Prediction Method of TBM Tunneling Parameters Based on Bi-GRU-ATT Model

Qinglong Zhang,Chen Guo,Anmin Cai,Chong Jiao,Boyu Yang,Yanwen Zhu,Mingfeng Lei

doi:10.1155/2022/3743472

Abstract

With tunnel boring machines (TBMs) widely used in tunnel construction, the adaptable adjustment of TBM operating status has become a research focus. Since the prediction of tunnel geological conditions is still challenging before excavating, the prediction of important TBM operating parameters plays an important role in the research on TBM adaptable adjustment. This paper proposes an intelligent prediction method of TBM tunneling parameters based on bidirectional gate recurrent unit incorporating attention mechanism (Bi-GRU-ATT) and selects a complete tunneling cycle to predict the tunneling parameters of the TBM complete tunneling cycle. Relying on the TBM3 bid section of Jilin Water Supply Project, 21 key parameters of the complete tunneling cycle are selected as the input features of the model to realize the prediction of four tunneling parameters in the complete driving cycle section of TBM. Compared with the Bi-GRU, GRU, and Long Short-Term Memory (LSTM) models, it can be seen that the Bi-GRU-ATT model has a goodness of fit for predicting TBM tunneling parameters above 0.92, and the average absolute percentage error is less than 1.8%. The results show that the prediction method of TBM tunneling parameters based on Bi-GRU-ATT model proposed in this paper has stronger learning and prediction capabilities. This prediction method provides a more feasible auxiliary intelligent decision-making method for TBM aided intelligent construction.

Highlights

Tunnel boring machine (TBM) integrating the functions of excavation, support, slag discharge, and transportation is one of the most advanced types of equipment for a variety of tunnel constructions, e.g., traffic, municipal, water conservancy, water supply, and gas transmission pipelines [1]
The adaptability of TBM is often limited by complex geological conditions, the poor matching of tunneling parameters and rock mass state parameters, and the high requirements on the experience of construction personnel, making it difficult to effectively solve the prediction of TBM tunneling parameters [3, 4]. erefore, correct evaluation and prediction of TBM tunneling parameters are important issues in efficient tunnel excavation, which is of great significance for ensuring the safety and efficient construction of TBM
In the early empirical method research, Krause [12,13,14] proposed an empirical model for TBM load prediction, which is of great significance to TBM load calculation

Summary

Introduction

Tunnel boring machine (TBM) integrating the functions of excavation, support, slag discharge, and transportation is one of the most advanced types of equipment for a variety of tunnel constructions, e.g., traffic, municipal, water conservancy, water supply, and gas transmission pipelines [1]. Erefore, they cannot realize the real-time analysis of TBM tunnels To solve this problem, deep learning methods such as recurrent neural networks (RNN) [32] have been developed to model the time series data in the TBM tunnel. Gao et al [33] compared and analyzed the effects of different deep learning methods on real-time prediction of TBM tunneling parameters, and the results showed that as an improvement of the RNN network, the recurrent neural network algorithms with gate operations (such as long-short term memory (LSTM) [34] and gate recurrent unit (GRU) [35]) can well solve the problems of gradient explosion and gradient disappearance in RNN networks, and the prediction effect is better than that of the traditional RNN algorithms. Using Pearson correlation analysis, twenty-one parameters with the highest correlation with the predicted parameters are selected as the input characteristics of the model from 199-dimensional tunneling parameters of the complete tunneling cycle section. e data is processed by constructing a binary state discriminant function, eliminating nonworking state and abnormal data, the complete tunneling cycle is extracted, and the Adam optimizer is used to train the model. e comparative analysis of the BiGRU model, unidirectional LSTM model, and unidirectional GRU model verifies the accuracy and effectiveness of the BiGRU-ATT model in this paper, which has certain guiding significance for TBM intelligent construction

Recurrent Neural Network

Model Establishment

A11 GRU A12

Engineering Data Set

Findings

Model Application and Comparison