Surface settlement modelling using neural network
With the development of society, the utilization rate of underground space is getting higher and higher. Tunnel projects are gradually increasing. But settlement is the most common problem in tunnel engineering. Many scholars have conducted research on this issue, and they have developed many method...
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| Other Authors: | |
| Format: | Final Year Project (FYP) |
| Language: | English |
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Nanyang Technological University
2020
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| Online Access: | https://hdl.handle.net/10356/145041 |
| _version_ | 1826126616455020544 |
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| author | Chen, Rongxing |
| author2 | Zhao Zhiye |
| author_facet | Zhao Zhiye Chen, Rongxing |
| author_sort | Chen, Rongxing |
| collection | NTU |
| description | With the development of society, the utilization rate of underground space is getting higher and higher. Tunnel projects are gradually increasing. But settlement is the most common problem in tunnel engineering. Many scholars have conducted research on this issue, and they have developed many methods for predicting the settlement. But these methods have some shortcomings, that is, they are too complicated, and the accuracy of prediction is not high.
This research uses neural network to predict tunnel settlement. The neural network model can process a large amount of information like a human brain and is very suitable for non-linear problems.
In this study. There are two ways of neural network analysis. The first method is to find the best performing one by building 16 different hidden neurons models. The second method is to build models with different inputs. The models with 6 input performance best while model with 4 inputs is alternate optimal model. |
| first_indexed | 2024-10-01T06:55:14Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/145041 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T06:55:14Z |
| publishDate | 2020 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1450412020-12-09T05:52:50Z Surface settlement modelling using neural network Chen, Rongxing Zhao Zhiye School of Civil and Environmental Engineering CZZHAO@ntu.edu.sg Engineering::Civil engineering With the development of society, the utilization rate of underground space is getting higher and higher. Tunnel projects are gradually increasing. But settlement is the most common problem in tunnel engineering. Many scholars have conducted research on this issue, and they have developed many methods for predicting the settlement. But these methods have some shortcomings, that is, they are too complicated, and the accuracy of prediction is not high. This research uses neural network to predict tunnel settlement. The neural network model can process a large amount of information like a human brain and is very suitable for non-linear problems. In this study. There are two ways of neural network analysis. The first method is to find the best performing one by building 16 different hidden neurons models. The second method is to build models with different inputs. The models with 6 input performance best while model with 4 inputs is alternate optimal model. Bachelor of Engineering (Civil) 2020-12-09T05:52:50Z 2020-12-09T05:52:50Z 2020 Final Year Project (FYP) https://hdl.handle.net/10356/145041 en GE49 application/pdf Nanyang Technological University |
| spellingShingle | Engineering::Civil engineering Chen, Rongxing Surface settlement modelling using neural network |
| title | Surface settlement modelling using neural network |
| title_full | Surface settlement modelling using neural network |
| title_fullStr | Surface settlement modelling using neural network |
| title_full_unstemmed | Surface settlement modelling using neural network |
| title_short | Surface settlement modelling using neural network |
| title_sort | surface settlement modelling using neural network |
| topic | Engineering::Civil engineering |
| url | https://hdl.handle.net/10356/145041 |
| work_keys_str_mv | AT chenrongxing surfacesettlementmodellingusingneuralnetwork |