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Academic Journal of Engineering and Technology Science, 2021, 4(6); doi: 10.25236/AJETS.2021.040606.

Study on Mechanic Parameters Selection of Rock Slope Based on BP Neural Network Displacement Back Analysis


Zhaohui Liu1, Xia Wu2

Corresponding Author:
Xia Wu

1School of Architecture and Surveying & Mapping Engineering, Jiangxi University of Science and Technology, Ganzhou, 341000, China

2School of Architecture Engineering, Jiangxi College of Applied Technology, Ganzhou, 341000, China


Natural rock mass is a complex three-phase composition, and its internal structure, physical characteristics such as fragmentation and water content have significant temporal and spatial variability. The method of assigning the mechanical parameters of rock mass according to the code ignores this important feature. In order to obtain the mechanical parameters of natural rock mass more accurately, based on a rock slope in NO.2 section of Ganxian-Xingguo expressway, a method for calculating mechanical parameters based on displacement back analysis of BP neural network was proposed on the basis of traditional code assignment. The back analysis model was optimized by neuron weight, inertia correction and algorithm step control, and then the mechanical parameters of the whole slope were back analyzed. Fatherly, the finite element numerical simulation software ANSYS was used to establish the grid model of the slope to judge the whole stability of the slope. The field monitoring was used to verify the calculation results. Facts and figures showed that the mechanic parameters gotten from the back analysis is more reliable than those in the code. The study can provide reference for mechanic parameters assignment of the rock slope.


Rock mass, Mechanical parameters, Assignment, Back analysis, BP neural network

Cite This Paper

Zhaohui Liu, Xia Wu. Study on Mechanic Parameters Selection of Rock Slope Based on BP Neural Network Displacement Back Analysis. Academic Journal of Engineering and Technology Science (2021) Vol. 4, Issue 6: 31-38. https://doi.org/10.25236/AJETS.2021.040606.


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