Large infrastructure projects like tunnel excavations, underground constructions face innumerable risks. For successful completion of these projects, it is essential that we foresee and understand risks that could occur during different phases of the project. One of the risks affecting underground excavation projects is the inaccurate performance prediction of TBMs. As this could severely affect the project, resulting in lower advance rates and could also result in changing the excavation machine or method, due to difficulties in excavation, thereby increasing the overall cost of tunnelling. This makes performance prediction of the TBMs as one of the crucial aspects in tunnelling, as a precise estimation of its performance is required for planning and estimation of time and cost of the tunnelling projects. This performance prediction of TBM is a challenging geotechnical problem that is intricate and complex in nature, this is possibly due to the fact that TBM performance prediction involves understanding the rock fragmentation process in wide range from micro-scale (i.e. the interaction of surface of rock material and cutter tip) to macro-scale (including the interaction of rock mass and TBM). Thus in order for the performance prediction model to be efficient, parameters of both TBM specifications and ground conditions have to be considered. Various models have been proposed by researchers since the early phases of TBM application, these studies resulted in the development and improvement of numerous penetration prediction models. Most of these models consider the geotechnical parameters that are input to the model to be consistent along the TBM drive and neglect the uncertainties in parameters that occur. And so to improve the predictive capability of these models and to incorporate the above uncertainties, the penetration rate is modeled as a stochastic variable depending on the geotechnical and TBM parameters, its statistical distribution is determined and is used to characterize the penetration rate instead.

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