H. Fattahi,
Volume 6, Issue 2 (6-2016)
Abstract
The tunnel boring machine (TBM) penetration rate estimation is one of the crucial and complex tasks encountered frequently to excavate the mechanical tunnels. Estimating the machine penetration rate may reduce the risks related to high capital costs typical for excavation operation. Thus establishing a relationship between rock properties and TBM penetration rate can be very helpful in estimation of this vital parameter. However, establishing relationship between rock properties and TBM penetration rate is not a simple task and cannot be done using a simple linear or nonlinear method. Adaptive neuro fuzzy inference system based on fuzzy c–means clustering algorithm (ANFIS–FCM) is one of the
robust artificial intelligence algorithms proved to be very successful in recognition of relationships between input and output parameters. The aim of this paper is to show the application of ANFIS–FCM in estimation of TBM performance. The model was applied to available data given in open source literatures. The results obtained show that the ANFIS–FCM model can be used successfully for estimation of the TBM performance.
H. Fattahi,
Volume 9, Issue 2 (4-2019)
Abstract
key factor in the successful application of a tunnel boring machine (TBM) in tunneling is the ability to develop accurate penetration rate estimates for determining project schedule and costs. Thus establishing a relationship between rock properties and TBM penetration rate can be very helpful in estimation of this vital parameter. However, this parameter cannot be simply predicted since there are nonlinear and unknown relationships between rock properties and TBM penetration rate. Relevance vector regression (RVR) is one of the robust artificial intelligence algorithms proved to be very successful in recognition of relationships between input and output parameters. The aim of this paper is to show the application of RVR in prediction of TBM performance. The model was applied to available data given in open source literatures. In this model, uniaxial compressive strengths of the rock (UCS), the distance between planes of weakness in the rock mass (DPW) and rock quality designation (RQD) were utilized as the input parameters, while the measured TBM penetration rates was the output parameter. The performances of the proposed predictive model was examined according to two performance indices, i.e., coefficient of determination (R2) and mean square error (MSE). The obtained results of this study indicated that the RVR is a reliable method to predict penetration rate with a higher degree of accuracy.
