The radial consolidation rate of prefabricated vertical drain (PVD)-installed soft deposits is known to be closely related to the PVD discharge capacity, which usually decreases during consolidation. Conventional solutions for radial consolidation of PVD-installed deposits have been developed to consider discharge capacity reduction using small-strain theory, in which the volume compressibility coefficient and soil permeability were assumed to be constant. This paper formulates a general expression for discharge capacity reduction with time in numerical analysis based on large-strain theory. Soil disturbance effects caused by PVD installation, such as a nonlinear distribution for radial hydraulic conductivity, are captured in the proposed solution. The proposed solution was applied to field data from a test embankment at Saga Airport. The proposed solution provides a good result which is close to the measured data. 相似文献
Predictive mapping of landslide occurrence at the regional scale was performed at Mt. Umyeon, in the southern part of Seoul, Korea, using an evidential belief function (EBF) model. To generate the landslide susceptibility map, approximately 90 % of 163 actual landslide locations were randomly selected as a training set, and about 10 % of them were used as a validation set. Spatial data sets relevant to landslide occurrence (topographic factors, hydrologic factors, forest factors, soil factors, and geologic factors) were analyzed in a geographic information system environment. In this study, landslide susceptibility was assessed on the basis of mass function assignment (belief, disbelief, uncertainty, and plausibility) and integration within a data-driven approach. The most representative of the resulting integrated susceptibility maps (the belief map) was validated using the receiver operating characteristic (ROC) method. The verification result showed that the model had an accuracy of 74.3 % and a predictive accuracy of 88.1 %. The frequency ratio (FR) model was also used for comparison with the EBF model. Prediction and success rates of 72.1 and 85.9 % were achieved using the FR model. The validation results showed satisfactory agreement between the susceptibility map and the existing landslide location data. The EBF model was more accurate than the FR model for landslide prediction in the study area. The results of this study can be used to mitigate landslide-induced hazards and for land-use planning. 相似文献
Bulletin of Engineering Geology and the Environment - The aim of this study is to evaluate and compare the performances of 5 machine learning (ML) techniques for predicting the spatial probability... 相似文献
Bulletin of Engineering Geology and the Environment - Rainfall and earthquakes are two significant triggering factors of mass movement. Since the Gyeongju earthquake on 12 September 2016, which... 相似文献
In this study, a new ensemble method was developed to assess landslide hazard models in Mt. Umyeon, South Korea, using the results of a physically based model as a conditioning factor (CF). Hydrological conditions were obtained from the national-scale rainfall threshold. To incorporate rainfall threshold in landslide initiation, national landslide inventory data were used to prepare I-D and C-D thresholds. A series of factor of safety (FS) distribution maps were prepared using a physically based model with a 12-h cumulative rainfall threshold. We created an ensemble model to overcome limitations in the physically based model, which could not incorporate important environmental variables such as hydrology, forest, soil, and geology. To determine the effect of CFs on landslide distribution, spatial data layers of elevation, drainage proximity, soil drainage characters, stream power index, sediment transport index, topographic wetness index, forest type, forest density, tree diameter, soil type geology, and the FS distribution map were analyzed in a maximum entropy-based machine learning algorithm. Validation was performed with a receiver operating characteristic curve (ROC). The ROC showed 65.9% accuracy in the physically based model, whereas the ensemble model had higher accuracy (79.6%) and a prediction rate of 89.7%. The ensemble landslide hazard model is a new approach, incorporating the FS distribution map into the available independent environmental variables.
An artificial reef has been actively installed to restore marine ecosystems and increase fish catching. The artificial reef which installed on soft ground loses its function due to the settlement and scour of seabed. This study performed a series of laboratory tests to investigate settlement and scour characteristics of seabed according to different reinforcement type, reinforced area and soil type. Two reinforcement types with different reinforced area were applied to reduce settlement and scour of ground: geogrid and geogrid-bamboo mat. Soil types of ground are clay, silt and sand deposits. A series of laboratory tests includes California bearing ratio test, large size settlement test, and two-dimensional wave channel test. The test results indicated that the reinforced artificial reef had less settlement and scour depth than the unreinforced artificial reef. Especially, the artificial reef reinforced with geogrid-bamboo mat had more improved stability than that with geogrid due to high bending stiffness of bamboo mat. 相似文献
Landslide susceptibility and vulnerability maps are key components for urban planning and risk management. The main objective of this research was spatial vulnerability mapping in the probable landslide runout zone in Soacha Province, Colombia. This study included three major steps: identification of a landslide susceptible area, identification of its runout zone, and vulnerability assessment using an area damage index method. The landslide-prone area was identified through a susceptibility analysis using a logistic regression model. In total, 182 landslide locations were collected and randomly distributed as training data (70%) and validation data (30%). The final landslide susceptibility map was validated using the area under the curve method. The validation result showed success and prediction rates of 88.71% and 89.96%, respectively. The Flow-R model was applied to identify the runout zone, and a back-propagation analysis approach was applied to estimate two essential input data for the model, i.e., the travel angle and velocity. From seven locations, the back-propagation analysis showed an average travel angle of 14.6° and an average velocity of 11.4 m/s. A total of 3777 buildings were identified within the probable runout zone. A physical vulnerability assessment was done by finding the ratio between area of buildings and area of runout zone in each small unit boundary. The physical vulnerability was classified as low, moderate, extensive, and complete on the basis of building exposure. The final result revealed that most of the village areas are in null or moderate vulnerability zones. In contrast to the village areas, the city areas include zones of extensive and complete vulnerability. This study showed that about 52% of the area of the city of Cazuca is completely vulnerable, i.e., in areas where abandoned quarry sites are present. The map of vulnerable areas may assist planners in overall landslide risk management.
