Robustness Analyses and Optimal Sampling Gap of Recurrent Neural Network for Dynamic Matrix Pseudoinversion

Liao, Bolin; Xiang, Qiuhong

doi:10.20965/jaciii.2017.p0778

Cited by 1 publication

(1 citation statement)

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“…In addition, the construction process has the influence of natural weather, such as drought and rain, and the stress of the terrain structure will change. Due to the limited knowledge of the author, further study and exploration are needed [30,31].…”

Section: Discussionmentioning

confidence: 99%

Forecast of Ground Deformation Caused by Tunnel Excavation Based on Intelligent Neural Network Model

Jia

Meng

et al. 2022

Mobile Information Systems

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With the development of science and technology, the demand for traffic has increased, and the requirements for tunnel excavation have become more and more stringent. Tunnel excavation is an important traffic construction engineering technology. Due to the influence of many factors in the excavation process, surface settlement or deformation will inevitably occur, so its deformation must be predicted in real time to prevent safety accidents and property losses. The previous numerical methods and neural network methods cannot accurately predict in real time, and the intelligent neural network model can more accurately predict the deformation of the ground because of the characteristics of adaptive organizational learning according to different situations and different environments. This article aims to study and design an intelligent neural network model to predict and calculate the amount of ground deformation caused by the tunnel excavation process. An intelligent neural network model with more accurate prediction is proposed, and simulation experiments are carried out on tunnel excavation of different terrains, and the accuracy of the model for predicting the deformation amount is calculated. The experimental results show that the prediction error range of the model is 10 times smaller than that of the traditional neural network. The prediction accuracy of this model is above 95%, and the volatility rate of prediction accuracy is lower than 11%, while the volatility rate of traditional prediction accuracy is even more than 365%. The intelligent neural network model can effectively predict the deformation of tunnel excavation.

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Section: Discussionmentioning

confidence: 99%