Prediction of TBM Penetration Rate Using Fuzzy Logic, Particle Swarm Optimization and Harmony Search Algorithm

Afradi, Alireza; Ebrahimabadi, Arash; Hallajian, Tahereh

doi:10.1007/s10706-021-01982-x

Cited by 15 publications

(4 citation statements)

References 62 publications

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“…However, due to the variability of TBM tunneling parameters and the complexity of geological conditions (Feng et al, 2015;Feng et al, 2022;Yang et al, 2022), it is difficult to quantitatively analyze the interaction law between TBM and rock. Tunneling often relies on human experience to adjust repeatedly, and the real-time matching is poor, and it cannot adapt to the complex geological environment (Zhang et al, 2018a;Afradi et al, 2021). Therefore, accurate and effective real-time prediction of TBM tunneling parameters has become an urgent problem to be solved in the field of tunnel engineering.…”

Section: Introductionmentioning

confidence: 99%

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

Zhang

Zhu

et al. 2022

Front. Earth Sci.

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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.

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Section: Introductionmentioning

confidence: 99%

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

Zhang

Zhu

et al. 2022

Front. Earth Sci.

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“…Moreover, the effectiveness of the intelligent model was verified on Iranian road tunnels. Afradi A et al used the fuzzy logic method to predict the penetration rate of the tunnel boring machine, and the input parameters included compressive strength, density of the rock, and so on [ 14 ]. The results show that the prediction accuracy is better than the traditional mechanistic models.…”

Section: Introductionmentioning

confidence: 99%

Optimizing Driving Parameters of the Jumbo Drill Efficiently with XGBoost-DRWIACO Framework: Applied to Increase the Feed Speed

Guo,

Lin,

et al. 2024

Sensors

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The jumbo drill is a commonly used driving equipment in tunnel engineering. One of the key decision-making issues for reducing tunnel construction costs is to optimize the main driving parameters to increase the feed speed of the jumbo drill. The optimization of the driving parameters is supposed to meet the requirements of high reliability and efficiency due to the high risk and complex working conditions in tunnel engineering. The flaws of the existing optimization algorithms for driving parameter optimization lie in the low accuracy of the evaluation functions under complex working conditions and the low efficiency of the algorithms. To address the above problems, a driving parameter optimization method based on the XGBoost-DRWIACO framework with high accuracy and efficiency is proposed. A data-driven prediction model for feed speed based on XGBoost is established as the evaluation function, which has high accuracy under complex working conditions and ensures the high reliability of the optimized results. Meanwhile, an improved ant colony algorithm based on dimension reduction while iterating strategy (DRWIACO) is proposed. DRWIACO is supposed to improve efficiency by resolving inefficient iterations of the ant colony algorithm (ACO), which is manifested as falling into local optimum, converging slowly and converging with a slight fluctuation in a certain dimension. Experimental results show that the error by the proposed framework is less than 10%, and the efficiency is increased by over 30% compared with the comparison methods, which meets the requirements of high reliability and efficiency for tunnel construction. More importantly, the construction cost is reduced by 19% compared with the actual feed speed, which improves the economic benefits.

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“…Some promising research domains for machine learning in tunnelling are the geological prognosis ahead of the face, the interpretation of monitoring results, automation and maintenance [32]. At present, however, research appears to be focussed on the following topics: prediction of TBM operational parameters [34,[39][40][41][42][43][44][45][46], penetration rate [47][48][49][50][51][52][53][54][55][56][57][58][59][60][61][62][63], porewater pressure [64], ground settlement [65][66][67], disc cutter replacement [68][69][70], jamming risk [71,72] and geological classification [73][74][75][76]). Few authors estimated the face support pressure of TBMs with machine learning [35,52].…”

Section: Introductionmentioning

confidence: 99%

Reinforcement learning for the face support pressure of tunnel boring machines

Soranzo¹,

Guardiani²,

Wu³

2023

Preprint

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In tunnel excavation with boring machines, the tunnel face is supported to avoid collapse and minimise settlement. This article proposes the use of reinforcement learning, specifically the Deep Q-Network algorithm, to predict the face support pressure. The approach is tested both analytically and numerically. By using the soil properties ahead of the tunnel face and the overburden depth as the input, the algorithm is capable of predicting the optimal tunnel face support pressure, adapting to changes in geological and geometrical conditions.

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Prediction of TBM Penetration Rate Using Fuzzy Logic, Particle Swarm Optimization and Harmony Search Algorithm

Cited by 15 publications

References 62 publications

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

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

Optimizing Driving Parameters of the Jumbo Drill Efficiently with XGBoost-DRWIACO Framework: Applied to Increase the Feed Speed

Reinforcement learning for the face support pressure of tunnel boring machines

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