Application of Uncertainty Average Clustering Measurement Model to Classification of Tunnel Surrounding Rock
SHI Xiu zhi and ZHOU Jian
DOI:10.11835/j.issn.1674-4764.2009.02.012
Received ,Revised , Accepted , Available online July 01, 2015
Volume ,2009,Pages 62-67
- Abstract
Based on the uncertainty measurement theory, a uncertainty average clustering measurement model for tunnel surrounding rock classification was established. Due to the uncertain factors in judging the engineering quality of rock masses, seven indexes, i.e., the rock grade, rock weathering degree, rock mass structure, elasticity longitudinal wave velocity of rock mass, influence degree of geological structure, development of joint fissure and ground water regime, were used as the discriminating factors. the indexes functions of unascertained measurement of 20 sets of rock samples were established, and the centre of the classification was indicated by using the average of classification of samples. The weight of indexes was calculated by entropy weight theory, and a prediction for the classification of residual tunnel surrounding rock was carried out using the rules of credible recognition. Each of the 20 sets of tunnel surrounding rockmass samples was tested according to the model, and the correctness rate is 100%. The other 10 sets of tunnel surrounding rock samples were predicted by using this model. The results show that the uncertainty measurement model classification agrees well with the actual measured ones. Therefore, it shows that the uncertaity measurement model is effective, available and can be applied to classification of tunnel surrounding rock in underground engineering.