Reproducing Field Measurements Using Scaled-Down Hydraulic Model Studies in a Laboratory

Lee, Seung Oh; Hong, Seung-Ho

doi:10.1155/2018/9091506

Cited by 5 publications

(4 citation statements)

References 17 publications

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“…The flow disturbances around obstacles such as bridge piers, inserted into an alluvial streambed, induce local scouring which is one of the most common being riverbed scour. Since the late 1950s the estimation of scour at bridges has attracted the attention of many researchers [88].…”

Section: Discussionmentioning

confidence: 99%

A Hybrid Intelligence Approach to Enhance the Prediction Accuracy of Local Scour Depth at Complex Bridge Piers

et al. 2020

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Local scour depth at complex piers (LSCP) cause expensive costs when constructing bridges. In this study, a hybrid artificial intelligence approach of random subspace (RS) meta classifier, based on the reduced error pruning tree (REPTree) base classifier, namely RS-REPTree, was proposed to predict the LSCP. A total of 122 laboratory datasets were used and portioned into training (70%: 85 cases) and validation (30%: 37 cases) datasets for modeling and validation processes, respectively. The statistical metrics such as mean absolute error (MAE), root mean squared error (RMSE), correlation coefficient (R), and Taylor diagram were used to check the goodness-of-fit and performance of the proposed model. The capability of this model was assessed and compared with four state-of-the-art soft-computing benchmark algorithms, including artificial neural network (ANN), support vector machine (SVM), M5P, and REPTree, along with two empirical models, including the Florida Department of Transportation (FDOT) and Hydraulic Engineering Circular No. 18 (HEC-18). The findings showed that machine learning algorithms had the highest goodness-of-fit and prediction accuracy (0.885 < R < 0.945) in comparison to the other models. The results of sensitivity analysis by the proposed model indicated that pile cap location (Y) was a more sensitive factor for LSCP among other factors. The result also depicted that the RS-REPTree ensemble model (R = 0.945) could well enhance the prediction power of the REPTree base classifier (R = 0.885). Therefore, the proposed model can be useful as a promising technique to predict the LSCP.

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Section: Discussionmentioning

confidence: 99%

A Hybrid Intelligence Approach to Enhance the Prediction Accuracy of Local Scour Depth at Complex Bridge Piers

et al. 2020

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“…As shown in Figure 1a,b, in previous studies [22][23][24], laboratory experiments were conducted using various scaled hydraulic model of the Chattahoochee River bridge at Cornelia, Georgia, and Flint River bridge at Bainbridge, Georgia, USA, respectively, including the full river bathymetry. The previous experimental studies successfully explored the effect of sediment size on pier scour depth at different geometric model scales and, also based on the large number of experimental and field investigations/comparisons, improved local scour formulas were suggested.…”

Section: Methodsmentioning

confidence: 99%

Turbulence Characteristics before and after Scour Upstream of a Scaled-Down Bridge Pier Model

Lee

Hong

2019

Water

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Bridge pier scour is one of the main causes of bridge failure and a major factor that contributes to the total construction and maintenance costs of bridge. Recently, because of unexpected high water during extreme hydrologic events, the resilience and security of hydraulic infrastructure with respect to the scour protection measure along a river reach has become a more immediate topic for river engineering society. Although numerous studies have been conducted to suggest pier scour estimation formulas, understanding of turbulence characteristics which is dominant driver of sediment transport around a pier foundation is still questionable. Thus, to understand near bed turbulence characteristics and resulting sediment transport around a pier, hydraulic laboratory experiments were conducted in a prismatic rectangular flume using scale-down bridge pier models. Three-dimensional velocities and turbulent intensities before and after scour were measured with Acoustic Doppler Velocimeter (ADV), and the results were compared/analyzed using the best available tools and current knowledge gained from recent studies. The results show that the mean flow variable is not enough to explain complex turbulent flow field around the pier leading to the maximum scour because of unsteady flows. Furthermore, results of quadrant analysis of velocity measurements just upstream of the pier in the horseshoe vortex region show significant differences before and after scour.

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“…During the velocity measurements, correlation values in these experiments were greater than 80% and the Signal Noise Ratio (SNR) was greater than 15. The sampling frequency of the ADV was chosen to be 25 Hz with a sampling duration of two minutes and perhaps as much as five minutes depending on the turbulence at each measuring location [14][15][16][17][18][19]. The phase-space despiking algorithm of Goring and Nikora [20] was also employed to remove any spikes in the time record caused by aliasing of the Doppler signal, which sometimes occurs near a boundary.…”

Section: Methodsmentioning

confidence: 99%

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

Yoon

Lee

Hong

2019

Water

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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.

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Reproducing Field Measurements Using Scaled-Down Hydraulic Model Studies in a Laboratory

Cited by 5 publications

References 17 publications

A Hybrid Intelligence Approach to Enhance the Prediction Accuracy of Local Scour Depth at Complex Bridge Piers

A Hybrid Intelligence Approach to Enhance the Prediction Accuracy of Local Scour Depth at Complex Bridge Piers

Turbulence Characteristics before and after Scour Upstream of a Scaled-Down Bridge Pier Model

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

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