# Prediction of Shallow Failure on a Slope Using Volumetric Water Content Gradient Characteristics
## 4 Measurement Position Setting Method
### Sensor Installation Positions  
Figure 14 shows the VWC response to the small-scale failure that occurred in the first experiment. Owing to rainfall infiltration, the VWC showed different increase patterns at each position. The results measured in the crest part with convergence values \(>\) 40% were considered most directly related to the small-scale failure because the VWC rapidly increased immediately before the time of failure. This indicates that the measurement position is important to use the VWC as a precursor of failure. In the first experiment, the active surface of the second failure occurred at the boundary of a soil depth of approximately 1.8 m. However, the installed VWC sensor with a maximum depth of 1.5 m, could not observe responses directly related to the failure. Based on the trial and error efforts of the first experiment, the maximum measurement depth of the VWC sensor was modified to 1.8 m in the second experiment. In the second experiment, small-scale and shallow failures followed the same pattern as those in the first experiment. In the first and second experiments, the VWC gradient was calculated for each VWC sensor installation position and depth (Figures 15 and 16). In the first experiment, the VWC gradient was higher than 0.1 at all times. In the lower part, all sensors issued \"prewarning\" or \"warning\" alerts approximately 10 h before the occurrence of the final failure. However, the image analysis showed that high VWC gradient values were obtained regardless of the failure that occurred under the influence of surface loss and scour. Given that most of the sensors installed in the upper part issued \"prewarning\" alerts approximately 1 h before the occurrence of failure in the b  
Figure 16: Variation of the VWC gradient as a function of time (second experiment). Figure 15: Variation of the VWC gradient as a function of time (first experiment). position is suitable for predicting failure in the berm. However, the position is not suitable for predicting the final failure because the VWC value converged too early to predict the second failure. In the case of depth, the sensors installed at a depth of 0.5 m and in the lower part exhibited high VWC gradient values regardless of failure subject to the influence of surface loss and scour. The sensors installed at depths of 1.0 and 1.5 m showed no significant differences as a function of depth. Consequently, in the first experiment, the lower part and the shallow depth (0.5 m) appeared to be inappropriate as sensor installation positions because high VWC gradient values were mostly observed under the influence of surface loss and scour. Therefore, sensors need to be installed in the upper part (a minimum depth of 1.0 m) to predict failure in the berm part and at a depth higher than 1.5 m to predict the final failure. When the VWC gradient in the second experiment was examined, the values relevant to the sensors installed in the lower part and at a depth of 0.5 m mostly converged at the beginning of the experiment as in the first experiment, indicating that the positions are not suitable for sensor installations. The sensors in the upper and middle parts installed at a depth of 1.0 m could predict the small-scale failure that occurred in the upper part approximately 1 h earlier on average, thus showing that the positions are suitable for measurements. The sensors in the upper and middle parts installed at a depth of 1.8 m could predict failure 1.74 h and 1.03 h earlier, respectively, on average. Based on the above results, it was confirmed that the lower part and a shallow depth of 0.5 m are not suitable for measurement because: (a) water was concentrated due to surface loss and scour, and (b) the VWC value rapidly converged regardless of failure. In addition, it was concluded that sensors needed to be installed at a depth of 1.0 m in the upper part to predict failure in the berm part where a sign of failure could be identified.