The physical model test of landslides is important for studying landslide structural damage, and parameter measurement is key in this process. To meet the measurement requirements for deep displacement in landslide physical models, an automatic flexible inclinometer probe with good coupling and large deformation capacity was designed. The flexible inclinometer probe consists of several gravity acceleration sensing units that are protected and positioned by silicon encapsulation, all the units are connected to a 485-comunication bus. By sensing the two-axis tilt angle, the direction and magnitude of the displacement for a measurement unit can be calculated, then the overall displacement is accumulated according to all units, integrated from bottom to top in turn. In the conversion from angle to displacement, two spline interpolation methods are introduced to correct and resample the data; one is to interpolate the displacement after conversion, and the other is to interpolate the angle before conversion; compared with the result read from checkered paper, the latter is proved to have a better effect, with an additional condition that the displacement curve move up half the length of the unit. The flexible inclinometer is verified with respect to its principle and arrangement by a laboratory physical model test, and the test results are highly consistent with the actual deformation of the landslide model.

Accurately predicting the surface displacement of the landslide is important and necessary. However, most of the existing research has ignored the frequency component of inducing factors and how it affects the landslide deformation. Therefore, a hybrid displacement prediction model based on time series theory and various intelligent algorithms was proposed in this paper to study the effect of frequency components. Firstly, the monitoring displacement of landslide from the Three Gorges Reservoir area (TGRA) was decomposed into the trend and periodic components by complete ensemble empirical mode decomposition (CEEMD). The trend component can be predicted by the least square method. Then, time series of inducing factors like rainfall and reservoir level was reconstructed into high frequency components and low frequency components with CEEMD and t-test, respectively. The dominant factors were selected by the method of dynamic time warping (DTW) from the frequency components and other common factors (e.g., current monthly rainfall). Finally, the ant colony optimization-based support vector machine regression (ACO-SVR) is utilized for prediction purposes in the TGRA. The results demonstrate that after considering the frequency components of landslide-induced factors, the accuracy of the displacement prediction model based on ACO-SVR is better than that of other models based on SVR and GA-SVR.

Landslide, as geological disaster, can cause significant damage to people and property. Monitoring landslide displacement and velocity are essential parameters for predicting landslides. This research proposes a self-powered landslide displacement sensor which can measure the landslide displacement and velocity based on the triboelectric nanogenerator. Test results demonstrate that the sensor can measure the displacement range of 0 to 42 mm and the velocity range of 0 to 1 cm/s within environmental temperature and humidity range of 0 to 75 degrees Celsius and 0 to 90%, respectively. The sensor measures displacement in discrete intervals within the measurement range with a minimum resolution of 3 mm, and the measurement errors of displacement and velocity are both less than 3%. Additionally, the sensor is self-powered, and power generation tests show that the sensor can output the maximum power of 281×10 ^-10 W when the external load resistance is 10M ohms. Compared with traditional landslide monitoring sensors, the self-powered feature makes this sensor more suitable for use in the field monitoring environment.

Landslide displacement monitoring plays a fundamental role in the study of landslide evolution mechanisms, forecasting, risk assessment, prevention, and control. To fill the deficiencies of traditional instrumentation for measuring landslide displacement distributed along lateral direction, a landslide displacement measurement method based on deformation-coupled pipeline trajectory measurement is proposed, and a pipeline trajectory inertial measurement instrument is developed. The developed instrument, primarily comprised of a single shaft gyro, two axis accelerometers, and an external roller encoder, is designed as an axial half strapdown-radial half platform structure combined with a mechanical gravity platform. This structure avoids the singularity of pitch angle and roll angle and can expediently calculate a pipeline trajectory with an Eulerian transformation when obtaining several basic physical variables, e.g., the axial linear velocity, pitch angle, roll angle, and azimuth angle. Additionally, the pipeline trajectory, measured at different times, possesses the ability to reflect the displacement evolution feature of landslides. The results of prototype simulation tests imply a single measurement accuracy of a 12 cm/100 m span and a singly periodic multiple (more than five times) measurement accuracy of a 3 cm/100 m span, which meets medium-precision displacement measurement requirements for a landslide. Additionally, the finished instrument has been successfully applied to the deformation monitoring of the Majiagou I# landslide, which further verifies its feasibility and offers a reference for similar landslides.

The velocity and direction of groundwater flow characterize the magnitude and direction of hydrodynamic pressure in landslides, which is critical for landslide stability analysis. However, there is no high-stability, high-precision, and easy-to-operate borehole flowmeter for in-situ real-time monitoring of groundwater flow. As such, an image-based borehole flowmeter was developed for this purpose in this study. By integrating hardware such as a high-resolution camera, a cable winch, and a tracer ball, and combining them with feature detection algorithms like the Circle Hough Transform (CHT), it can realize real-time, high-precision monitoring of groundwater flow velocity and direction. Calibration experiments were conducted to assess the accuracy of flow velocity and direction under various object distances and tracer ball sizes, enabling the determination of their optimal combination. Experimental results indicate that the developed borehole flowmeter demonstrates high accuracy in both flow velocity and direction measurements, with the measured flow velocity and direction exhibiting a high linear correlation with the actual flow velocity and direction. The measurement accuracy of the developed borehole flowmeter is optimal when the object distance is set to 12 cm and the tracer ball diameter is 8 mm. Beyond monitoring groundwater in landslide boreholes, this sensor can precisely measure flow velocity and direction in micro-flow surface waters, such as rivers and lakes, thus demonstrating the broad potential for diverse applications.