Keywords

Introduction

There is a long history of prevention and mitigation of rainfall and/or scouring-induced landslides. Mechanical countermeasures to prevent slope failure have been widely used, including retaining walls and ground anchors. However, these methods can be expensive and are not always realistically applicable for all slopes of varying scale and potential risk factors. Therefore, careful monitoring of slope behavior and consequent early warning of failure provides a reasonable and slope-specific alternative.

In this paper, an early warning system for slope failure is proposed and its development is described (Fig. 1) (Uchimura et al. 2011). The system consists of a minimum number of low-cost sensors strategically placed on a slope, with monitoring data that are collected being transmitted via a wireless network. It is anticipated that this low-cost and simple system will provide at risk residents with access to accurate and timely precautions or warnings of slope failure.

Fig. 1
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Schematic illustration of MEMS tiltmeter sensor for early warning

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Uchimura et al. (2015) summarized case studies of slope tilting rates during pre-failure stages obtained on several natural slope sites under natural or artificial heavy rainfall. Figure 2 presents an example of the typical monitoring data obtained, in which the tilting rate (X-axis) can be related with the time elapsed until slope failure or slope stabilization (Y-axis). Figure 3 shows the definition of the tilting rate and the time in Fig. 2, in which Ti is the time until failure or stabilization, and Ri is tilting rate.

Fig. 2
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Graphic illustration of the tilting rate as a function of time before slope failure (or stabilization) for several case studies

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Fig. 3
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Definition of the tilting rate and the durations

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In cases where the slope failed at the position of the tilt sensor, the elapsed time is measured from the time when tilting accelerated to the time of failure. In cases where the slope did not fail but instead stabilized, the time is measured from when tilting decelerated to the time when the slope stabilized.

According to Fig. 2, the order of tilting rate observed with slope deformation varied widely, from 0.0001 to 10°/h depending on a number of factors. The tilting rate tends to increase towards failure with a relatively short time until failure, when a higher tilting rate is observed. The observed tilting rate was >0.01°/h for all the cases in which the slope failed or nearly failed, while it was <0.1°/h for all other cases. Durations of 1–10 h were observed before failure for a tilting rate of 0.1°/h.

Based on the results of our researches, it is proposed that when the tilting rate exceeds 0.1°/h a warning of slope failure should be issued, and a precaution issued at a tilting rate of 0.01°/h, taking safety into account. Additionally, this paper explores efforts by the current authors to improve the applicability of the monitoring and early warning system. The miniature tilt sensors is modified from that currently available to be more cost-effective, smaller in size and weight, and simpler to install, maintain and operate. As a result, it is possible to install a larger number of sensors on a given slope, thereby providing greater coverage and higher data density.

Figure 4 illustrates the typical arrangement of two types of proposed sensors, with data transfer pathways also shown. Despite the advantages described above, the new type miniature tilt sensors have relatively short radio transmission distances (~30 m in non-ideal conditions). They are arranged densely on high-risk areas of a slope, with one conventional tilt sensor unit collecting all the data of each area. The data are transmitted over greater distances (300–600 m), and uploaded to an internet server.

Fig. 4
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An early warning system of slope failure by multi-point tilt and volumetric water content

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MEMS Inclinometer Technology Embedded to Sensor Unit Sections of the Manuscript

The proposed system measures the inclination on the slope surface and the volumetric water content in the slope. A MEMS tilt module (nominal resolution = 0.04 mm/m = 0.0025°) is embedded in each sensor unit. The tilt module is a 3D-MEMS-based dual axis inclinometer that provides sensor unit grade performance for levelling applications. The measuring axes of the sensing elements are parallel to the mounting plane and orthogonal to each other. Low temperature dependency, high resolution, power-saving and low noise, together a with robust sensing element design, if we keep on levelling installation, this MEMS type inclinometer is ideal choice for slope failure sensors.

Field Validation in Japan and Taiwan

A Case of Detection of a Cut Slope at Yenchao, Kaohsiung, Taiwan

Prototypes of the proposed new sensor units were installed on a cut slope site in Yenchao, Kaohsiung, Taiwan at the end of 2014. The slope is on a municipal waste disposal site, and the cut slope began moving gradually after excavation works were conducted during construction. The slope, consisting of modern clay shale, is sliding along the established geological jointing direction (Fig. 5).

Fig. 5
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Arrangement of the tilt sensors on the moving cut slope

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Figure 6 shows typical tilt behaviors of X-axis, and Fig. 7 shows Y-axis behaviors with time histories. These data were obtained from 21 to 30 May 2015. Because of one week continuous rainfall shown in Fig. 8, the Sensors H5, T4, T3 showed some significant inclination changes. The tilt behavior varied across sensors and there was some time delay following the heavy rainfall event. Notably, it was also found that T3, which is outside of the sliding block, recorded movement after the heavy rainfall event. Distribution of surface tilt behaviors, together with time histories, can be determined based on these data, the movement histories of every sensors can be recorded as shown in Fig. 5.

