Research on Joint Parameter Inversion for an Integrated Underground Displacement 3D Measuring Sensor

Underground displacement monitoring is a key means to monitor and evaluate geological disasters and geotechnical projects. There exist few practical instruments able to monitor subsurface horizontal and vertical displacements simultaneously due to monitoring invisibility and complexity. A novel underground displacement 3D measuring sensor had been proposed in our previous studies, and great efforts have been taken in the basic theoretical research of underground displacement sensing and measuring characteristics by virtue of modeling, simulation and experiments. This paper presents an innovative underground displacement joint inversion method by mixing a specific forward modeling approach with an approximate optimization inversion procedure. It can realize a joint inversion of underground horizontal displacement and vertical displacement for the proposed 3D sensor. Comparative studies have been conducted between the measured and inversed parameters of underground horizontal and vertical displacements under a variety of experimental and inverse conditions. The results showed that when experimentally measured horizontal displacements and vertical displacements are both varied within 0 ~ 30 mm, horizontal displacement and vertical displacement inversion discrepancies are generally less than 3 mm and 1 mm, respectively, under three kinds of simulated underground displacement monitoring circumstances. This implies that our proposed underground displacement joint inversion method is robust and efficient to predict the measuring values of underground horizontal and vertical displacements for the proposed sensor.

[1]  A. Bayoumi,et al.  On the Evaluation of Settlement Measurements Using Borehole Extensometers , 2011 .

[2]  Hong-hu Zhu,et al.  Monitoring of lateral displacements of a slope using a series of special fibre Bragg grating-based in-place inclinometers , 2012 .

[3]  Pierpaolo Oreste,et al.  Back-Analysis Techniques for the Improvement of the Understanding of Rock in Underground Constructions , 2005 .

[4]  I. Butler,et al.  Displacement Forecasting Method in Brittle Crack Surrounding Rock Under Excavation Unloading Incorporating Opening Deformation , 2014, Rock Mechanics and Rock Engineering.

[5]  Hani S. Mitri,et al.  Stability of underground mine development intersections during the life of a mine plan , 2014 .

[6]  Qing Li,et al.  Research on an Electromagnetic Induction-Based Deep Displacement Sensor , 2011, IEEE Sensors Journal.

[7]  Haijun Yang,et al.  Comparison between several multi-parameter seismic inversion methods in identifying plutonic igneous rocks , 2011 .

[8]  Fawu Wang,et al.  Deformation characteristics and influential factors for the giant Jinnosuke-dani landslide in the Haku-san Mountain area, Japan , 2007 .

[9]  H. S. Wolff,et al.  iRun: Horizontal and Vertical Shape of a Region-Based Graph Compression , 2022, Sensors.

[10]  Wolfgang Durner,et al.  Simultaneous Estimation of Soil Hydraulic and Root Distribution Parameters from Lysimeter Data by Inverse Modeling , 2013 .

[11]  Charles H. Dowding,et al.  Monitoring deformation in rock and soil with TDR sensor cables: Part 1. Concept and case history , 2003 .

[12]  Jiuchuan Wei,et al.  Dynamic monitoring research on displacement of rock mass in coal seam floor on the 1604 workface in NanTun coalmine, Shandong Province, China , 2011 .

[13]  Wei Jin,et al.  Monitoring Internal Displacements of a Model Dam Using FBG Sensing Bars , 2010 .

[14]  Jian-Hua Yin,et al.  An optical fibre monitoring system for evaluating the performance of a soil nailed slope , 2012 .

[15]  Jinping Ou,et al.  Review: optical fiber sensors for civil engineering applications , 2015 .

[16]  Marte Gutierrez,et al.  Parameter identification in numerical modeling of tunneling using the Differential Evolution Genetic Algorithm (DEGA) , 2012 .

[17]  Hangseok Choi,et al.  Slope inclinometers for landslides , 2008 .

[18]  P. Cui,et al.  Monitoring and warning of landslides and debris flows using an optical fiber sensor technology , 2011 .

[19]  Giovanni Battista Barla,et al.  Monitoring of the Beauregard landslide (Aosta Valley, Italy) using advanced and conventional techniques , 2010 .

[20]  Hongde Wang,et al.  Real-time monitoring and early warning of landslides at relocated Wushan Town, the Three Gorges Reservoir, China , 2010 .

[21]  L. Ran,et al.  Long-Term Monitoring and Safety Evaluation of A Metro Station During Deep Excavation , 2011 .

[22]  Yingxi Liu,et al.  A procedure of parameter inversion for a nonlinear constitutive model of soils with shield tunneling , 2011, Comput. Math. Appl..

[23]  A. Elnashai,et al.  Self-learning simulation method for inverse nonlinear modeling of cyclic behavior of connections , 2008 .

[24]  Massimo Ramondini,et al.  Main features of mudslides in tectonised highly fissured clay shales , 2005 .

[25]  Jin-Jian Chen,et al.  Prediction of tunnel displacement induced by adjacent excavation in soft soil , 2013 .

[26]  Richard J. Finno,et al.  Inverse analysis techniques for parameter identification in simulation of excavation support systems , 2008 .

[27]  Daniel Dias,et al.  Back analysis of geomechanical parameters by optimisation of a 3D model of an underground structure , 2011 .

[28]  Paulo André,et al.  Structural health monitoring of different geometry structures with optical fiber sensors , 2012 .

[29]  An-Bin Huang,et al.  Development of a fibre Bragg grating sensored ground movement monitoring system , 2006 .

[30]  Shui-Long Shen,et al.  Field performance of underground structures during shield tunnel construction , 2012 .

[31]  Shucai Li,et al.  A study on sidewall displacement prediction and stability evaluations for large underground power station caverns , 2010 .

[32]  Qing Li,et al.  A Theoretical Model to Predict Both Horizontal Displacement and Vertical Displacement for Electromagnetic Induction-Based Deep Displacement Sensors , 2011, Sensors.

[33]  Carlos Rodrigues,et al.  FBG based strain monitoring in the rehabilitation of a centenary metallic bridge , 2012 .

[34]  K. Krebber,et al.  Fiber-optic sensor applications in civil and geotechnical engineering , 2011 .

[35]  John C. Brigham,et al.  Self-learning finite elements for inverse estimation of thermal constitutive models , 2006 .

[36]  Kefeng Zhang,et al.  Parameter Identification for Root Growth based on Soil Water Potential Measurements – An Inverse Modeling Approach , 2013 .

[37]  Jian Zhao,et al.  Structural health monitoring of underground facilities – technological issues and challenges , 2005 .