Buy article PDF
Instant access to
the full article PDF
for the next 48 hrs
US$ 35
|
Structural Monitoring and Maintenance Volume 5, Number 1, March 2018, pages 67-78 DOI: http://dx.doi.org/10.12989/smm.2018.5.1.067 |
|
|
|
Risk identification, assessment and monitoring design of high cutting loess slope in heavy haul railway |
||
Qian Zhang, Yang Gao, Hai-xia Zhang, Fei Xu and Feng Li
|
||
| Abstract | ||
| The stability of cutting slope influences the safety of railway operation, and how to identify the stability of the slope quickly and determine the rational monitoring plan is a pressing problem at present. In this study, the attribute recognition model of risk assessment for high cutting slope stability in the heavy haul railway is established based on attribute mathematics theory, followed by the consequent monitoring scheme design. Firstly, based on comprehensive analysis on the risk factors of heavy haul railway loess slope, collapsibility, tectonic feature, slope shape, rainfall, vegetation conditions, train speed are selected as the indexes of the risk assessment, and the grading criteria of each index is established. Meanwhile, the weights of the assessment indexes are determined by AHP judgment matrix. Secondly, The attribute measurement functions are given to compute attribute measurement of single index and synthetic attribute, and the attribute recognition model was used to assess the risk of a typical heavy haul railway loess slope, Finally, according to the risk assessment results, the monitoring content and method of this loess slope were determined to avoid geological disasters and ensure the security of the railway infrastructure. This attribute identification- risk assessment- monitoring design mode could provide an effective way for the risk assessment and control of heavy haul railway in the loess plateau. | ||
| Key Words | ||
| loess cutting slope; risk assessment; attribute recognition; geological hazard monitoring | ||
| Address | ||
| Qian Zhang, Yang Gao, Fei Xu1 and Feng Li: 1Key Laboratory of Structural Health Monitoring and Control, Shijiazhuang Tiedao University, Shijiazhuang, China Hai-xia Zhang: Beijing General Municipal Engineering Design & Research Institute Co. Ltd., Beijing, China | ||