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http://dx.doi.org/10.7474/TUS.2018.28.6.651

Prediction Model for Specific Cutting Energy of Pick Cutters Based on Gene Expression Programming and Particle Swarm Optimization  

Hojjati, Shahabedin (Department of Energy Resources Engineering, Seoul National University)
Jeong, Hoyoung (Department of Energy Resources Engineering, Seoul National University)
Jeon, Seokwon (Department of Energy Resources Engineering, Seoul National University)
Publication Information
Tunnel and Underground Space / v.28, no.6, 2018 , pp. 651-669 More about this Journal
Abstract
This study suggests the prediction model to estimate the specific energy of a pick cutter using a gene expression programming (GEP) and particle swarm optimization (PSO). Estimating the performance of mechanical excavators is of crucial importance in early design stage of tunnelling projects, and the specific energy (SE) based approach serves as a standard performance prediction procedure that is applicable to all excavation machines. The purpose of this research, is to investigate the relationship between UCS and BTS, penetration depth, cut spacing, and SE. A total of 46 full-scale linear cutting test results using pick cutters and different values of depth of cut and cut spacing on various rock types was collected from the previous study for the analysis. The Mean Squared Error (MSE) associated with the conventional Multiple Linear Regression (MLR) method is more than two times larger than the MSE generated by GEP-PSO algorithm. The $R^2$ value associated with the GEP-PSO algorithm, is about 0.13 higher than the $R^2$ associated with MLR.
Keywords
Specific energy; Pick cutter; Gene expression programming (GEP); Particle swarm optimization (PSO);
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