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

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 parameteras 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/cm 3 ), 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 R 2 = 0.8830 and RMSE = 0.6955, BCO model has R 2 = 0.9367 and RMSE = 0.5113 and PSO model has R 2 = 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.

Assessment of rate of penetration of a tunnel boring machine in the longest railway tunnel of Turkey

“…However, Salimi et al [47] mentioned that the boreability of rock decreases with the increase in UCS. Afradi et al [3] used a comprehensive database including uniaxial compressive strength, Brazilian tensile strength, RQD, cohesion, elasticity modulus, Poisson's ratio, density, joint angle and joint spacing as input parameters for estimating penetration rate. Mahdevari et al [41] employed uniaxial compressive strength, tensile strength, brittleness index, distance between the plane of weakness, alpha angle and machine parameters Fig.…”

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

“…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…”

“…Namli and Bilgin [28] developed a model to predict daily advance rates of EPB-TBMs. Afradi et al [29] suggested a new method for TBM penetration rate using ant colony optimization, bee colony optimization and the particle swarm optimization. The aim of this paper is to show the application of ANN, SVM and GEP for prediction of TBM penetration rate in Chamshir water conveyance tunnel which is considered as one the most important TBM tunneling projects in Iran.…”

Prediction of the number of consumed disc cutters of tunnel boring machine using intelligent methods

“…The root mean squared error (RMSE) is considered as a measure of absolute error between precipitation and observation. In this study, in order to evaluate the accuracy and performance of the models, the coefficients of determination (R 2 ) and root mean square error (RMSE) is used [36].…”