KNN METHOD FOR INTELLIGENT OBSERVATIONAL CLASSIFICATION OF ROCK MASS QUALITY IN TUNNEL

During the construction of tunnel, the observational classification of rock mass quality is the most direct method to evaluate the quality of the tunnel face surrounding rock. It is also an important basis for preventing geological disasters in tunnel construction, deciding construction excavation method and supporting measures. The traditional Q method and BQ method of national standard rock mass quality grading evaluation method require on-site and indoor laboratory tests and analysis, which results in the lag time of rock mass quality evaluation, often reduces the construction efficiency, or misses the window time to prevent sudden construction geological disasters. Rapidly and accurately to rock mass quality classification of tunnel face is the primary problem to be solved in observational rock mass classification of highway tunnel during construction period. The artificial intelligence algorithm provides a method to solve the real-time and accurate evaluation of the rock mass of the tunnel. Taking Yanqing-Chongli highway for Beijing Winter Olympic Games as an example, we adopt a method of using photogrammetry and artificial intelligence rock structure parameter to identify working face. In this method, seven index parameters system are established. We use the KNN intelligent algorithm to evaluate the quality of the tunnel rock and select 150 samples from 40 working faces in 8 tunnels for the training and learning. Another 50 samples are selected for rock mass quality evaluation and verification. Compared with the rock mass quality evaluation results of BQ, the accuracy reachs 90%. Conclusions are as follows. (1)KNN method for observational classification of rock mass quality of highway tunnel is a fast and efficient method for observational classification of rock mass quality by using artificial intelligence technology, which can obtain real-time rock mass evaluation results on site. (2)KNN classification method selects seven discriminant indicators, considering the occurrence of rock environment, geological structure, rock structure and other characteristics, and reflecting the operability and applicability of these indicators in the actual engineering. (3)KNN classification method misjudgment rate is very low. It has strong discriminant ability when marking discriminant classification by excluding the influence of artificial factors. This method provides a new way to solve the rock mass grade determination and classification.