Reference Guide for Applying Risk and Reliability-Based Approaches for Bridge Scour Prediction (2013) / Chapter Skim
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27 3.1 Introduction Bridge scour processes, including pier, abutment, and contraction scour, have been well researched over the past several decades and equations have been developed to estimate scour depths for each of the scour components. The vulnerability of a bridge to scour is due to the existence of a weakness or a design that can lead to an unexpected, undesirable event compromising the bridge safety.
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28 Reference Guide for Applying Risk and Reliability-Based Approaches for Bridge Scour Prediction to assess parameter uncertainty and observed data (laboratory and field) was used to assess model uncertainty.
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Evaluating Uncertainty Associated with Scour Prediction 29 As discussed in Chapter 4, the bias and COV for each of the scour equations were evaluated based on available laboratory and field data, and the reliability index, b, was determined for each scour equation. Because the determination of bias and COV requires observed data, the limitations of each data source need to be addressed.
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30 Reference Guide for Applying Risk and Reliability-Based Approaches for Bridge Scour Prediction aerial photos as described in NCHRP Report 533: Handbook for Predicting Stream Meander Migration (Lagasse et al.
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Evaluating Uncertainty Associated with Scour Prediction 31 When rasTool© is started, rasTool© initializes a double-precision random-number generator (RNG) , seeded with the computer clock time, to generate a large (~1055)
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32 Reference Guide for Applying Risk and Reliability-Based Approaches for Bridge Scour Prediction 3.4.3 Discharge A lognormal discharge distribution about its expected value (mean in logarithm transform) was assumed.
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Evaluating Uncertainty Associated with Scour Prediction 33 The COV values in Table 3.4 were multiplied by the expected value discharge in natural logarithm space to determine discharge lognormal standard deviation values for each bridge type (small, medium, or large) and hydrologic uncertainty scenario (low, medium, or high)
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34 Reference Guide for Applying Risk and Reliability-Based Approaches for Bridge Scour Prediction 3.4.6 Summary Each Monte Carlo realization generated a set of randomized input variables based on the underlying input variables and summary statistics discussed in this section. These variables were assigned to the HEC-RAS model and a HEC-RAS model run was performed for each realization.
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Evaluating Uncertainty Associated with Scour Prediction 35 confidence limits and an "expected probability" estimate of discharge. As defined by Bulletin 17B, the confidence limits are one-sided, meaning that 95% of the estimates of discharge are greater than the lower bound and 95% less than the upper bound.
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36 Reference Guide for Applying Risk and Reliability-Based Approaches for Bridge Scour Prediction 3.5.2.3 Manning n Uncertainty As described in Johnson (1996) , numerous methods have been used to describe the uncertainty in Manning n estimates.
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Evaluating Uncertainty Associated with Scour Prediction 37 the 77 engineers corresponds well to the expected Manning n for the nine rivers at 100-year flood stage. The COV of the log-transformed data was 0.082.
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38 Reference Guide for Applying Risk and Reliability-Based Approaches for Bridge Scour Prediction not be ignored. Johnson found that several types of distributions have been used to describe channel and friction slope, including uniform, normal, triangular, and lognormal.
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Evaluating Uncertainty Associated with Scour Prediction 39 The data in Figure 3.3 also illustrate the variability in water surface measurements at the Butte City gage. The standard deviation of the differences in the observed values versus the trend line is 0.49 ft.
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40 Reference Guide for Applying Risk and Reliability-Based Approaches for Bridge Scour Prediction The statistical properties describing model uncertainty, bias and COV, were defined. In addition, model uncertainty in relation to the key hydraulic parameters of discharge (hydrologic uncertainty)
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