Uncertainty-Based Performance Prediction and Optimization of High-Fluidization Cement Grouting Material Using Machine Learning and Bayesian Inference
Abstract In pavement engineering, cement grouting material is widely used to pour into large void asphalt concrete to prepare semi-flexible composite mixtures. It plays an essential role in the performance of the semi-flexible composite mixture. To meet specific engineering requirements, various add...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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SpringerOpen
2022-12-01
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| Series: | International Journal of Concrete Structures and Materials |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s40069-022-00562-4 |
| _version_ | 1811204192597966848 |
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| author | Jiaolong Ren Meng Wang Lin Zhang Zedong Zhao Jian Wang Jingchun Chen Hongbo Zhao |
| author_facet | Jiaolong Ren Meng Wang Lin Zhang Zedong Zhao Jian Wang Jingchun Chen Hongbo Zhao |
| author_sort | Jiaolong Ren |
| collection | DOAJ |
| description | Abstract In pavement engineering, cement grouting material is widely used to pour into large void asphalt concrete to prepare semi-flexible composite mixtures. It plays an essential role in the performance of the semi-flexible composite mixture. To meet specific engineering requirements, various additives are mixed into the grouting material to improve the physical and mechanical properties. As a result, the uncertainty of the grouting material is also more significant as the complexity of material composition increases during the material design. It will bring some unknown risks for the engineering application. Hence, it is necessary to quantize the uncertainty during the material design of the grouting material and evaluate the reliability of the material formula. In this study, a novel framework of material design was developed by combing the Multioutput support vector machine (MSVM), Bayesian inference, and laboratory experiments. The MSVM was used to approximate and characterize the complex and nonlinear relationship between the grouting material formula and its properties based on laboratory experiments. The Bayesian inference was adopted to deal with the uncertainty of material design using the Markov Chain Monte Carlo. An optimized formula of the cement grouting material is obtained based on the developed framework. Experimental results show that the optimized formula improves engineering properties and performance stability, especially early strength. The developed framework provides a helpful, valuable, and promising tool for evaluating the reliability of the material design of the grouting material considering the uncertainty. |
| first_indexed | 2024-04-12T03:08:16Z |
| format | Article |
| id | doaj.art-5706f377e6544fc2b766993f04574e26 |
| institution | Directory Open Access Journal |
| issn | 1976-0485 2234-1315 |
| language | English |
| last_indexed | 2024-04-12T03:08:16Z |
| publishDate | 2022-12-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | International Journal of Concrete Structures and Materials |
| spelling | doaj.art-5706f377e6544fc2b766993f04574e262022-12-22T03:50:27ZengSpringerOpenInternational Journal of Concrete Structures and Materials1976-04852234-13152022-12-0116111510.1186/s40069-022-00562-4Uncertainty-Based Performance Prediction and Optimization of High-Fluidization Cement Grouting Material Using Machine Learning and Bayesian InferenceJiaolong Ren0Meng Wang1Lin Zhang2Zedong Zhao3Jian Wang4Jingchun Chen5Hongbo Zhao6School of Civil and Architectural Engineering, Shandong University of TechnologySchool of Civil and Architectural Engineering, Shandong University of TechnologySchool of Civil and Architectural Engineering, Shandong University of TechnologySchool of Transportation and Vehicle Engineering, Shandong University of TechnologySchool of Civil and Architectural Engineering, Shandong University of TechnologySchool of Civil and Architectural Engineering, Shandong University of TechnologySchool of Civil and Architectural Engineering, Shandong University of TechnologyAbstract In pavement engineering, cement grouting material is widely used to pour into large void asphalt concrete to prepare semi-flexible composite mixtures. It plays an essential role in the performance of the semi-flexible composite mixture. To meet specific engineering requirements, various additives are mixed into the grouting material to improve the physical and mechanical properties. As a result, the uncertainty of the grouting material is also more significant as the complexity of material composition increases during the material design. It will bring some unknown risks for the engineering application. Hence, it is necessary to quantize the uncertainty during the material design of the grouting material and evaluate the reliability of the material formula. In this study, a novel framework of material design was developed by combing the Multioutput support vector machine (MSVM), Bayesian inference, and laboratory experiments. The MSVM was used to approximate and characterize the complex and nonlinear relationship between the grouting material formula and its properties based on laboratory experiments. The Bayesian inference was adopted to deal with the uncertainty of material design using the Markov Chain Monte Carlo. An optimized formula of the cement grouting material is obtained based on the developed framework. Experimental results show that the optimized formula improves engineering properties and performance stability, especially early strength. The developed framework provides a helpful, valuable, and promising tool for evaluating the reliability of the material design of the grouting material considering the uncertainty.https://doi.org/10.1186/s40069-022-00562-4Cement grouting materialUncertaintyOptimization designBayesian inferenceMultioutput support vector machine |
| spellingShingle | Jiaolong Ren Meng Wang Lin Zhang Zedong Zhao Jian Wang Jingchun Chen Hongbo Zhao Uncertainty-Based Performance Prediction and Optimization of High-Fluidization Cement Grouting Material Using Machine Learning and Bayesian Inference International Journal of Concrete Structures and Materials Cement grouting material Uncertainty Optimization design Bayesian inference Multioutput support vector machine |
| title | Uncertainty-Based Performance Prediction and Optimization of High-Fluidization Cement Grouting Material Using Machine Learning and Bayesian Inference |
| title_full | Uncertainty-Based Performance Prediction and Optimization of High-Fluidization Cement Grouting Material Using Machine Learning and Bayesian Inference |
| title_fullStr | Uncertainty-Based Performance Prediction and Optimization of High-Fluidization Cement Grouting Material Using Machine Learning and Bayesian Inference |
| title_full_unstemmed | Uncertainty-Based Performance Prediction and Optimization of High-Fluidization Cement Grouting Material Using Machine Learning and Bayesian Inference |
| title_short | Uncertainty-Based Performance Prediction and Optimization of High-Fluidization Cement Grouting Material Using Machine Learning and Bayesian Inference |
| title_sort | uncertainty based performance prediction and optimization of high fluidization cement grouting material using machine learning and bayesian inference |
| topic | Cement grouting material Uncertainty Optimization design Bayesian inference Multioutput support vector machine |
| url | https://doi.org/10.1186/s40069-022-00562-4 |
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