Prediction of the Penetration Rate and Number of Consumed Disc Cutters of Tunnel Boring Machines (TBMs) Using Artificial Neural Network (ANN) and Support Vector Machine (SVM)—Case Study: Beheshtabad Water Conveyance Tunnel in Iran

Afradi, Alireza; Ebrahimabadi, Arash; Hallajian, Tahereh

doi:10.3233/ajw190006

Cited by 30 publications

(17 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For future works, it is suggested to use other novel heuristic algorithms such as shark smell optimization and shuffled frog leaping algorithm to predict the penetration rate of the tunnel boring machine. [93] TBM penetration rate (m/h) 0.72 0.18 Queens water tunnel Yagiz and Karahan [14] TBM penetration rate (m/h) 0.66 0.20 Queens water tunnel Afradi et al [91] TBM penetration rate (m/h) 0.97 0.48 Beheshtabad water conveyance tunnel Adoko et al [1] TBM penetration rate (m/h) 0.66 0.22 Queens water tunnel Afradi et al [29] TBM penetration rate (m/h) 0.97 0.34 Sabzkooh water conveyance tunnel…”

Section: Discussionmentioning

confidence: 99%

“…In this model, a single-point, two-point and gene combination are used. It is preferable that the two-point combination is able to turn the unencoded areas into chromosomes more extensively [89][90][91]. The general structure of the computation performed in the GEP algorithm to arrive at the answer considers the following:…”

Section: Gene Expression Programming (Gep)mentioning

confidence: 99%

See 1 more Smart Citation

Comparison of artificial neural networks (ANN), support vector machine (SVM) and gene expression programming (GEP) approaches for predicting TBM penetration rate

Afradi

Ebrahimabadi

2020

SN Appl. Sci.

Self Cite

View full text Add to dashboard Cite

Section: Discussionmentioning

confidence: 99%

Section: Gene Expression Programming (Gep)mentioning

confidence: 99%

Comparison of artificial neural networks (ANN), support vector machine (SVM) and gene expression programming (GEP) approaches for predicting TBM penetration rate

Afradi

Ebrahimabadi

2020

SN Appl. Sci.

Self Cite

View full text Add to dashboard Cite

“…Statistical methods, such as MRA, SPSS, together with ANNs, are conducted to predict TBM performance [58,59]. For the penetration rate of TBM, the prediction accuracy of SVM, LMRA, and ANN are compared [60,61]. In order to predict the penetration rate and advance rate of TBM, Armaghani et al utilized an ANN, PSO-ANN, and ICA-ANN to make the prediction and compared the prediction ability of these methods [30,39].…”

Section: Roadheader Performance and Tbm Performancementioning

confidence: 99%

“…Nevertheless, the abovementioned conclusions can provide a reference in the tunnel engineering field. [21] WNN>ANN [22] GP>SVM>ANN [78] SVMFF>ANNFF [79] MLP>MRA [76] PSO-ANN>ANN [93] ANFIS>CFT>ANN [5] FLM>BPNN>MRA [20] ICA-ANN>ANN>LMRA [94] TL>RNN>SVM [49] PSO-SVR>PSO-BPNN>PSO-ELM [53] Stability BPNN>LRM [56] MLP>RBFNN [58] TBM performance BPNN>NMRA [59] ANN>SPSS [31] ANN>MRA [60] ANN>LMRA [30] ICA-ANN>PSO-ANN>ANN [61] SVM>ANN [39] PSO-ANN>ICA-ANN>ANN [24] Geological conditions ANN>XGBoost, CatBoost, RF, DT, SVR, KNN, BLR [18] Overbreak ANN>NMRA>LMRA [67] ANFIS>FLM>ANN>SVM>NMRA>L MRA [38] GA-ANN>ANN [68] ABC-ANN>ANN [40] ABC-ANN>ANN [11] Tunnel convergence MLP>RBFNN>MRA [36] SVM>ANN [35] MLP>MARS [69] MLP>SPSS [70] Rockburst and flying rocks PSO-ANN>ICA-ANN>GA-ANN Note: The '>' means the performance of the left model outperforms the right one.…”

Section: A Characteristics Of Ann-based Modelsmentioning

confidence: 99%

Application of Artificial Neural Network in Tunnel Engineering: A Systematic Review

Wang

Wei

et al. 2020

IEEE Access

View full text Add to dashboard Cite

Due to the lack of living space and the increase in population, there has been a construction boom in the underground space to improve the quality of human life. Tunnel engineering plays a vital role in the development of underground space. In addition to traditional methods, some intelligent methods such as artificial neural networks (ANNs) have been applied to various problems in the tunnel domain in recent years. This paper systematically reviews the application of ANNs from different aspects of tunnel engineering. It reveals that the backpropagation algorithm (BPA) and Levenberg-Marquardt algorithm (LMA) are the most widely used. Due to the limitations of some original models, some scholars use optimization algorithms such as particle swarm optimization (PSO) and genetic algorithm (GA) to optimize the original ANNs to obtain better prediction results. A comparison between the ANN-based methods and methods like statistical methods is conducted. Finally, the following conclusions can be drawn: (1) The recommended ratio of the training set and test set is 3:1; (2) The advantage of optimized ANNs is not apparent when the optimization algorithm varies. Additionally, the performance of ANNs is always better than that of statistical methods.

show abstract

“…In the SVM, the principles of the learning machine and creating a model is the only data placed in the support vectors [19]. This algorithm is not sensitive to the other points, and it aims to find an optimal line of data so that it has the maximum allowable distance with regard to all classifications (the support vectors) [20]. 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.…”

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

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Prediction of the Penetration Rate and Number of Consumed Disc Cutters of Tunnel Boring Machines (TBMs) Using Artificial Neural Network (ANN) and Support Vector Machine (SVM)—Case Study: Beheshtabad Water Conveyance Tunnel in Iran

Cited by 30 publications

References 8 publications

Comparison of artificial neural networks (ANN), support vector machine (SVM) and gene expression programming (GEP) approaches for predicting TBM penetration rate

Comparison of artificial neural networks (ANN), support vector machine (SVM) and gene expression programming (GEP) approaches for predicting TBM penetration rate

Application of Artificial Neural Network in Tunnel Engineering: A Systematic Review

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

Contact Info

Product

Resources

About