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CHAPTER 2
Research Approach
An extensive information and data search was conducted at number of equilibrium and scour evolution equations from
the onset of the project. The search included (1) questionnaires 23 and 10 to 17 and 6, respectively.
to state DOT engineers, consulting engineers, FHWA and A method for assessing the quality of the measured data
U.S. Geological Survey (USGS) engineers, and university was developed. The measurement of local scour depths and the
researchers; (2) electronic literature searches; and (3) emails sediment and flow parameters on which these depths depend
and telephone calls to personal contacts working in this field in is not straightforward. For example, the bed shear stress,
Japan, Portugal, India, Malaysia, Singapore, United Kingdom, which is one of the important parameters, is usually estimated
Canada, New Zealand, Australia, and the United States. The (or inferred) from the depth-averaged approach velocity. It is
questionnaire and some of the response statistics are presented assumed that the flow is fully developed and that the depth-
in Appendix A (available on the NCHRP Report 682 summary averaged velocity can be obtained from a point measurement
web page: www.trb.org/Main/Blurbs/164161.aspx). or that the sectional averaged velocity can be obtained from
The search resulted in the acquisition of 569 laboratory the volumetric discharge rate in the flume. If the distance from
equilibrium scour data points, 943 field data points, and the flume entrance to the test section is too short, the flow will
142 laboratory scour evolution data sets. The important vari- not be fully developed and the bed shear stress will most likely
ables were grouped in dimensionless groups that represent be larger than computed for a fully developed flow. Even small
ratios of the pertinent physical phenomena. For example, the errors in discharge or velocity measurement in the clear-water
Froude number, V1 y 1g , which is the ratio of flow velocity scour regime can have major effects on equilibrium scour
to the propagation speed of a shallow water surface wave, is depths and scour depth predictions. These examples illustrate
important in open channel flow. For sediment transport and potential problems associated with making accurate local scour
local sediment scour, the ratio of flow velocity to the velocity measurements in the laboratory. Accurate scour measurements
required to initiate sediment movement on a flat stream bed, at prototype structures in the field are even more difficult to
V1/Vc, is important, etc. The range of the dimensionless groups make. In addition to the measurement problems resulting
more commonly used to characterize local scour covered by from the temporal and spatial variations in flow velocities,
the compiled data sets is given in Table 1. the soil properties can vary spatially in all directions as well.
The search also produced 23 equilibrium local scour and Perhaps the greatest problem with most field data is the lack
10 scour evolution predictive equations/methods. of information regarding the level of maturity of the scour
Analysis of the search results showed that there is only hole at the time of measurement, i.e., how near the measured
limited information on local (equilibrium and evolution) scour depth is to its equilibrium value for the flow, sediment,
scour depths for wide or long skewed piers. Most of the scour and structure conditions at the time of measurement.
prediction equations do, however, state or imply that they are Publications in professional journals often present insuffi-
applicable for these conditions. For this reason, all equilibrium cient detail regarding measurement techniques, instrumenta-
and evolution scour equations/methods that were obtained tion, etc., which makes assessing the quality of the reported data
in the search were analyzed in this study. more difficult. It was necessary to develop a data evaluation
The next step was to perform a preliminary screening of scheme based on deviations from mean values computed using
the predictive equations to exclude those equations/methods all the data. This procedure worked well for the equilibrium
that yielded predictions significantly different from those scour data. Refined laboratory and field data sets were created
for the remaining equations. This screening reduced the for use in evaluating the predictive equations.

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Table 1. Range of values of the dimensionless groups covered
by the compiled data sets.
Data Type y1/a a/D50 V1/Vc V1 / y1g
Equilibrium
0.05 to 21.05 3.65 to 4159 0.40 to 5.99 0.07 to 1.50
Laboratory
Equilibrium Field 0.18 to 9.67 8.33 to 65047 0.13 to 7.58 0.03 to 1.95
Laboratory Scour
0.09 to 11.11 6.72 to 4159 0.46 to 5.99 0.07 to 1.76
Evolution
y1 = the approach water depth V1 = the depth-averaged velocity
a = the structure width Vc = the sediment critical depth-averaged velocity
D50 = the median sediment grain diameter g = the acceleration of gravity.
Evaluation of the equilibrium scour equations was a An initial screening of the scour evolution equations was per-
two-step process. The first step involved using all of the formed by simply evaluating and plotting the predictions for a
equations to evaluate the scour for a range of hypothetical range of input conditions and omitting the equations produc-
(but practical) structure, sediment, and flow conditions and ing drastically different results from the mean produced by all
comparing their results. Equations that produced results of the equations. The remaining equations were then evaluated
that significantly deviated from the mean values were elim- using the scour evolution laboratory data and errors were
inated from further consideration. The refined laboratory computed and presented in graphs.
and field data sets were then used to evaluate the remaining Based on the performance of the equations in these tests,
equations. the best-performing equations/methods were identified and
An initial screening procedure similar to that used for the recommended for predicting equilibrium scour depth and
equilibrium scour data was used for the scour evolution data. scour evolution rates at wide piers and long skewed piers.