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Water-induced landslides pose a great risk to the society in Norway due to their high frequency and capacity to evolve in destructive debris flows. Hydrological monitoring is a widely employed method to understand the initiation mechanism of water-induced landslides under various climate conditions. Hydrological monitoring systems can provide relevant information that can be utilized in landslide early warning systems to mitigate the risk by issuing early warnings. These monitoring systems can be significantly enhanced, and wider deployments can be achieved through the recent developments within the domain of the Internet of Things (IoT). Therefore, this study aims to demonstrate a case study on an automated hydrological monitoring system supported by the IoT-based state-of-the-art technologies employing public mobile networks. Volumetric water content (VWC) sensors, suction sensors, and piezometers were used in the hydrological monitoring system to monitor the hydrological activities. The monitoring system was deployed in a case study area in central Norway at two locations of high susceptible geological units. During monitored period, the IoT-based hydrological monitoring system provided novel and valuable insights into the hydrological response of slopes to seasonally cold climates in terms of VWC and matric suction. The effects of rainfall, snow melting, ground freezing, and thawing were captured. The current study also made an attempt to integrate the collected data into a physical-based landslide susceptibility model to obtain a more consistent and reliable hazard assessment. [ABSTRACT FROM AUTHOR]

The inherent variability of soil has a crucial role in reliability-based design, especially for offshore foundations where the variability and uncertainty are more critical due to high costs as compared to the onshore counterparts. In this study, spatial probabilistic evaluation of the characteristics of offshore/nearshore sea bottom soils is performed based on data of 65 cone penetration tests (CPTs), reaching to 200-m depths in seabed soils, in up to 64 m of water. The types and typical characteristics of sea bottom soils are reported, together with statistical evaluation. A key parameter for random field theory, the spatial correlation length, based on CPT data is obtained for different soil types, using four different autocovariance functions (exponential, squared exponential, cosine exponential and second-order autoregressive). For these purposes, a MATLAB code is developed to take the CPT data, identify individual soil layers, carry out statistical evaluation of the properties of soils and report the vertical spatial correlation length of each layer using four different autocovariance functions. The undrained shear strength of clays in nearshore and offshore soils increases with depth, at rates of 1 to 3 kPa/m. Sands nearshore and offshore have similar relative density that is generally less than 50% (i.e. in loose to medium-dense state). The vertical spatial correlation length based on CPT of all soils is in the range of 0.11 m to 0.27 m, for all four different autocovariance functions, for all CPT cone tip resistances, sleeve friction and friction ratio, and for all shallow- and deep-water soils. The vertical spatial correlation length of nearshore soils is slightly larger than offshore soils. The results add to the scarce data on the spatial correlation length of offshore soils and can be useful for future studies on reliability and risk assessment of nearshore/offshore foundations. [ABSTRACT FROM AUTHOR]