An Improved Calibration Method to Determine the Strain Coefficient for Optical Fibre Sensing Cables

The strain coefficient of an optical fibre sensing cable is a critical parameter for a distributed optical fibre sensing system. The conventional tensile load test method tends to underestimate the strain coefficient of sensing cables due to slippage or strain transfer loss at the fixing points during the calibration procedure. By optimizing the conventional tensile load test setup, the true strain of a sensing cable can be determined by using two sets of displacement measuring equipment. Thus, the strain calculation error induced by slippage or strain transfer loss between a micrometre linear stage and sensing cable can be avoided. The performance of the improved calibration method was verified by using three types of sensing cables with different structures. In comparison to the conventional tensile load test method, the strain coefficients obtained by the improved calibration method for sensing cables A, B, and C increase by 1.52%, 2.06%, and 1.86%, respectively. Additionally, the calibration errors for the improved calibration method are discussed. The test results indicate that the improved calibration method has good practicability and enables inexperienced experimenters or facilities with limited equipment to perform precise strain coefficient calibration for optical fibre sensing cables

Measurement of Rock Joint Surfaces by Using Smartphone Structure from Motion (SfM) Photogrammetry

The measurement of rock joint surfaces is essential for the estimation of the shear strength of the rock discontinuities in rock engineering. Commonly used techniques for the acquisition of the morphology of the surfaces, such as profilometers and laser scanners, either have low accuracy or high cost. Therefore, a high-speed, low-cost, and high-accuracy method for obtaining the topography of the joint surfaces is necessary. In this paper, a smartphone structure from motion (SfM) photogrammetric solution for measuring rock joint surfaces is presented and evaluated. Image datasets of two rock joint specimens were taken under two different modes by using an iPhone 6s, a Pixel 2, and a T329t and subsequently processed through SfM-based software to obtain 3D models. The technique for measuring rock joint surfaces was evaluated using the root mean square error (RMSE) of the cloud-to-cloud distance and the mean error of the joint roughness coefficient (JRC). The results show that the RMSEs by using the iPhone 6s and Pixel 2 are both less than 0.08 mm. The mean errors of the JRC are −7.54 and −5.27% with point intervals of 0.25 and 1.0 mm, respectively. The smartphone SfM photogrammetric method has comparable accuracy to a 3D laser scanner approach for reconstructing laboratory-sized rock joint surfaces, and it has the potential to become a popular method for measuring rock joint surfaces

Measurement of Rock Joint Surfaces by Using Smartphone Structure from Motion (SfM) Photogrammetry

The measurement of rock joint surfaces is essential for the estimation of the shear strength of the rock discontinuities in rock engineering. Commonly used techniques for the acquisition of the morphology of the surfaces, such as profilometers and laser scanners, either have low accuracy or high cost. Therefore, a high-speed, low-cost, and high-accuracy method for obtaining the topography of the joint surfaces is necessary. In this paper, a smartphone structure from motion (SfM) photogrammetric solution for measuring rock joint surfaces is presented and evaluated. Image datasets of two rock joint specimens were taken under two different modes by using an iPhone 6s, a Pixel 2, and a T329t and subsequently processed through SfM-based software to obtain 3D models. The technique for measuring rock joint surfaces was evaluated using the root mean square error (RMSE) of the cloud-to-cloud distance and the mean error of the joint roughness coefficient (JRC). The results show that the RMSEs by using the iPhone 6s and Pixel 2 are both less than 0.08 mm. The mean errors of the JRC are −7.54 and −5.27% with point intervals of 0.25 and 1.0 mm, respectively. The smartphone SfM photogrammetric method has comparable accuracy to a 3D laser scanner approach for reconstructing laboratory-sized rock joint surfaces, and it has the potential to become a popular method for measuring rock joint surfaces

Meso-structure evolution of the sliding zone under seepage conditions

Periodic fluctuations of reservoir water level lead to the variations in seepage stress inside landslide bodies. Dynamic seepage pressures can lead to deterioration in the structure and strength of the slide zone, which affects the stability of the landslide. To identify the effect of seepage on the pore structure of the slip zone, the seepage tests were performed. First, a seepage test apparatus was developed and combined with CT scanning technology to obtain the meso-structure of the sliding zone under different seepage conditions. Then, the changes in the structural parameters of the slip zone soils were quantified by Avizo software. Finally, the mechanism of the meso-structural evolution of the sliding zone under seepage was analyzed. The results show that the permeability coefficient of the sliding zone decreases exponentially with time, and a higher seepage pressure will lead to a smaller permeability coefficient of the sliding zone. Statistical data show that the apparent porosity of sliding zone soil decreases from 5% to 1%. The proportion of pores with an equivalent spherical diameter of less than 80 μm increases with seepage time, while the proportion of pores with an equivalent spherical diameter greater than 80 μm decreases with seepage time. The above results indicate that the large pores in the slip zone soils are filled with small particles during the seepage.Hence, the seepage channels become elongated and curved, and the effective connectivity of the pores is weakened