Information Entropy in Predicting Location of Observation Points for Long Tunnel

Based on the Markov model and the basic theory of information entropy, this paper puts forward a new method for optimizing the location of observation points in order to obtain more information from limited geological investigation. According to the existing data from observation points data, classification of tunnel geological lithology was performed, and various lithology distribution were determined along the tunnel using the Markov model and theory. On the basis of the information entropy theory, the distribution of information entropy was obtained along the axis of the tunnel. Therefore, different information entropy could be acquired by calculating different classification of rocks. Furthermore, uncertainty increases when information entropy increased. The maximum entropy indicates maximum uncertainty and thus, this value determines the position of the new drilling hole. A new geology situation will be decided by the maximum entropy for the lowest accuracy. Optimal distribution will be obtained after recalculating, using the new location of the geology situation. Taking the engineering for the Bashiyi Daban water diversion tunneling in Xinjiang as a case, the maximum information entropy of the geological conditions was analyzed by the method proposed in the present study, with 25 newly added geology observation points along the axis of the 30-km tunnel. The results proved the validity of the present method. The method and results in this paper may be used not only to predict the geological conditions of underground engineering based on the investigated geological information, but also to optimize the distribution of the geology observation points.