Prediction of tunnel boring machine penetration rate using ant colony optimization, bee colony optimization and the particle swarm optimization, case study: Sabzkooh water conveyance tunnel

Abstract

Purpose. The purpose of this study is to use a novel approach to estimate the tunnel boring machine (TBM) penetration rate in diverse ground conditions. Methods. The methods used in this study include ant colony optimization (ACO), bee colony optimization (BCO) and the particle swarm optimization (PSO). Moreover, a comprehensive database was created based on machine performance using penetration rate (m/h) as an output parameter – as well as intact rock and rock mass parameters including uniaxial compressive strength (UCS) (MPa), Brazilian tensile strength (BTS) (MPa), rock quality designation (RQD) (%), cohesion (MPa), elasticity modulus (GPa), Poisson’s ratio, density(g/cm3), joint angle (deg.) and joint spacing (m) as input parameters. Findings. Results showed that the analyses yielded several realistic and reliable models for predicting penetration rate of TBMs. ACO model has R2 = 0.8830 and RMSE = 0.6955, BCO model has R2 = 0.9367 and RMSE = 0.5113 and PSO model has R2 = 0.9717 and RMSE = 0.3418. Originality. Prediction of TBM penetration rate using these methods has been carried out in the Sabzkooh water conveyance tunnel for the first time. Practical implications. According to the results, all three approaches are very effective but PSO yields more precise and realistic findings than other methods.