Explaining the Flood Behavior for the Bridge Collapse Sites

Abstract

Given the increasing intensity and frequency of flood events, and the casualties and cost associated with bridge collapse events, explaining the flood behavior for the collapse sites would be of great necessity. In this study, annual peak flows of two hundred and five watersheds, associated with two hundred and ninety-seven collapse sites, are analyzed. Generalized Extreme Value distribution together with other statistical analyses are used to derive and analyze the shape parameters of the distributions which represent the extremeness of flood events. Random forest mechanism is employed in order to identify the predictor variables (and the associated importance levels) for the shape parameters. Peak flows are also classified in order to find the extremes and the associated return periods. The results indicate that most of the bridge collapse sites across different physiographic regions, i.e., Appalachian Highland, Central Lowland, Coastal Plain, and Interior Highlands, exhibit common characteristics such as (a) variation of important predictor variables, (b) human interference, (c) extremeness of flood events similar to the regions with hydrologic heterogeneity, and (d) frequent occurrence of extreme flows. These results indicate a commonality in flood behavior, as stems from specific settings, for the collapse sites studied. The findings instigate the revisiting of the bridge design practices and guidelines and provide some basis to assess the risk of future collapse.

Understanding the Challenges of Hydrological Analysis at Bridge Collapse Sites

“…The Appalachian Highland region possesses intrinsic risk conditions, including high erosion at bed and bank and/or debris jams [45,49]. In a recent study on 147 collapse sites within the Appalachian Highland region, including the current study sites, it was found that the region exhibits the characteristics of hydrologic heterogeneity [50]. For the Appalachian Highland region, forty predictor variables are identified, and among them, certain climate and topography variables are found to be the most important variables (ranking 1 to 10) in predicting flood behavior [50].…”

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].…”

KI/ML‐gestützte Auswertung und Interpretation der IABSE‐Brückeneinsturzdatenbank

“…Verwendet man den Faktor von 20 % auf die 270 jährlichen Einstürze, kommt man auf bis zu 1350 Brückeneinstürze pro Jahr. [33] nennt jährliche Brückeneinstürze in den USA durch Auskolkung zwischen 20 und 100. Das würde mit den gleichen Annahmen wie im vorangegangenen Absatz weltweite jährliche Brückeneinstürze von 100/15 × (20 …100) × 5 = 667 … 3333 ergeben.…”