NDLI: Calculation of surface settlements caused by EPBM tunneling using artificial neural network, SVM, and Gaussian processes

Content Provider	Springer Nature Link
Author	Ocak, Ibrahim Seker, Sadi Evren
Copyright Year	2013
Abstract	Increasing demand on infrastructures increases attention to shallow soft ground tunneling methods in urbanized areas. Especially in metro tunnel excavations, due to their large diameters, it is important to control the surface settlements observed before and after excavation, which may cause damage to surface structures. In order to solve this problem, earth pressure balance machines (EPBM) and slurry balance machines have been widely used throughout the world. There are numerous empirical, analytical, and numerical analysis methods that can be used to predict surface settlements. But substantially fewer approaches have been developed for artificial neural network-based prediction methods especially in EPBM tunneling. In this study, 18 different parameters have been collected by municipal authorities from field studies pertaining to EPBM operation factors, tunnel geometric properties, and ground properties. The data source has a preprocess phase for the selection of the most effective parameters for surface settlement prediction. This paper focuses on surface settlement prediction using three different methods: artificial neural network (ANN), support vector machines (SVM), and Gaussian processes (GP). The success of the study has decreased the error rate to 13, 12.8, and 9, respectively, which is relatively better than contemporary research.
Starting Page	1263
Ending Page	1276
Page Count	14
File Format	PDF
ISSN	18666280
Journal	Environmental Earth Sciences
Volume Number	70
Issue Number	3
e-ISSN	18666299
Language	English
Publisher	Springer Berlin Heidelberg
Publisher Date	2013-01-08
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Twin tunnel Prediction of surface settlement EPBM Artificial neural network Gaussian processes Support vector machine Geology
Content Type	Text
Resource Type	Article
Subject	Global and Planetary Change Earth-Surface Processes Soil Science Environmental Chemistry Pollution Geology Water Science and Technology

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