Surface settlement modelling using neural network 2
With an ever-increasing population and scare land, tunnelling underground has emerged as a feasible alternative for providing public works while optimising space use. Ground displacements generated by tunnelling construction is very critical since existing infrastructure and high-rise structures in...
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| Format: | Final Year Project (FYP) |
| Language: | English |
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Nanyang Technological University
2021
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| Online Access: | https://hdl.handle.net/10356/149956 |
| _version_ | 1811695207796703232 |
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| author | Khoo, Wei Yang |
| author2 | Zhao Zhiye |
| author_facet | Zhao Zhiye Khoo, Wei Yang |
| author_sort | Khoo, Wei Yang |
| collection | NTU |
| description | With an ever-increasing population and scare land, tunnelling underground has emerged as a feasible alternative for providing public works while optimising space use. Ground displacements generated by tunnelling construction is very critical since existing infrastructure and high-rise structures in the urban environment can be very sensitive to any ground movements. The traditional approaches for predicting displacement are focused on empirical studies, which have limitations and does not often provide an accurate estimate due to the complexity and unknown influences. This report will study the use of Artificial Neural Network (ANN) to create a model, capable of predicting settlement. Different analyses will be carried out to obtain the important input parameters and iterations will be done to ensure the accuracy and reliability of the model. With a good model developed, it can be used for future studies and also be tested in situations where the prediction of settlement is required. |
| first_indexed | 2024-10-01T07:19:48Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/149956 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T07:19:48Z |
| publishDate | 2021 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1499562021-05-20T05:01:51Z Surface settlement modelling using neural network 2 Khoo, Wei Yang Zhao Zhiye School of Civil and Environmental Engineering CZZHAO@ntu.edu.sg Engineering::Civil engineering::Geotechnical With an ever-increasing population and scare land, tunnelling underground has emerged as a feasible alternative for providing public works while optimising space use. Ground displacements generated by tunnelling construction is very critical since existing infrastructure and high-rise structures in the urban environment can be very sensitive to any ground movements. The traditional approaches for predicting displacement are focused on empirical studies, which have limitations and does not often provide an accurate estimate due to the complexity and unknown influences. This report will study the use of Artificial Neural Network (ANN) to create a model, capable of predicting settlement. Different analyses will be carried out to obtain the important input parameters and iterations will be done to ensure the accuracy and reliability of the model. With a good model developed, it can be used for future studies and also be tested in situations where the prediction of settlement is required. Bachelor of Engineering (Civil) 2021-05-20T05:01:51Z 2021-05-20T05:01:51Z 2021 Final Year Project (FYP) Khoo, W. Y. (2021). Surface settlement modelling using neural network 2. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/149956 https://hdl.handle.net/10356/149956 en application/pdf Nanyang Technological University |
| spellingShingle | Engineering::Civil engineering::Geotechnical Khoo, Wei Yang Surface settlement modelling using neural network 2 |
| title | Surface settlement modelling using neural network 2 |
| title_full | Surface settlement modelling using neural network 2 |
| title_fullStr | Surface settlement modelling using neural network 2 |
| title_full_unstemmed | Surface settlement modelling using neural network 2 |
| title_short | Surface settlement modelling using neural network 2 |
| title_sort | surface settlement modelling using neural network 2 |
| topic | Engineering::Civil engineering::Geotechnical |
| url | https://hdl.handle.net/10356/149956 |
| work_keys_str_mv | AT khooweiyang surfacesettlementmodellingusingneuralnetwork2 |