Open AccessMaximum surface settlement prediction in EPB TBM tunneling using soft computing techniques
Author(s) -
Hadi Samadi,
Jafar Hassanpour,
Ebrahim Farrokh
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1973/1/012195
Subject(s) - settlement (finance) , soft computing , regression analysis , engineering , geotechnical engineering , computer science , artificial intelligence , artificial neural network , machine learning , world wide web , payment
Earth pressure balance (EPB) TBMs are commonly used for soft ground tunneling in urban areas. In metro tunnels’ excavation, designing a comprehensive monitoring system to control surface settlement is essential to prevent damage to surface structures. The present study aims to develop new prediction models to estimate the ground surface settlement using two soft computing techniques, SVM and ANN-MLP, and a multiple variable regression model to develop the new empirical formulas. The TBM operational parameters collected from the Tehran metro line 6, South extension (TML6-SE) project have been applied to confirm the provided models. In the data analysis process, the relationships between various parameters (torque index, thrust index, and earth pressure) and the ground surface settlement are investigated. Moreover, several statistical evaluation criteria are implemented to evaluate the performance of the developed models. The results show that the predicted values are in good agreement with the real data. The results can be used for similar ground and TBM tunneling conditions.