Application of several optimization techniques for estimating TBM advance rate in granitic rocks
By Danial Jahed Armaghani, Mohammadreza Koopialipoor, Aminaton Marto, Saffet Yagiz. Journal of Rock Mechanics and Geotechnical Engineering, 2019
222 citations recorded by the source corpus.
Abstract This study aims to develop several optimization techniques for predicting advance rate of tunnel boring machine (TBM) in different weathered zones of granite. For this purpose, extensive field and laboratory studies have been conducted along the 12,649 m of the Pahang – Selangor raw water transfer tunnel in Malaysia. Rock properties consisting of uniaxial compressive strength (UCS), Brazilian tensile strength (BTS), rock mass rating (RMR), rock quality designation (RQD), quartz content (q) and weathered zone as well as machine specifications including thrust force and revolution per minute (RPM) were measured to establish comprehensive datasets for optimization. Accordingly, to estimate the advance rate of TBM, two new hybrid optimization techniques, i.e. an artificial neural network (ANN) combined with both imperialist competitive algorithm (ICA) and particle swarm optimization (PSO), were developed for mechanical tunneling in granitic rocks. Further, the new hybrid optimization techniques were compared and the best one was chosen among them to be used for practice. To evaluate the accuracy of the proposed models for both testing and training datasets, various statistical indices including coefficient of determination (R2), root mean square error (RMSE) and variance account for (VAF) were utilized herein. The values of R2, RMSE, and VAF ranged in 0.939–0.961, 0.022–0.036, and 93.899–96.145, respectively, with the PSO-ANN hybrid technique demonstrating the best performance. It is concluded that both the optimization techniques, i.e. PSO-ANN and ICA-ANN, could be utilized for predicting the advance rate of TBMs; however, the PSO-ANN technique is superior.
Subfields: Civil and Structural Engineering, Mechanical Engineering, Ocean Engineering
View Paper: https://www.semanticscholar.org/paper/9ac6dcbac53871fd0da50c0ed0a44e3c48efd302
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Citations in the corpus
- Examining Hybrid and Single SVM Models with Different Kernels to Predict Rock Brittleness (2020, background): “Although multiple linear regression models demonstrated a higher precision level in comparison with the available simple regression models [18,21,22], they are not always robust enough to describe the behavior of complex systems accurately [62,80].”