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Real-time unstable slope monitoring is essential for recognition of landslide occurrence, as well as for early warning to reduce landslide-induced damages. This study investigated an unstable slope monitoring system that consists of tilt sensors aiming to establish an advanced time prediction model (TPM) for landslide early warning. The monitoring process utilized additional support devices (e.g. pipe strain gauges, water level gauges and a rain gauge) installed on a natural cut slope site. The tilt sensors could detect movements (in tilt angle) within the slope generated by heavy rains. Analysis of the recorded data revealed that the rate of movement, or tilt, was influenced by groundwater table fluctuations and antecedent rainfalls. A relationship between the tilt rate and the horizontal displacement calculated from pipe strain value has been established. Subsequently, a new classification of slope movement was proposed according to the tilt rates obtained from the deformation process of the slope. Considering the movement characteristics, two warning levels were identified such as warning and evacuation. Further, an advanced TPM was proposed in relation to realtime slope surface tilt rates. The TPM could provide efficient results at the continuous acceleration phase of the landslide occurrence. Therefore, we suggest this technique can be applied for monitoring and early warning of rainfall-induced shallow landslides.

Real-time monitoring is essential for avoiding potential damage that may be caused by the failure of a marginally stable slope. This study investigated an unstable slope monitoring system that was comprised of a multi-point tilting sensor, including a pipe strain gauge. The monitoring process utilized additional supporting devices (e.g. water level gauge and rain gauge) that were installed on a cut slope site. Movement data was transmitted by way of a wireless communication system. The wireless system detected movements within the slope and that were generated by heavy rains. Analysis of the recorded data revealed that the rate of movement, or tilting, was influenced by the groundwater fluctuation and antecedent rainfall. Considering the tilting rate, three levels were identified as warning indicators and thus a time prediction formula was proposed. It could be suggested that this monitoring technique would be applicable for an early warning in the event of a rainfall – induced slope instability.