• Journal of Korean Society of Disaster and Security
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• v.12 no.2
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• pp.73-82
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• 2019

Recently, as the occurrence frequency of sudden floods due to climate change increased, the flood damage on riverside social infrastructures was extended so that there has been a threat of overflow. Therefore, a rapid prediction of potential flooding in riverside social infrastructure is necessary for administrators. However, most current flood forecasting models including hydraulic model have limitations which are the high accuracy of numerical results but longer simulation time. To alleviate such limitation, data driven models using artificial neural network have been widely used. However, there is a limitation that the existing models can not consider the time-series parameters. In this study the water surface elevation of the Hangang River bridge was predicted using the NARX model considering the time-series parameter. And the results of the ANN and RNN models are compared with the NARX model to determine the suitability of NARX model. Using the 10-year hydrological data from 2009 to 2018, 70% of the hydrological data were used for learning and 15% was used for testing and evaluation respectively. As a result of predicting the water surface elevation after 3 hours from the Hangang River bridge in 2018, the ANN, RNN and NARX models for RMSE were 0.20 m, 0.11 m, and 0.09 m, respectively, and 0.12 m, 0.06 m, and 0.05 m for MAE, and 1.56 m, 0.55 m and 0.10 m for peak errors respectively. By analyzing the error of the prediction results considering the time-series parameters, the NARX model is most suitable for predicting water surface elevation. This is because the NARX model can learn the trend of the time series data and also can derive the accurate prediction value even in the high water surface elevation prediction by using the hyperbolic tangent and Rectified Linear Unit function as an activation function. However, the NARX model has a limit to generate a vanishing gradient as the sequence length becomes longer. In the future, the accuracy of the water surface elevation prediction will be examined by using the LSTM model.

• Journal of the Korean Geotechnical Society
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• v.34 no.11
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• pp.21-31
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• 2018

Bucket foundations are widely used in offshore areas due to their various benefits such as easy and fast installations. A bucket is installed using self-weight and the hydraulic pressure difference across the lid generated by pumping out water from inside the bucket. When buckets are installed in high permeable soil such as sands, upward seepage flow occurs around the bucket tip and interior, leading to a decrease in the effective stress in the soil inside the buckets. This process reduces the penetration resistance of buckets. However, the soil inside and outside the bucket can be disturbed due to the upward seepage flow and this can change the soil properties around the bucket. Moreover, upward seepage flow can create significant soil plug heave, thereby hindering the penetration of the bucket to the target depth. Despite of these problems, soil disturbance and soil plug heave created by suction installation are not well understood. This study aims to investigate the behavior of soil during suction installation. To comprehend the phenomena of soil plug heave during installation, a series of small-scale model tests were conducted with different testing conditions. From a series of tests, the effects of tip thickness of bucket, penetration rate, and self-weight were identified. Finally, soil properties inside the bucket after installation were approximated from the measured soil plug heave.