Advance prediction method for rock mass stability of tunnel boring based on deep neural network of time series. (May 2022)

• Record Type: Journal Article
• Authors: Junzhou, Huo; Guopeng, Jia; Bin, Liu; Shiwu, Nie; Junbo, Liang; Hanyang, Wu
• Is Part Of: Proceedings of the Institution of Mechanical Engineers. Volume 236:Number 10(2022)
• Page Start: 5618
• Page End: 5633
• Publication Date: 2022-05
• Subjects: Tunnel boring machines -- data mining -- deep neural network -- long short-term memory structure -- the advance prediction of rock mass category; Mechanical engineering -- Periodicals; 621.05
• DOI: 10.1177/09544062211061682
• Languages: English
• ISSNs: 0954-4062

Abstract

Geological layers excavated using tunnel boring machines are buried deeply and sampled difficultly, and the geological behavior exhibits high diversity and complexity. Excavating in uncertain geology conditions bears the risks of excessive damage to the equipment and facing geologic hazards. Many scholars have used various signals to predict the advance geology conditions, but accurate prediction of these conditions in real-time and without effecting operations has not been realized yet. In this article, based on a large amount of corresponding data, an advance prediction model of the rock mass category (RMC) is formulated. First, the problem is divided into two parts, which are modeled separately to reduce the complexity of design and training. Then, the two models are combined in a pre-trained model, which is retrained to as the final prediction model to avoid the problem of error accumulation. The final model can predict the advance RMC in real-time and without affecting operations. The accuracy of the prediction model reaches 99% at an advance time of 60 min. The advance RMC can be used to guide the selection of support modes and control parameters without additional detection equipment and excavation down-time.