Fig. 6
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Time histories of X-axis inclinations

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Fig. 7
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Time histories of Y-axis inclinations

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Fig. 8
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Rainfall record during the disaster in 2015

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Monitoring Slope Failure at Manzawa, Yamanashi, Japan

The Manzawa area in the Yamanashi Prefecture of Japan contains a large scale reactivation of old slope failures featuring rock falls that involve the detachment and rapid downward movement of rock.

Because most traditional slope monitoring methods are expensive, difficult to control and may not be suitable for application in this civilian area, the simple and low-cost monitoring system was deployed on a test slope to validate field performance. It should be noted that the research is supported by the Japanese Government, and the following result that is reported in this paper is intermediate.

Figure 9 shows the scale of Manzawa slope failure site, and Fig. 10 shows the arrangement of the multi-point tilt sensors and locations, where two types of tilt sensor were used. The arrangement interval of the sensor is designed to five meters. A total of 66 sets of sensors were deployed.

Fig. 9
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Area of slope failure at Manzawa site, Japan

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Fig. 10
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Arrangement of the multi-point tilt sensors

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The system proposed in this study implemented wireless sensors consisting of MEMS accelerometers to measure tilt from angular movements. This orientation change data from the MEMS accelerometers were transmitted wirelessly to a remote monitoring facility. A real-time monitoring system would be an effective tool for the transmission of alerts and immediate activation of emergency procedures, thus providing ample time to save lives and property.

Necessary components of the system include sensors with the required resolution and software with the capacity for signal interpretation and failure alert algorithms. The challenges exist in identifying methods to minimize energy consumption of the units (i.e. improving battery life), keeping the appropriate number of devices for deployment and recognizing patterns of movement so that incipient sliding can be distinguished from random movements and environmental effects. The requirement for battery lifetime should ideally be longer than one year to reliably monitor the most critical time period without interruption and multiple year lifetimes should be achievable given the progress being made in battery technology.

Algorithms can then be developed to account for these movements and the sensitivity of these to varying threshold values can be evaluated. Finally an effective early warning system can be developed.

The 66 sensor units are divided into three groups, left/middle/right zone, and one data receiver unit and one logger/gateway unit for internet collect all the data from respective group as shown in Fig. 10. There were eight heavy rainfall event during summer of 2015 shown in Fig. 11, and the tilt angles accumulated distribution due to each rain are summarized as Fig. 12. The tilting rate averaged during each rainfall event is shown in Fig. 13. Distribution of tilting behaviors is figured out by multi-point monitoring.

Fig. 11
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Time histories of movements in rainy days

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Fig. 12
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Distribution of accumulated inclination angle

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Fig. 13
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Distribution of tilting rates during each rain day

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For practice, criteria for issuing early warning have to be defined based on data from the large number of sensors. One of very simple index for the criteria is simple sum of tilting rate from the sensors:

$$ V_{alarm} = \sum\limits_{n = 1}^{N} {\left( {\left| {V_{n} } \right|*\frac{{A_{n} }}{{A_{0} }}*\partial_{n} } \right)} $$
(1)

Here, n is serial number of tilt sensors, Vn is tilting rate of slope sliding direction at the n-th sensor, An is the area of installation of the n-th sensor, A0 is the total area of monitored slope, and n is a constant weight for the n-th sensor decided considering geology, geography, vegetation, and other factors. As the simplest example, values calculated with n = 1 for all the sensors are indicated in Fig. 14. The rain on 4/20, 6/3, and 8/13 caused relatively higher value of Valarm in this case, but did not exceed precaution threshold of 0.01°/h.

Conclusions

A low-cost and simple monitoring method for an early warning system of rainfall-induced landslides has been proposed. Tilting angles in the surface layer of the slope are mainly monitored using this method and, in several case studies, distinct behaviors in the tilting angles in the pre-failure stages were detected. From this behavior it is recommended that, from a regulatory perspective, a precaution is issued when the tilting rate of a slope is 0.01°/h, and a warning issued when the tilting rate is 0.1°/h.

The prototype of the proposed new sensor units were installed on a cut slope site in Yenchao, Kaohsiung, Taiwan at the end of 2014. Whilst monitoring has only just begun, and some tilt behavior has been observed, more significant behavior may be recorded in the 2015 rainy season, which the current authors will report on.

Finally, density of sensor installation is a difficult problem. Lazzari and Danese (2012) proposed a new method for local landslide susceptibility evaluation and forecasting based on spatial statistics techniques and in particular on kernel density estimation, it would be referred to sensors arrangement for our future research.