a-uniaxial-compression-experiment-with-co2-bearing-coal-using

A Uniaxial Compression Experiment with CO<sub>2</sub>-Bearing Coal Using a Visualized and Constant-Volume Gas-Solid Coupling Test System

Injecting carbon dioxide (CO2) into a deep coal seam is of great significance for reducing the concentration of greenhouse gases in the atmosphere and ...

Shandong University

Fiber Bragg Grating (FBG) sensors are comprehensively recognized as a structural stability monitoring device for all kinds of geo-materials by either embedding into or bonding onto the structural entities. The physical model in geotechnical engineering, which could accurately simulate the construction processes and the effects on the stability of underground caverns on the basis of satisfying the similarity principles, is an actual physical entity. Using a physical model test of underground caverns in Shuangjiangkou Hydropower Station, FBG sensors were used to determine how to model the small displacements of some key monitoring points in the large-scale physical model during excavation. In the process of building the test specimen, it is most successful to embed FBG sensors in the physical model through making an opening and adding some quick-set silicon. The experimental results show that the FBG sensor has higher measuring accuracy than other conventional sensors like electrical resistance strain gages and extensometers. The experimental results are also in good agreement with the numerical simulation results. In conclusion, FBG sensors could effectively measure small displacements of monitoring points in the whole process of the physical model test. The experimental results reveal the deformation and failure characteristics of the surrounding rock mass and make some guidance for the in situ engineering construction. 

Structural Stability Monitoring of a Physical Model Test on an Underground Cavern Group during Deep Excavations Using FBG Sensors.

The measurement accuracy of the intelligent flexible morphological sensor based on fiber Bragg grating (FBG) structure was limited in the application of geotechnical engineering and other fields. In order to improve the precision of intellisense for displacement, an FBG implantable flexible morphological sensor was designed in this study, and the classification morphological correction method based on conjugate gradient method and extreme learning machine (ELM) algorithm was proposed. This study utilized finite element simulations and experiments, in order to analyze the feasibility of the proposed method. Then, following the corrections, the results indicated that the maximum relative error percentages of the displacements at measuring points in different bending shapes were determined to be 6.39% (Type 1), 7.04% (Type 2), and 7.02% (Type 3), respectively. Therefore, it was confirmed that the proposed correction method was feasible, and could effectively improve the abilities of sensors for displacement intellisense. In this paper, the designed intelligent sensor was characterized by temperature self-compensation, bending shape self-classification, and displacement error self-correction, which could be used for real-time monitoring of deformation field in rock, subgrade, bridge, and other geotechnical engineering, presenting the vital significance and application promotion value.

Article	Year
Structural Stability Monitoring of a Physical Model Test on an Underground Cavern Group during Deep Excavations Using FBG Sensors. Sensors (Basel, Switzerland)\| PubMed ID: 26404287	2015
Design and Optimization of FBG Implantable Flexible Morphological Sensor to Realize the Intellisense for Displacement. Sensors (Basel, Switzerland)\| PubMed ID: 30029470	2018

Hanpeng Wang

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