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Title: Modern Machine Learning Techniques for Univariate Tunnel Settlement Forecasting: A Comparative Study
Authors: Hu, M.
Li, W.
Yan, K. 
Ji, Z.
Hu, H.
Issue Date: 2019
Publisher: Hindawi Limited
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.
Rights: Attribution 4.0 International
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.
Source Title: Mathematical Problems in Engineering
ISSN: 1024-123X
DOI: 10.1155/2019/7057612
Rights: Attribution 4.0 International
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