Search results
- Title
- Experimental and numerical study of local scour around side-by-side bridge piers under ice-covered conditions
- Contributors
- Mohammad Reza Namaee (author), Jueyi Sui (thesis advisor), Youmin Tang (thesis advisor), Jianbing Li (committee member), Liang Chen (committee member)
- Abstract
- Local scour around piers and abutments is one of the main causes of the collapse of many bridges constructed inside rivers. Many researchers have conducted various studies to predict the maximum depth of a scour hole around bridge piers and abutments. However, most of them have been done in small-scale laboratory flumes and specifically for the open channel condition. Besides, most of the existing research on bridge piers uses uniform sediment which is not an appropriate representative of natural river systems. This can result in excessively conservative design values for scour in low risk or non-critical hydrologic conditions. The most severe cases of bridge pier scouring occur in cold regions when the surface of water turns into ice in which, an additional boundary layer is being added to the water surface, which leads to significant changes in the flow field and scour pattern around bridge piers. Ice cover also causes the maximum flow velocity to move closer to the channel bed.
- Discipline
- Natural Resources & Environmental Studies
- Date added
- 2019-08-15T20:08:54.045Z
- Title
- A high-resolution snow distribution on alpine catchments over Southern Columbia mountains
- Contributors
- Hamidreza Shams (author), Jueyi Sui (thesis advisor), University of Northern British Columbia College of Science and Management (Degree granting institution), Jiangbing Li (committee member), Liang Chen (committee member)
- Abstract
- Snow plays an important role on the hydrological cycle of watersheds in cold regions. Predicting timing and magnitude of snow accumulation and ablation is necessary for water management in different sectors. A spatially distributed snow model (SnowModel) is chosen for our research, which is forced by meteorological data provided from automated weather stations. SnowModel is evaluated for two watersheds in southeast of BC. Two consecutive year (2006-2008) are selected for the calibration and validation processes. Simulated snow depth and snow water equivalent (SWE) are compared with observed data from snow pillows. Two error factors of Nash-Sutcliffe Efficiency Index, and R-squared show 0.96, 0.98 values in accumulation period and 0.87, 0.86 for ablation period, respectively. Spatially distribution of snow depth and SWE over domains also are discussed. In general, SnowModel is able to estimate the accumulated snow depth and SWE in alpine areas in a high level of accuracy.
- Discipline
- NRES-Environmental Science
- Date added
- 2019-07-09T18:39:16.917Z
- Title
- Neighbouring proximity
- Contributors
- Hongyuan Shi (author), Jueyi Sui (thesis advisor), Jernej Polajnar (committee member)
- Abstract
- Deep Learning has become increasingly popular since 2006. It has an outstanding capability to extract and represent the features of raw data and it has been applied to many domains, such as image processing, pattern recognition, computer vision, machine translation, natural language processing, and autopilot. While the advantages of deep learning methods are widely accepted, the limitations are not well studied. This thesis studies cases where deep learning methods lose their advantages over traditional methods. Our experiments show that, when the neighbouring proximity disappears, deep learning methods are significantly less powerful than traditional methods. Our work not only clearly indicates that deep structure methods cannot fully replace traditional shallow methods but also shows the potential risks of applying deep learning to autopilot.
- Discipline
- Computer Science
- Date added
- 2019-09-04T21:14:09.159Z