Benefits of Compiling and Analyzing Hydraulic-Design Data for Bridges

Benedict, Stephen T.; Knight, Thomas P

doi:10.1177/03611981211023757

Cited by 5 publications

(3 citation statements)

References 7 publications

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“…The original PSDB-2014 database [26] contains no information regarding the river widths. Benedict and Knight [40] have applied basin data to correlate known channel widths against drainage area, flow depth, and stream slopes. To validate filtering criteria for large rivers, correlation by Benedict and Knight [40] was used herein to calculate channel widths, of which 69% are greater than 30 m.…”

Section: Data Filteringmentioning

confidence: 99%

Analysis of Variables Influencing Scour on Large Sand-Bed Rivers Conducted Using Field Data

et al. 2023

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Throughout the lifespan of a bridge, morphological changes in the riverbed affect the variable action-imposed loads on the structure. This emphasizes the need for accurate and reliable data that can be used in model-based projections targeted for the identification of risk associated with bridge failure induced by scour. The aim of this paper is to provide an analysis of scour depth estimation on large sand-bed rivers under the clear water regime, detect the most influential (i.e., explanatory) variables, and examine the relationship between them and scour depth as a response variable. A dataset used for the analysis was obtained from the United States Geological Survey’s extensive field database of local scour at bridge piers, i.e., the Pier-Scour Database (PSDB-2014). The original database was filtered to exclude the data that did not reflect large sand-bed rivers, and several influential variables were omitted by using the principal component analysis. This reduction process resulted in 10 influential variables that were used in multiple non-linear regression scour modeling (MNLR). Two MNLR models (i.e., non-dimensional and dimensional models) were prepared for scour estimation; however, the dimensional model slightly overperformed the other one. According to the Pearson correlation coefficients (r), the most influential variables for estimating scour depth were as follows: Effective pier width (r = 0.625), flow depth (r = 0.492), and critical and local velocity (r = 0.474 and r = 0.436), respectively. In the compounded hydraulic-sediment category, critical velocity had the greatest impact (i.e., the highest correlation coefficient) on scour depth in comparison to densimetric Froude and critical Froude numbers that were characterized by correlation coefficients of r = 0.427 and r = 0.323, respectively. The remaining four variables (local and critical bed shear stress, Froude number, and particle Reynolds number) exhibited a very weak correlation with scour depth, with r < 0.3.

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Section: Data Filteringmentioning

confidence: 99%

Analysis of Variables Influencing Scour on Large Sand-Bed Rivers Conducted Using Field Data

et al. 2023

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“…Recently, failure scenarios were studied and systematized for small bridges in case of extreme events such as floods [15]. However, the performance of bridges against varying flooding scenarios is usually unknown, as the design is typically carried out on a deterministic basis [16]. On the contrary, fragility curves link the expected damage to a range of hazard intensities [17].…”

Section: Introductionmentioning

confidence: 99%

Fragility Analysis Based on Damaged Bridges during the 2021 Flood in Germany

Pucci,

Eickmeier,

Sousa

et al. 2023

Applied Sciences

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Floods trigger the majority of expenses caused by natural disasters and are also responsible for more than half of bridge collapses. In this study, empirical fragility curves were generated by referring to actual failures that occurred in the 2021 flood in Germany. To achieve this, a calibrated hydraulic model of the event was used. Data were collected through surveys, damage reports and condition ratings from bridge owners. The database comprises 250 bridges. The analysis revealed recurrent failure mechanisms belonging to two main categories: those induced by scour and those caused by hydraulic forcing. The severity of the damage was primarily dependent on the bridge typology and, subsequently, on the deck’s weight. The analysis allowed us to draw conclusions regarding the robustness of certain bridge typologies compared to others for a given failure mechanism. The likelihood of occurrence of the triggering mechanism was also highlighted as a factor to consider alongside the damage probability. This study sheds light on existing vulnerabilities of bridges to river floods, discussing specific areas in which literature data are contradictory. The paper also strengthens the call for a shift towards a probabilistic approach for estimating hydraulic force in bridge design and assessment.

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“…With reference to the latter issue, major civil engineering works subject to risk of flooding, such as bridges (Benedict & Knight, 2021), storm sewers (Sun et al, 2011), dam-drainage systems (Khaddor et al, 2021), levees (Huang et al, 2015), and other hydraulic structures for river flood control (Cipollini et al, 2021;Lendering et al, 2019;Scussolini et al, 2016) are all designed to withstand relatively extreme events, with large return periods (Ponce, 1989;Rasekh et al, 2010;Sayers et al, 2013), depending on their strategic importance and the threat that their failure would pose to human lives and properties (Cipollini et al, 2021;Lendering et al, 2019;Morrison et al, 2018;Shah et al, 2018;Tung, 2005;Vogel & Castellarin, 2017). However, there are many other applications where accurate prediction of frequent floods is critical.…”

Section: Introductionmentioning

confidence: 99%

Underestimation of Frequent Floods when Using Annual Maxima for Frequency Analysis: Drivers, Spatial Variability, and Possible Solutions

Dell'Aira

Cancelliere

Meier

2023

Preprint

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Because they are conceptually unable to consider events at the sub-annual scale, probabilistic flood analyses based on annual maxima (AM) underestimate the actual frequency of frequent floods (with return periods under 5 years), so that peaks-over-threshold (POT) approaches should be preferred. While this has been acknowledged for decades, frequent floods are still estimated too often using AM, probably because the procedure is simpler, and AM series are longer and easier to obtain. However, the negative bias incurred when performing flood frequency with AM can be severe. This affects fields such as river restoration, stream ecology, and fluvial geomorphology, which require a correct characterization of frequent floods. Using hundreds of U.S. watersheds with natural flow regimes, across different climatic and geomorphic conditions, we systematically study the variability in how AM frequency analyses underestimate frequent floods, finding clear spatial patterns. Exploiting the duality between the Generalized Extreme Value and the Generalized Pareto distributions (used for modeling AM and POT, respectively), we identify the drivers of frequent-flood underestimation, studying the influence of the distributions’ shapes. In turn, with the support of an optimal feature-selection technique, we determine the physical drivers explaining underestimation, from a wide spectrum of basin descriptors, investigating their linkages with the distributional characteristics that affect underestimation. A theoretical relationship is derived to infer the underestimation rate, allowing for post-hoc correction of AM-predicted frequent floods, without the need to perform POT frequency analyses. However, this approach underperforms at sites with mixed flood populations.

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Benefits of Compiling and Analyzing Hydraulic-Design Data for Bridges

Cited by 5 publications

References 7 publications

Analysis of Variables Influencing Scour on Large Sand-Bed Rivers Conducted Using Field Data

Analysis of Variables Influencing Scour on Large Sand-Bed Rivers Conducted Using Field Data

Fragility Analysis Based on Damaged Bridges during the 2021 Flood in Germany

Underestimation of Frequent Floods when Using Annual Maxima for Frequency Analysis: Drivers, Spatial Variability, and Possible Solutions

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