Prediction of TBM Penetration Rate Using Support Vector Machine

Afradi, Alireza; Ebrahimabadi, Arash; Hallajian, Tahereh

doi:10.26895/geosaberes.v11i0.1048

Cited by 5 publications

(2 citation statements)

References 11 publications

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“…The correlation between predicted and actual TBM PR values for the ANN is plotted in Figure 15(b1,b2). In the past, ANN-based studies have shown R 2 values of 0.66 [8], 0.82 [62], 0.83 [63], 0.90 [53], and 0.94 [64]. Furthermore, the ANN algorithm has been used to perform different training functions (lbfgs, sgd, and ADAM) to predict the TBM performance, as shown in Figure 16.…”

Section: Resultsmentioning

confidence: 99%

Digital Twin-Driven Framework for TBM Performance Prediction, Visualization, and Monitoring through Machine Learning

Latif,

Sharafat,

Seo

2023

Applied Sciences

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The rapid development in underground infrastructure is encouraging faster and more modern ways, such as TBM tunneling, to meet the needs of the world. However, tunneling activities generate complex and heterogeneous data, which makes it difficult to visualize the performance of a project. Advancements in information technology, such as digital twins and machine learning, provide platforms for digital demonstration, visualization, and system performance monitoring of such data. Therefore, this study proposes a digital twin-driven framework for TBM performance prediction through machine learning, visualization, and monitoring. This novel approach integrates machine learning and real-time performance data to predict, visualize, and monitor the status of the tunnel construction progress. A digital twin virtual model of TBM was constructed based on TBM design parameters, the input parameter, boring energy, RPM, torque, thrust force, speed, gripper pressure, total revolution, and Q-value provided to SVR and ANN models to predict the TBM AR and PR, and TBM daily progress was visualized continuously. The predictive performance indices R2 (0.97) and RMSE (0.011) were estimated for AR prediction, showing the accuracy of the proposed model. To demonstrate the proposed framework, this study shows the its effectiveness. By implementing this framework, stakeholders can minimize the risk associated with the cost and schedule of a tunneling project by simultaneously visualizing and monitoring the performance of TBMs through digital twin and machine learning algorithms.

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Section: Resultsmentioning

confidence: 99%

Digital Twin-Driven Framework for TBM Performance Prediction, Visualization, and Monitoring through Machine Learning

Latif,

Sharafat,

Seo

2023

Applied Sciences

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“…In a simple way, the support vectors are a set of points in ndimensional space of data which determine the border of classifications, so that the data classification could be carried out. The classification output can be changed as a result of moving one of the vectors [21]. The specification of SVM used in predicting penetration rate was listed in Table 2.…”

Section: Support Vector Machine (Svm)mentioning

confidence: 99%

Prediction of TBM Penetration Rate of Water Conveyance Tunnels in Iran Using Modern Methods

Afradi

Ebrahimabadi

Hallajian

2020

CEJ

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TBM has been used extensively in civil engineering activities and plays an important role in tunnelling projects. Hence, the penetration rate of these machines plays a crucial role in the success of their application. Therefore, in order to predict the TBM penetration rate in this study in several water conveyance tunnels including the tunnels of Karaj, Ghomrood, Golab, Nosoud and Sabzkooh, four intelligence techniques including multiple linear and nonlinear regression analysis, Gene Expression Programming (GEP) method, and Support Vector Machine (SVM) were applied. The obtained values of R2 and RMSE included 0.43 and 3.08 for linear regression, 0.68 and 2.3 for nonlinear regression, 0.74 and 2.09 for GEP method and 0.97 and 0.6 for SVM method, respectively which were utilized to predict TBM penetration rate. By investigating the tunnels database, the results indicated that the SVM method had the most accurate prediction of penetration rate (in terms of R2 and RMSE) and the maximum amount of R2 and the minimum amount of RMSE among all predictive modelings. Finally, respecting the amount of R2 and RMSE, the other methods like GEP method, nonlinear regression, and linear regression are listed to have the required accuracy in predicting penetration rate.

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Process Development for Rare Earth Elements Recovery and Struvite Production from Biocrudes

Li,

Zhang

2024

J. Sustain. Metall.

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Prediction of TBM Penetration Rate Using Support Vector Machine

Cited by 5 publications

References 11 publications

Digital Twin-Driven Framework for TBM Performance Prediction, Visualization, and Monitoring through Machine Learning

Digital Twin-Driven Framework for TBM Performance Prediction, Visualization, and Monitoring through Machine Learning

Prediction of TBM Penetration Rate of Water Conveyance Tunnels in Iran Using Modern Methods

Process Development for Rare Earth Elements Recovery and Struvite Production from Biocrudes

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