Estimation of TBM Utilization with Artificial Neural Networks

Determination of schedule and bid prices for mechanized tunneling needs to estimate of tunneling machine advance rate. Also determination of machine advance rate needs to estimate machine utilization. Although some empirical equations were proposed for this purpose, these equations don’t have good accuracy. The aim of this study is estimation of TBM utilization with artificial neural networks. For this purpose data set of open TBM tunneling was gathered and a neural network with 5 inputs (in-situ stress, uniaxial compressive strength, disc thrust, joint orientation factor and Q rock mass classification index) and one output (TBM utilization) was designed. Since input parameters have a good relationship with output parameter, neural network estimate TBM utilization with high accuracy.