Study on Strength Prediction and Material Scheme Optimization for Modified Red Mud Based on Artificial Neural Networks
Focusing on the complex nonlinear problems of strength prediction and the material scheme design of modified red mud for use as a road material in engineering applications, a strength prediction neural network is established and utilized to optimize the material scheme, including the compound-solidi...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-11-01
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| Series: | Buildings |
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| Online Access: | https://www.mdpi.com/2075-5309/14/11/3544 |
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| author | Qiaoling Ji Xiuru Jia Yingjian Wang Yu Cheng |
| author_facet | Qiaoling Ji Xiuru Jia Yingjian Wang Yu Cheng |
| author_sort | Qiaoling Ji |
| collection | DOAJ |
| description | Focusing on the complex nonlinear problems of strength prediction and the material scheme design of modified red mud for use as a road material in engineering applications, a strength prediction neural network is established and utilized to optimize the material scheme, including the compound-solidifying agent ratio, water content, and curing age, based on experimental data accumulated during years of engineering practice and an artificial neural network. In this study, a backpropagation (BP) neural network is adopted, and 114 sets of experimental data are used to train the parameters of the unconfined compressive strength prediction model. Then, using the BP strength prediction model, the material scheme optimization process is carried out, with the strength and material costs as the objectives. The results show that the BP neural network model has a high prediction accuracy, the relative prediction error is basically within 10%, the root-mean-squared error is less than 0.04, and the correlation coefficient is more than 0.99. According to the strength requirements of modified red mud in different road projects and the constraints of each property, an optimal material scheme with a lower cost and higher 7 d target strength is obtained using a mix of polymer agent–fly-ash–cement–speed-cement in a ratio of 0.02%:1.96%:4.78%:0%, with a 33.93% water content of raw red mud, so that the target strength and material cost are 2.987 MPa and 17.099 CNY/T. This study creates an optimal material scheme, incorporating the compound-solidifying agent ratio, curing age, and water content of the modified red mud road material according to the strength requirements of different projects, thereby promoting the popularization of the utilization of red mud with better engineering practicability and economy. |
| format | Article |
| id | doaj-art-92bc8f70e44d4e73a235bf5a3db87815 |
| institution | Kabale University |
| issn | 2075-5309 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Buildings |
| spelling | doaj-art-92bc8f70e44d4e73a235bf5a3db878152024-11-26T17:56:03ZengMDPI AGBuildings2075-53092024-11-011411354410.3390/buildings14113544Study on Strength Prediction and Material Scheme Optimization for Modified Red Mud Based on Artificial Neural NetworksQiaoling Ji0Xiuru Jia1Yingjian Wang2Yu Cheng3College of Transportation, Shandong University of Science and Technology, Qingdao 266590, ChinaCollege of Transportation, Shandong University of Science and Technology, Qingdao 266590, ChinaCollege of Transportation, Shandong University of Science and Technology, Qingdao 266590, ChinaCollege of Transportation, Shandong University of Science and Technology, Qingdao 266590, ChinaFocusing on the complex nonlinear problems of strength prediction and the material scheme design of modified red mud for use as a road material in engineering applications, a strength prediction neural network is established and utilized to optimize the material scheme, including the compound-solidifying agent ratio, water content, and curing age, based on experimental data accumulated during years of engineering practice and an artificial neural network. In this study, a backpropagation (BP) neural network is adopted, and 114 sets of experimental data are used to train the parameters of the unconfined compressive strength prediction model. Then, using the BP strength prediction model, the material scheme optimization process is carried out, with the strength and material costs as the objectives. The results show that the BP neural network model has a high prediction accuracy, the relative prediction error is basically within 10%, the root-mean-squared error is less than 0.04, and the correlation coefficient is more than 0.99. According to the strength requirements of modified red mud in different road projects and the constraints of each property, an optimal material scheme with a lower cost and higher 7 d target strength is obtained using a mix of polymer agent–fly-ash–cement–speed-cement in a ratio of 0.02%:1.96%:4.78%:0%, with a 33.93% water content of raw red mud, so that the target strength and material cost are 2.987 MPa and 17.099 CNY/T. This study creates an optimal material scheme, incorporating the compound-solidifying agent ratio, curing age, and water content of the modified red mud road material according to the strength requirements of different projects, thereby promoting the popularization of the utilization of red mud with better engineering practicability and economy.https://www.mdpi.com/2075-5309/14/11/3544modified red mudoptimizationstrength predictionBP neural networksolidifying-agent ratiowater content |
| spellingShingle | Qiaoling Ji Xiuru Jia Yingjian Wang Yu Cheng Study on Strength Prediction and Material Scheme Optimization for Modified Red Mud Based on Artificial Neural Networks Buildings modified red mud optimization strength prediction BP neural network solidifying-agent ratio water content |
| title | Study on Strength Prediction and Material Scheme Optimization for Modified Red Mud Based on Artificial Neural Networks |
| title_full | Study on Strength Prediction and Material Scheme Optimization for Modified Red Mud Based on Artificial Neural Networks |
| title_fullStr | Study on Strength Prediction and Material Scheme Optimization for Modified Red Mud Based on Artificial Neural Networks |
| title_full_unstemmed | Study on Strength Prediction and Material Scheme Optimization for Modified Red Mud Based on Artificial Neural Networks |
| title_short | Study on Strength Prediction and Material Scheme Optimization for Modified Red Mud Based on Artificial Neural Networks |
| title_sort | study on strength prediction and material scheme optimization for modified red mud based on artificial neural networks |
| topic | modified red mud optimization strength prediction BP neural network solidifying-agent ratio water content |
| url | https://www.mdpi.com/2075-5309/14/11/3544 |
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