Abstract

One of the primary tasks for effective disaster relief after a catastrophic earthquake is robust communication. In this paper, we propose a simple logistic method based on two-parameter sets of geology and building structure for the failure prediction of the base stations in post-earthquake. Using the post-earthquake base station data in Sichuan, China, the prediction results are 96.7% and 90% for the two-parameter sets and all parameter sets, respectively, and 93.3% for the neural network method sets. The results show that the two-parameter method outweighs the whole parameter set logistic method and the neural network prediction and can effectively improve the accuracy of the prediction. The weight parameters of two-parameter set by the actual field data significantly show that the failure of base stations after earthquake is mainly due to the geological differences where the base stations are located. It can be envisioned that if the geological distribution between the earthquake source and the base station is parameterized, the multi-parameter sets logistic method can not only effectively solve the failure prediction after earthquakes and the evaluation of communication base stations under complex conditions, but also provide site selection evaluation for the construction of civil buildings and power grid towers in earthquake-prone areas.

Details

Title
Reliability prediction and evaluation of communication base stations in earthquake prone areas
Author
Li, Xueming 1 ; Wei, Yao 2 ; Ming, Zheng 1 ; Cong, Hao 1 ; Zheng, Xuanyu 1 ; Chang, Qihai 1 

 Xizang Minzu University, College of Information Engineering, Xianyang, China (GRID:grid.460748.9) (ISNI:0000 0004 5346 0588) 
 Chengdu University of Technology, College of Mathematics and Physics, Chengdu, China (GRID:grid.411288.6) (ISNI:0000 0000 8846 0060); Chengdu University of Technology, Geomathematics Key Laboratory of Sichuan Province, Chengdu, China (GRID:grid.411288.6) (ISNI:0000 0000 8846 0060) 
Pages
8981
Publication year
2023
Publication date
2023
Publisher
Nature Publishing Group
e-ISSN
20452322
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2821768689
Copyright
© The Author(s) 2023. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.