Length Parameter for Scaling Abutment Scour Depth

Flow constriction caused by bridge abutment increases bed shear stress and thus enhances local scour. For scaling the maximum scour depth at the abutment, either abutment length or flow depth has been empirically used in previous studies. By performing a step-by-step analysis, this study proposes a new length scale, which is able to represent combined effects of abutment length, approach flow depth and channel width. Physically, the new length scale describes the maximum possible dimension of the associated vortex system (or large-scale turbulence). Six series of data compiled from the published literature were used in the analysis. The results indicate that the new length scale helps improve the agreement of predictions with the experimental data.

A Non-Tuned Machine Learning Technique for Abutment Scour Depth in Clear Water Condition

Abutment scour is a complex three-dimensional phenomenon, which is one of the leading causes of marine structure damage. Structural integrity is potentially attainable through the precise estimation of local scour depth. Due to the high complexity of scouring hydrodynamics, existing regression-based relations cannot make accurate predictions. Therefore, this study presented a novel expansion of extreme learning machines (ELM) to predict abutment scour depth (ds) in clear water conditions. The model was built using the relative flow depth (h/L), excess abutment Froude number (Fe), abutment shape factor (Ks), and relative sediment size (d50/L). A wide range of experimental samples was collected from the literature, and data was utilized to develop the ELM model. The ELM model reliability was evaluated based on the estimation results and several statistical indices. According to the results, the sigmoid activation function (correlation coefficient, R = 0.97; root mean square error, RMSE = 0.162; mean absolute percentage error, MAPE = 7.69; and scatter index, SI = 0.088) performed the best compared with the hard limit, triangular bias, radial basis, and sine activation functions. Eleven input combinations were considered to investigate the impact of each dimensionless variable on the abutment scour depth. It was found that ds/L = f (Fe, h/L, d50/L, Ks) was the best ELM model, indicating that the dimensional analysis of the original data properly reflected the underlying physics of the problem. Also, the absence of one variable from this input combination resulted in a significant accuracy reduction. The results also demonstrated that the proposed ELM model significantly outperformed the regression-based equations derived from the literature. The ELM model presented a fundamental equation for abutment scours depth prediction. Based on the simulation results, it appeared the ELM model could be used effectively in practical engineering applications of predicting abutment scour depth. The estimated uncertainty of the developed ELM model was calculated and compared with the conventional and artificial intelligence-based models. The lowest uncertainty with a value of ±0.026 was found in the proposed model in comparison with ±0.50 as the best uncertainty of the other models.

Time-Averaged Turbulent Velocity Flow Field through the Various Bridge Contractions during Large Flooding

Extreme rainfall events, larger than 500-year floods, have produced a large number of flooding events in the land and also close to the shore, and have resulted in massive destruction of hydraulic infrastructures because of scour. In light of climate change, this trend is likely to continue in the future and thus, resilience, security and sustainability of hydraulic infrastructures has become an interesting topic for hydraulic engineering stakeholders. In this study, a physical model experiment with a geometric similarity of the bridge embankments, abutments, and bridge deck as well as river bathymetry was conducted in a laboratory flume. Flow conditions were utilized to get submerged orifice flow and overtopping flow in the bridge section in order to simulate extreme hydrologic flow conditions. Point velocities of the bridge section were measured in sufficient details and the time-averaged velocity flow field were plotted to obtain better understandings of scour and sediment transport under high flow conditions. The laboratory study concluded that existing lateral flow contraction as well as vertical flow contraction resulted in a unique flow field through the bridge and the shape of velocity profile being “fuller”, thereby increasing the velocity gradients close to the bed and subsequently resulting in a higher rate of bed sediment transport. The relationships between the velocity gradients measured close to the bed and the degree of flow contraction through the bridge are suggested. Furthermore, based on the location of maximum scour corresponding to the measured velocity flow field, the classification of scour conditions, long setback abutment scour and short setback abutment scour, are also suggested.