Yonghui Zhu, Xiaolong Guo, Yajun Xie
Friday 3 july 2015
8:45 - 9:00h at Europe 2 (level 0)
Themes: (T) Extreme events, natural variability and climate change, (ST) Learning from disasters
Parallel session: 14I. Extreme events - Lessons Disaster
Modeling and prediction of breach growth in embankments is of significant importance for disaster mitigation, public security guarantee, and for social and economic sustainable development, etc. Unfortunately, breaching of embankments is a complicated process relying on many factors. Specially, for the headcut erosion, i.e. the main erosion mechanism of embankment breaching due to overflowing, understanding of the formation and development of the headcut is still limited. So far there is not a widely accepted model for the headcut erosion, and this already become a restriction for the further improvement of embankment breaching models. Based on the mechanism of headcut erosion as observed in various tests in the laboratory and in the field, the 2D mathematical model of Zhu (2006) for the headcut erosion during breaching of homogeneous embankments is further improved. The improved model covers the shear erosion along the headcut top surface, the jet scour of foundation and headcut undermining, and the slope mass failure. The model is capable of simulate the process and average rate of headcut erosion, with either erodible or non-erodible foundation. Laboratory experiments were conducted in a flume at the Changjiang River Scientific Research Institute to improve the understanding of the physics of headcut erosion. Then the improved mathematical model has been calibrated against the data of seven flume tests, with good agreement between the model predictions and the experimental data. Based on the model calibration results, the relationship between the soil erodibility coefficient (kd) in the erosion rate formula and the embankment soil properties of Zhu (2006) is further improved as well. With this expression, validation of the model against the data of four other tests yields good agreement between the model predictions and the measurements.