Study on Early Identification of Landslide Perilous Rocks Based on Multi-Dynamics Parameters

Yanchang Jia (), Zhanhui Li (), Tong Jiang, Yan Li, Shaokai Wang and Guihao Song
Additional contact information
Yanchang Jia: College of Geosciences and Engineering, North China University of Water Resources and Electric Power, Zhengzhou 450046, China
Zhanhui Li: College of Geosciences and Engineering, North China University of Water Resources and Electric Power, Zhengzhou 450046, China
Tong Jiang: College of Geosciences and Engineering, North China University of Water Resources and Electric Power, Zhengzhou 450046, China
Yan Li: College of Geosciences and Engineering, North China University of Water Resources and Electric Power, Zhengzhou 450046, China
Shaokai Wang: College of Geosciences and Engineering, North China University of Water Resources and Electric Power, Zhengzhou 450046, China
Guihao Song: College of Geosciences and Engineering, North China University of Water Resources and Electric Power, Zhengzhou 450046, China

Sustainability, 2023, vol. 15, issue 7, 1-13

Abstract: The dynamics parameters cause sudden change during the damage of the structural plane of landslide perilous rocks, and these can be easily accessed. Therefore the changes in dynamics parameters can effectively achieve early identification, stability evaluation, and monitoring and pre-alarming of the perilous rocks. Seven kinds of dynamic indexes, such as pulse indicator, margin index, the center of gravity frequency, root mean square frequency, impact energy, relative energy of the first frequency band, and damping ratio, are introduced and the early identification of landslide perilous rock is achieved based on the support vector machines (SVM) model, improved by particle swarm optimization algorithm. A laser vibrometer collected seven dynamic indexes of two rock masses on the reservoir bank slope in Baihebao Reservoir, China. Based on the particle group optimization algorithm optimization support vector (PSO–SVM) perilous rocks recognition model, and seven dynamic indicators, the stability of two rock masses was recognized with high efficiency and accuracy. The identification results were consistent with the landslide perilous rock identification results based on natural vibration frequency, and the results verify the accuracy of the PSO–SVM perilous rocks identification model. The results show that the sensitivity order of each identification index is: root mean square frequency > margin index > relative energy of the first frequency band > center of gravity frequency > impact energy > pulse indicator > damping ratio. The accuracy of the multi-dynamics parameters landslide perilous rock mass identification model can be improved by selecting appropriate dynamic indexes with good sensitivity. The research results have high theoretical significance and application value for early identification of landslide perilous rocks, stability evaluation, and safety monitoring, and early warning.

Keywords: landslide perilous rocks; early identification; dynamics parameters; stability evaluation; monitoring (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/2071-1050/15/7/6296/pdf (application/pdf)
https://www.mdpi.com/2071-1050/15/7/6296/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:15:y:2023:i:7:p:6296-:d:1117388

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().