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| Unstable rock mass identification method based on multi-level dynamic parameters |
| HUO Leichen1，DU Yan1，XIE Mowen1，LIU Weinan1，ZHANG Xiaoyong1，JIA Beining2，CONG Xiaoming2 |
| (1. Beijing Key Laboratory of Urban Underground Space Engineering，University of Science and Technology Beijing，
Beijing 100083，China；2. Comprehensive Institute of Geotechnical Investigation and Surveying，
Ministry of Construction，Beijing 100007，China) |
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Abstract In the process of slope rock mass from stable state to unstable state，the dynamic index will change significantly. Therefore，the introduction of appropriate dynamic index is the key to realize the accurate identification of unstable rock mass. In this study，six dynamic indexes such as time domain，frequency domain and energy index are introduced，and the PSO-SVM algorithm is used to carry out the experimental study on the identification method of unstable rock mass. The results show that compared with the identification model of unstable rock mass with single dynamic index in time domain and double dynamic index in time domain and frequency domain，the prediction effect of the identification model of unstable rock mass based on multi-level dynamic index is the best，with MSE of 0.004 772 and of 0.984 865. Therefore，the accurate quantitative analysis of unstable rock mass can be realized by using the identification method of unstable rock mass with a variety of sensitive dynamic indicators. According to the statistical analysis，the importance order of identification sensitivity of six dynamic indicators is the mean square frequency＞the margin index＞the relative energy of the first frequency band＞the center frequency＞the impact energy＞the pulse index. The study provides a relatively rich and sensitive dynamic monitoring index for the identification of unstable rock mass in the engineering site，which is helpful to establish a set of unstable rock identification method based on big data analysis，so as to meet the practical needs of disaster prevention and mitigation of collapse disasters in alpine valley areas.
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2022, Vol. 41 
