Tunnel boring machines (TBM) performance prediction: A case study using big data and deep learning

Feng, Shijie; Chen, Zuyu; Luo, Hui; Wang, Shanyong; Zhao, Yufei; Liu, Lipeng; Ling, Daosheng; Jing, Lei

doi:10.1016/j.tust.2020.103636

Cited by 73 publications

(17 citation statements)

References 28 publications

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“…TBMs provide face support pressures up to approximately 200 to 300 kPa in soft soils [104]. At every excavation step, the proposed model searches the optimal support pressure within the interval (50,250) kPa. According to the guidelines [4], based on a limit equilibrium approach for drained conditions, the required support pressure is calculated with…”

Section: Face Support Pressure and Settlementmentioning

confidence: 99%

“…The support pressure p f is comprised in the discrete interval of natural integers (50, 250) kPa and is assigned in 50 kPa steps. Hence, the output layer is a vector with five elements, one for each possible support pressure (50,100,150,200 and 250 kPa). The first and second hidden layers have each 50 neurons.…”

Section: Deep Q-networkmentioning

confidence: 99%

“…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%

See 2 more Smart Citations

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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Section: Face Support Pressure and Settlementmentioning

confidence: 99%

Section: Deep Q-networkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Reinforcement learning for the face support pressure of tunnel boring machines

Soranzo¹,

Guardiani²,

Wu³

2023

Preprint

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“…On the other hand, in the case of the same datasets, Mahdevari et al [15] intended an ML approach, namely support vector regression, to solve a TBM problem. In predicting FPI values, Feng et al [27] and Adoko and Yagiz [28] tried to solve this problem by proposing deep learning and FIS techniques, respectively. A group of other authors tried to solve performance of TBM using other single intelligent approaches like group modeling of data handling, genetic-based and neuro-fuzzy [2,9,[29][30][31].…”

Section: Introductionmentioning

confidence: 99%

A Novel Combination of PCA and Machine Learning Techniques to Select the Most Important Factors for Predicting Tunnel Construction Performance

et al. 2022

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Numerous studies have reported the effective use of artificial intelligence approaches, particularly artificial neural networks (ANNs)-based models, to tackle tunnelling issues. However, having a high number of model inputs increases the running time and related mistakes of ANNs. The principal component analysis (PCA) approach was used in this work to select input factors for predicting tunnel boring machine (TBM) performance, specifically advance rate (AR). A reliable and precise forecast of TBM AR is desirable and critical for mitigating risk throughout the tunnel building phase. The developed PCAs (a total of four PCAs) were used with the artificial bee colony (ABC) method to predict TBM AR. To assess the created PCA-ANN-ABC model’s capabilities, an imperialist competitive algorithm-ANN and regression-based methods for estimating TBM AR were also suggested. To evaluate the artificial intelligence and statistical models, many statistical evaluation metrics were evaluated and generated, including the coefficient of determination (R2). The findings indicate that the PCA-ANN-ABC model (with R2 values of 0.9641 for training and 0.9558 for testing) is capable of predicting AR values with a high degree of accuracy, precision, and flexibility. The modelling approach utilized in this study may be used to other comparable studies involving the solution of engineering challenges.

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“…With the continuous enrichment of sensor types and func-tions, there are numerous variables, also known as parameters or features, available in DT-based modeling for KPI prediction [11], [12]. However, invalid parameters containing irrelevant or redundant information not only increase the overfitting risk and pruning difficulty, but also adversely affect convergence and performance [13]- [15].…”

Section: Introductionmentioning

confidence: 99%

Improved Ensemble Feature Selection Based on DT for KPI Prediction

et al. 2021

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In the production process of large-scale machinery and complex industries, the key performance indicator (KPI) prediction is an essential part of project scheduling and cost estimation. The continuous enrichment of sensor types and functions brings us massive soft-sensing parameters for regression, but also brings severe challenges to algorithm learning. In this paper, an improved ensemble feature selection based on decision tree (EFS-DT) strategy for KPI prediction is developed. On the one hand, the ensemble of multi-criteria filtering results broadens the selector's perspective without the time cost of superposition. On the other hand, credibility and similarity analysis are designed to eliminate the concerns of Dempster's combination rule about conflict. After re-evaluating the variable scores, more highquality variables can be selected to build a more accurate and robust KPI prediction model. Finally, a realistic shield tunnel case in China is used to evaluate the feasibility and effectiveness of proposed approach.

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Tunnel boring machines (TBM) performance prediction: A case study using big data and deep learning

Cited by 73 publications

References 28 publications

Reinforcement learning for the face support pressure of tunnel boring machines

Reinforcement learning for the face support pressure of tunnel boring machines

A Novel Combination of PCA and Machine Learning Techniques to Select the Most Important Factors for Predicting Tunnel Construction Performance

Improved Ensemble Feature Selection Based on DT for KPI Prediction

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