architectural Engineering, Zhejiang University, Hangzhou 310058, ChinaSinohydro Engineering Bureau No.4 Co., Ltd., Xining 810007, ChinaSinohydro Engineering Bureau No.4 Co., Ltd., Xining 810007, ChinaChina Three Gorges Construction Engineering Corporation, Chengdu 610042, ChinaChina Water Conservancy and Hydropower Engineering Bureau No.11 Co., Ltd., Zhengzhou 450001, ChinaTo achieve precise multi-objective mix design of dam concrete-targeting strength, durability, and crack resistance-this study proposes a Dam Concrete Mix Proportioning intelligent generation System (DCMPS) based on a Retrieval-Augmented Generation (RAG) framework. Firstly, a total of 1723 mix proportioning records collected from 15 hydropower stations were structurally extracted using a customized data transformation tool. A knowledge graph (KG) was then constructed on the Neo4j platform, encompassing entities such as environmental conditions, material compositions, and performance indicators. Secondly, the Moka Massive Mixed Embedding (M3E) model was employed to encode the KG into a vector database, enabling accurate retrieval of relevant mix proportioning cases via cosine similarity calculation. Finally, based on a hierarchical reasoning architecture driven by prompt engineering, DCMPS retrieves relevant cases from the vector database using a semantic search mechanism. A large language model (LLM) conducts comparative analysis on the retrieved cases to generate mix design schemes tailored to specific requirements. The DCMPS’s performance was validated using a test dataset, The results demonstrate that the system outperforms the larger-parameter native LLM in overall evaluation on the test dataset, confirming that knowledge augmentation significantly enhances the performance of smaller models. Case studies indicate that the recommended mix designs closely align with the target performance requirements, and the comparison of relevant cases along with the analysis of material influence mechanisms enhances the interpretability of the results. This DCMPS provides effective technical support for intelligent analysis and engineering decision-making in concrete mix proportioning.http://www.sciencedirect.com/science/article/pii/S2214509525007776Mixture designKnowledge graphLarge language modelRetrieval-augmented generationSemantic retrieval |
| spellingShingle | Yuqing Shang Zhijiang Ke Peng Lin Qianhui Ren Wenshan Zhang Xiongwu Wang Xiaotao Li Fuyuan Gong Shiqi Wang Baofa Wang Zhengkui Xu Minglun Sun Shunli Tan Empowering knowledge graphs with hybrid retrieval-augmented generation for the intelligent mix scheme of mass concrete Case Studies in Construction Materials Mixture design Knowledge graph Large language model Retrieval-augmented generation Semantic retrieval |
| title | Empowering knowledge graphs with hybrid retrieval-augmented generation for the intelligent mix scheme of mass concrete |
| title_full | Empowering knowledge graphs with hybrid retrieval-augmented generation for the intelligent mix scheme of mass concrete |
| title_fullStr | Empowering knowledge graphs with hybrid retrieval-augmented generation for the intelligent mix scheme of mass concrete |
| title_full_unstemmed | Empowering knowledge graphs with hybrid retrieval-augmented generation for the intelligent mix scheme of mass concrete |
| title_short | Empowering knowledge graphs with hybrid retrieval-augmented generation for the intelligent mix scheme of mass concrete |
| title_sort | empowering knowledge graphs with hybrid retrieval augmented generation for the intelligent mix scheme of mass concrete |
| topic | Mixture design Knowledge graph Large language model Retrieval-augmented generation Semantic retrieval |
| url | http://www.sciencedirect.com/science/article/pii/S2214509525007776 |
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