Reducing The Time And Cost Uncertainty Of Tunneling Projects Using Dat Method Case Study: Hamro Road Tunnel In Iran
Among the significant discussions pertaining to the project design and planning, minimizing uncertainties is an important issue. Usually the uncertainties in subsurface projects arise from the unknown ground conditions which may cause the designer to fail to consider all the potential issues prone to occur during the construction procedure. Total time and cost uncertainties can be considered as the most important uncertainties; the time and costs are directly connected with thorough cognition of the subsurface conditions. Accordingly, in subsurface projects the actual time and costs are not ascertainable, and hence the probability methods should be used to assess such factors.In order to evaluate the uncertainties probabilistically, different tools (probabilistic models) have been introduced, including Decision Aids for Tunneling (DAT) tool. DAT was first introduced by Einstein; it was then developed by Prof. Einstein group and major advancements have been achieved in DAT research projects since then. The present study was undertaken on a Hamro road tunnel of 1310m length and cross section of $97 m^2$ as part of the under construction Sanandaj - Marivan road. In this paper, using DAT method and considering the pre-construction data of a Hamro tunnel, the probabilistic time and cost of the tunnel were predicted. In most cases in order to estimate the data needed for DAT method, a number of questionnaires were distributed among the tunneling experts and eventually the mean values of the respondents were applied to the model. Afterwards the time and cost obtained through DAT method were compared with the actual post-construction time and cost of the Hamro tunnel execution for model validation and reducing the uncertainty for the future projects. Finally, it was revealed that the obtained results of the DAT method has high similarity with the actual results, confirming the validity of DAT method for decreasing the total time and cost uncertainties in execution of future projects.
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