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  Multi-Dimensional Evaluation of Tunnelling Pre-Grouting Methodology Based on Excavation and Overhead Costs Considering Environmental Aspects

  Water drawdown due to water ingress is a probable issue in tunnelling processes. This makes serious problems including ground settlements, environmental problems, increasing costs, excavation hazards, and several executive difficulties. In this paper, using measured data, data investigation, probabilistic and sensitivity analysis, machine learning methods, curve fitting, and several numerical approaches, a model has been developed using Gene Expression Programming (GEP), which is well-known in machine learning and model generation methods. The proposed model has eight input parameters consists of water ingress, RMR, rainfall, distance and Poisson’s ratio. To training the model, 15 km headrace tunnel (HRT) of Uma Oya Multipurpose Development Project (UOMDP) located in Siri-Lanka has been considered as a case study. Based on the analyses, the proposed model has a good performance in predicting the water drawdown amounts and its variations. A graph was generated to consider the simultaneous effects of water ingress and distance on the drop of water level.

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