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Coupling relevance vector machine and response surface for geomechanical parameters identification

  • Zhao, Hongbo (School of Civil Engineering, Henan Polytechnic University) ;
  • Ru, Zhongliang (School of Civil Engineering, Henan Polytechnic University) ;
  • Li, Shaojun (State Key Laboratory of Geomechanics and Geotechnical Engineering, Institute of Rock and Soil Mechanics, Chinese Academy of Sciences)
  • Received : 2016.10.14
  • Accepted : 2018.04.25
  • Published : 2018.08.30

Abstract

Geomechanics parameters are critical to numerical simulation, stability analysis, design and construction of geotechnical engineering. Due to the limitations of laboratory and in situ experiments, back analysis is widely used in geomechancis and geotechnical engineering. In this study, a hybrid back analysis method, that coupling numerical simulation, response surface (RS) and relevance vector machine (RVM), was proposed and applied to identify geomechanics parameters from hydraulic fracturing. RVM was adapted to approximate complex functional relationships between geomechanics parameters and borehole pressure through coupling with response surface method and numerical method. Artificial bee colony (ABC) algorithm was used to search the geomechanics parameters as optimal method in back analysis. The proposed method was verified by a numerical example. Based on the geomechanics parameters identified by hybrid back analysis, the computed borehole pressure agreed closely with the monitored borehole pressure. It showed that RVM presented well the relationship between geomechanics parameters and borehole pressure, and the proposed method can characterized the geomechanics parameters reasonably. Further, the parameters of hybrid back analysis were analyzed and discussed. It showed that the hybrid back analysis is feasible, effective, robust and has a good global searching performance. The proposed method provides a significant way to identify geomechanics parameters from hydraulic fracturing.

Keywords

Acknowledgement

Supported by : University of Henan Province, National Natural Science Foundation of China

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