Please use this identifier to cite or link to this item:
https://doi.org/10.1155/2019/7057612
DC Field | Value | |
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dc.title | Modern Machine Learning Techniques for Univariate Tunnel Settlement Forecasting: A Comparative Study | |
dc.contributor.author | Hu, M. | |
dc.contributor.author | Li, W. | |
dc.contributor.author | Yan, K. | |
dc.contributor.author | Ji, Z. | |
dc.contributor.author | Hu, H. | |
dc.date.accessioned | 2021-11-16T07:25:52Z | |
dc.date.available | 2021-11-16T07:25:52Z | |
dc.date.issued | 2019 | |
dc.identifier.citation | Hu, M., Li, W., Yan, K., Ji, Z., Hu, H. (2019). Modern Machine Learning Techniques for Univariate Tunnel Settlement Forecasting: A Comparative Study. Mathematical Problems in Engineering 2019 : 7057612. ScholarBank@NUS Repository. https://doi.org/10.1155/2019/7057612 | |
dc.identifier.issn | 1024-123X | |
dc.identifier.uri | https://scholarbank.nus.edu.sg/handle/10635/206408 | |
dc.description.abstract | Tunnel settlement commonly occurs during the tunnel construction processes in large cities. Existing forecasting methods for tunnel settlements include model-based approaches and artificial intelligence (AI) enhanced approaches. Compared with traditional forecasting methods, artificial neural networks can be easily implemented, with high performance efficiency and forecasting accuracy. In this study, an extended machine learning framework is proposed combining particle swarm optimization (PSO) with support vector regression (SVR), back-propagation neural network (BPNN), and extreme learning machine (ELM) to forecast the surface settlement for tunnel construction in two large cities of China P.R. Based on real-world data verification, the PSO-SVR method shows the highest forecasting accuracy among the three proposed forecasting algorithms. © 2019 Min Hu et al. | |
dc.publisher | Hindawi Limited | |
dc.rights | Attribution 4.0 International | |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | |
dc.source | Scopus OA2019 | |
dc.type | Article | |
dc.contributor.department | BUILDING | |
dc.description.doi | 10.1155/2019/7057612 | |
dc.description.sourcetitle | Mathematical Problems in Engineering | |
dc.description.volume | 2019 | |
dc.description.page | 7057612 | |
Appears in Collections: | Elements Staff Publications |
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