Prediction Method of TBM Tunneling Parameters Based on PSO-Bi-LSTM Model

Abstract

With the wide application of full-face rock tunnel boring machine (TBM) in tunnel construction, the self-adaptive adjustment of TBM tunneling parameters is of great significance for the safety and efficiency of TBM tunnelling. Aiming at the shortcomings of the current TBM data mining capability and optimization methods of tunneling parameters, this paper proposes a prediction method of TBM tunneling parameters based on particle swarm optimization-bi-directional long short-term memory (PSO-Bi-LSTM) model, which selects the complete tunneling cycle data to predict the TBM tunneling parameters, and uses a number of numerical methods such as binary state discriminant function and 3σ criterion to preprocess the operation data of TBM3 bid section of Songhua River water conveyance project. By comparing with the Bi-LSTM model and evaluating the prediction effect under different surrounding rock levels, the applicability and prediction performance of the model to different strata are verified. The results show that the prediction accuracy of the model is proportional to the surrounding rock grade. Compared with the Bi-LSTM, the overall prediction effect of the proposed PSO-Bi-LSTM model is better, which can assist the intelligent construction of TBM with similar geological conditions.

An Empirical Modal Decomposition-Improved Whale Optimization Algorithm-Long Short-Term Memory Hybrid Model for Monitoring and Predicting Water Quality Parameters

“…The significant impact of the hyperparameters on a neural network's performance is demonstrated in the literature [37][38][39][40]. In this paper, the use of IWOA to determine the hyperparameters of the LSTM improves the prediction accuracy of the model, which is consistent with the findings in the above literature.…”

Intelligent tunnelling robot system for deep-buried long tunnels

“…According to previous studies (Zhang et al, 2022a;Zhang et al, 2022b), six key parameters were selected for the optimisation of decision-making: the cutterhead speed, cutterhead torque, cutterhead power, penetration, propulsion speed, and total propulsion force. Most of these parameters describe the relationship between them through the rock-mechanical interaction model.…”

“…However, the TBM is extremely sensitive to geological changes and is excessively dependent on the operator experience (Armetti et al, 2018). A manual operation relies on experience, and different operators have varying skill levels, which creates quality control issues in addition to similar OPEN ACCESS EDITED BY accidents repeatedly occurring (Mahdevari et al, 2014;Zhang et al, 2022a). Specifically, when encountering stratum changes or complex geological conditions, manual operations may cause jamming, collapsing, and other significant consequences owing to the inability to make effective adjustments in a timely manner.…”

“…The prediction of TBM performance mainly includes the tunnelling speed, construction speed, operation utilisation rate, and tool wear of the TBM. More than 30 theoretical and empirical models have been developed since the 1970s (Zhang et al, 2022a). The CSM models proposed by the Colorado School of Mines include the Sanio, dimensional, and improved CSM models, which are theoretical prediction models (Goshtasbi et al, 2009;Pan et al, 2021).…”

Classification and Prediction of Rock Mass Boreability Based on Daily Advancement during TBM Tunneling

“…Therefore, it is necessary to identify the geological conditions and predict the behavior of the surrounding rock in front of the cutterhead in TBM operations [22]. The relationship between the machine parameters and penetration rate can offer some information about the rock mass at the face and identify potential issues [1,[23][24][25][26]. Analysis of TBM operational parameters can offer some insight into the behavior of the surrounding rocks during the tunneling process.…”