Classification of surrounding rocks in tunnel based on Gaussian process machine learning
- Resource Type
- Conference
- Authors
- Yan Zhang; Guoshao Su; Liubin Yan
- Source
- 2011 International Conference on Electric Technology and Civil Engineering (ICETCE) Electric Technology and Civil Engineering (ICETCE), 2011 International Conference on. :3971-3974 Apr, 2011
- Subject
- Components, Circuits, Devices and Systems
Engineered Materials, Dielectrics and Plasmas
General Topics for Engineers
Power, Energy and Industry Applications
Fields, Waves and Electromagnetics
Geoscience
Communication, Networking and Broadcast Technologies
Computing and Processing
Rocks
Gaussian processes
Artificial neural networks
Support vector machines
Machine learning
Stability analysis
Water resources
tunnel
surrounding rocks classification
gaussian process
machine learning
- Language
Classification of surrounding rocks in tunnel is very important for design and construction. Aiming to the fact that it is still difficult to reasonably determine the classification of surrounding rocks in tunnel, the model based on Gaussian process machine learning is proposed for classifying surrounding rocks. With the help of simple learning process, the uncertain mapping relationship between classification of surrounding rocks and its influencing factors is established by Gaussian process for binary classification model. The model is applied to a real engineering. The results of case study show that Gaussian process for binary classification model is feasible and has the same results with artificial neural networks and support vector machine. Nevertheless, compared with artificial neural networks and support vector machine, it has attractive merit of self-adaptive parameters determination.