Estimating Maximum Surface Settlement Caused by EPB Shield Tunneling Utilizing an Intelligent Approach
To control tunneling risk, the prediction of the surface settlement rate induced by shield tunneling using earth pressure balance plays a crucial role. To achieve this, ten independent variables were identified that can affect the amount of settlement. The nonlinear relationship between maximum grou...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-04-01
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| Series: | Buildings |
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| Online Access: | https://www.mdpi.com/2075-5309/13/4/1051 |
| _version_ | 1827745591413702656 |
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| author | Tohid Moghtader Ahmad Sharafati Hosein Naderpour Morteza Gharouni Nik |
| author_facet | Tohid Moghtader Ahmad Sharafati Hosein Naderpour Morteza Gharouni Nik |
| author_sort | Tohid Moghtader |
| collection | DOAJ |
| description | To control tunneling risk, the prediction of the surface settlement rate induced by shield tunneling using earth pressure balance plays a crucial role. To achieve this, ten independent variables were identified that can affect the amount of settlement. The nonlinear relationship between maximum ground surface settlements and ten influential independent variables was considered in artificial neural network (ANN) models. A total of 150 genuine datasets derived from the Southern Development Section of the Tehran Metro Line 6 project were used to train, validate, and test ANN techniques. Hence, the ground surface settlements of the mentioned project were predicted by the most accurate back propagation ANN technique. Ultimately, the importance level of different influential parameters on ground settlement at tunneling is relatively determined based on the results of the optimal neural network. The results used in this paper to evaluate the relative importance of each variable involved in the rate of ground surface settlement demonstrate that the parameters of grout injection and permeability equivalent to the proportions of approximately 16.91% and 5.07% have the highest and lowest impact, successively. |
| first_indexed | 2024-03-11T05:09:41Z |
| format | Article |
| id | doaj.art-0336500b05bd44b8a6f352230ac00cac |
| institution | Directory Open Access Journal |
| issn | 2075-5309 |
| language | English |
| last_indexed | 2024-03-11T05:09:41Z |
| publishDate | 2023-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Buildings |
| spelling | doaj.art-0336500b05bd44b8a6f352230ac00cac2023-11-17T18:36:47ZengMDPI AGBuildings2075-53092023-04-01134105110.3390/buildings13041051Estimating Maximum Surface Settlement Caused by EPB Shield Tunneling Utilizing an Intelligent ApproachTohid Moghtader0Ahmad Sharafati1Hosein Naderpour2Morteza Gharouni Nik3Department of Civil Engineering, Science and Research Branch, Islamic Azad University, Tehran, IranDepartment of Civil Engineering, Science and Research Branch, Islamic Azad University, Tehran, IranFaculty of Civil Engineering, Semnan University, Semnan, IranSchool of Railway Engineering, Iran University of Science and Technology (IUST), Tehran, IranTo control tunneling risk, the prediction of the surface settlement rate induced by shield tunneling using earth pressure balance plays a crucial role. To achieve this, ten independent variables were identified that can affect the amount of settlement. The nonlinear relationship between maximum ground surface settlements and ten influential independent variables was considered in artificial neural network (ANN) models. A total of 150 genuine datasets derived from the Southern Development Section of the Tehran Metro Line 6 project were used to train, validate, and test ANN techniques. Hence, the ground surface settlements of the mentioned project were predicted by the most accurate back propagation ANN technique. Ultimately, the importance level of different influential parameters on ground settlement at tunneling is relatively determined based on the results of the optimal neural network. The results used in this paper to evaluate the relative importance of each variable involved in the rate of ground surface settlement demonstrate that the parameters of grout injection and permeability equivalent to the proportions of approximately 16.91% and 5.07% have the highest and lowest impact, successively.https://www.mdpi.com/2075-5309/13/4/1051EPB shieldtunnelsettlement predictionBP neural networksensitivity analysis |
| spellingShingle | Tohid Moghtader Ahmad Sharafati Hosein Naderpour Morteza Gharouni Nik Estimating Maximum Surface Settlement Caused by EPB Shield Tunneling Utilizing an Intelligent Approach Buildings EPB shield tunnel settlement prediction BP neural network sensitivity analysis |
| title | Estimating Maximum Surface Settlement Caused by EPB Shield Tunneling Utilizing an Intelligent Approach |
| title_full | Estimating Maximum Surface Settlement Caused by EPB Shield Tunneling Utilizing an Intelligent Approach |
| title_fullStr | Estimating Maximum Surface Settlement Caused by EPB Shield Tunneling Utilizing an Intelligent Approach |
| title_full_unstemmed | Estimating Maximum Surface Settlement Caused by EPB Shield Tunneling Utilizing an Intelligent Approach |
| title_short | Estimating Maximum Surface Settlement Caused by EPB Shield Tunneling Utilizing an Intelligent Approach |
| title_sort | estimating maximum surface settlement caused by epb shield tunneling utilizing an intelligent approach |
| topic | EPB shield tunnel settlement prediction BP neural network sensitivity analysis |
| url | https://www.mdpi.com/2075-5309/13/4/1051 |
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