Empowering Agricultural Knowledge and Technology

Cari alat penelitian? Klik Produk Labodia

Empowering Agricultural Knowledge and Technology

Empowering Agricultural Knowledge and Technology

Cari alat penelitian? Klik Produk Labodia

rainfall-induced slope failure

Site-specific warning system for rainfall-induced slope failure

Samuel Harris (i), Rolando P. Orense (ii) and Kazuya Itoh (iii)
i) Geotechnical Engineer, AECOM, Hamilton, New Zealand
ii) Associate Professor, Dept of Civil & Env. Engineering, University of Auckland, Auckland, New Zealand
iii) Senior Researcher, National Institute of Occupational Safety and Health, Japan (J-NIOSH), Tokyo, Japan



In New Zealand, rainfall-induced slope failures cause millions of dollars’ worth of damage annually. As a means of mitigating the associated risk, a site-specific early warning system was developed based on field instrumentation, laboratory tests and finite element modeling.

The selected site is a cut slope adjacent SH-1, one of Auckland’s busiest state highways. The site consists of Northland Allochthon residual soil, known for its susceptibility to rainfallinduced landslides.

In fact, a landslide occurred at the site in the winter of 2008 following a period of prolonged rainfall. Firstly, a variety of laboratory tests were undertaken on soils taken from the slope to better understand their shear strength and hydraulic characteristics.

Next, a cross section of the slope about 45m from the 2008 landslide site was instrumented with 13 volumetric water content sensors installed at different depths, together with a tipping bucket rain gauge to monitor rainfall events.

With the rainfall and volumetric water content readings monitored since 2010, the soil properties and boundary conditions of the monitored slope cross-section were calibrated using a transient seepage analysis program (SEEP/W).

After successful validation, the matric suction/pore-water pressure profile was coupled with a limit equilibrium analyses (SLOPE/W) to simulate the 2008 landslide.

For this purpose, an artificial neural network (ANN) was trained to predict the factor of safety (FoS), which successfully validated the model using the 2008 rainfall record, obtaining FoS=1.0 at the exact time the 2008 landslide occurred.

Finally, the ANN-based methodology was extended to predict the FoS at the monitored site in the future, using rainfall forecasts. This can serve as basis of an early warning system as a means to mitigate the risk of rainfall induced landslides.

Keywords: rainfall, landslide, field monitoring, laboratory testing, seepage analysis

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