Application of Autoregressive Model in the Construction Management of Tunnels

to their management with many uncertainties. From these
uncertainties, we can mention the geological condition of the tunnel
route and the time and costs required for construction. In order to
significantly reduce these uncertainties, techniques that have a high
predictive power must be used. For this purpose, in this study, an
autoregressive model was used to reduce the uncertainties related to
geology and construction time and cost in tunnelling projects. A
comparison between the predicted results and the actual values
through several statistical indices showed the high-performance
prediction of the autoregressive model in the prediction of tunnel
resources. Also, three input parameters affecting tunnel
construction time and costs, such as RQD, groundwater, and RMR,
were considered. The sensitivity analysis of these parameters on the
time and cost of tunnelling projects was investigated through
mutual information test (MIT). The groundwater was the most
effective parameter on the tunnel's time and cost.