Analysis of Peak Flow Distribution for Bridge Collapse Sites

Abstract

Bridge collapse risk can be evaluated more rigorously if the hydrologic characteristics of bridge collapse sites are demystified, particularly for peak flows. In this study, forty-two bridge collapse sites were analyzed to find any trend in the peak flows. Flood frequency and other statistical analyses were used to derive peak flow distribution parameters, identify trends linked to flood magnitude and flood behavior (how extreme), quantify the return periods of peak flows, and compare different approaches of flood frequency in deriving the return periods. The results indicate that most of the bridge collapse sites exhibit heavy tail distribution and flood magnitudes that are well consistent when regressed over the drainage area. A comparison of different flood frequency analyses reveals that there is no single approach that is best generally for the dataset studied. These results indicate a commonality in flood behavior (outliers are expected, not random; heavy-tail property) for the collapse dataset studied and provides some basis for extending the findings obtained for the 42 collapsed bridges to other sites to assess the risk of future collapses.Water 2020, 12, 52 2 of 25 are used to directly define an extreme value distribution; and peaks over-threshold (POT), in which distributions are fit to both the frequency of floods above a threshold and their magnitudes. The U.S. Water Resources Council requires the use of log-Pearson type 3 (LP3) distributions for the annual maxima approach, although the generalized extreme value distribution (GEV) seems to be the most widely used model for extreme events [7]. The major benefit of the GEV model is its ability to fit highly skewed data [7]. Therefore, recent hydrologic research has focused on explaining the parameters of the GEV distribution of streamflow extremes [8][9][10][11][12][13][14].If annual maximum exceedances are assumed to be GEV-distributed, the POT exceedances are assumed to be generalized Pareto (GP)-distributed [15], following recommendations in the field of statistics [16,17]. Fitting the GP distribution to exceedances over a high threshold and also estimating the frequency of exceeding the threshold by fitting a Poisson distribution allows for the simultaneous fitting of parameters concerning both the frequency and intensity of extreme events. Compared to the annual maxima approach, therefore, the main advantage of POT modeling is that it allows for a more rational selection of events to be considered as "floods" and is not confined to only one event per year. The POT approach considers a wide range of events and provides the possibility of controlling the number of flood occurrences to be included in the analysis by appropriate selection of the threshold. However, the POT approach remains under-employed mainly because of the complexities associated with the choice of threshold and the selection of criteria for retaining flood peaks (Lang et al. (1999)). Nonetheless, threshold selection is tightly linked to the choice of the process distribution, to t...

The Science behind Scour at Bridge Foundations: A Review

“…However, not infrequently it is observed that bridges collapse under floods having very low return periods. In general, the floods that cause failure due to scour are considerably scattered, in a range between 1 and more than 1000 years [9,169,170]; therefore, scour accumulation may have an active role in those failures occurring for floods with low return periods [144]. Figure 8 describes the empirical relationship between the return periods (T R ) of flood events producing bridge collapse and the bridge age, resulting from a study performing the historical analysis of bridge collapses in the U.S.A. [144].…”

At‐a‐station hydraulic geometry for reaches with bridge collapse events

“…Physiographic settings, such as Appalachian Highland, Central Lowland, and Coastal Plain, do possess high erodible beds and/or banks along with other disturbances such as debris jams, backwaters from Bays and/or intensive human interventions (Johnson, 2006; Simon & Rinaldi, 2000). Under such circumstances, the river sections will not be resilient to lower flows as would have been expected, and therefore collapse risk even at low flows (or flows with lower return periods) can be expected (Ashraf & Flint, 2020, 2021). Since all these collapse‐inducing conditions can manifest in a specific trend of hydraulic geometry, as derived for the study sites with limited human interference, certain trends in AHG can be arguably used as a criterion for risk analysis along with certain flow characteristics, such as low return period of heavy tail flows.…”

“…An α-value is used for determining the significance of Kendall's τ (Villarini et al, 2009). To check the behavior of the bank full flow, the shape parameter value of the flow distribution is extracted (Ashraf & Flint, 2020) increase more than the exponential order as compared to the low flows in the light tail. To check whether the bank full flow falls within the heavy or light tail category for a heavy tail flow distribution, this study employs the Jiang classification scheme (Jiang, 2012).…”

“…An α‐value is used for determining the significance of Kendall's τ (Villarini et al, 2009). To check the behavior of the bank full flow, the shape parameter value of the flow distribution is extracted (Ashraf & Flint, 2020). Positive values of the shape parameter indicate heavy tail distribution whereas negative values indicate light tail distribution.…”

The Sustainable Development of Bridges in China: Collapse Cause Analysis, Existing Management Dilemmas and Potential Solutions

“…Flow conditions vary in different regions, making flooding a major cause of damage to highway and railway bridges. Bridge collapses due to floods are not uncommon, and frequent flood disasters, inherent deficiencies in foundation design, and human-induced changes in riverbeds are the primary culprits [57][58][59][60][61]. Data maintained by The New York State Department of Transportation show that, between 1992 and 2014, hydraulic-induced failure caused 55.4% of the 428 bridge collapses in their database [62], and a historical analysis of hydraulic bridge collapses is performed to check the relationship between bridge collapses and flood frequency or intensity [63].…”