Multi-lab investigation of the effect of debris composition on bridge clogging during floods

Conference Paper (2024)
Author(s)

Lisa Burghardt (RWTH Aachen University)

Daan Poppema (TU Delft - Coastal Engineering)

Loïc Benet (Université de Liège)

D. Wüthrich (TU Delft - Hydraulic Structures and Flood Risk)

Sébastien Erpicum (Université de Liège)

Elena Maria Klopries (RWTH Aachen University)

Research Group
Hydraulic Structures and Flood Risk
DOI related publication
https://doi.org/10.3929/ethz-b-000676025
More Info
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Publication Year
2024
Language
English
Research Group
Hydraulic Structures and Flood Risk
Pages (from-to)
791-800
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Abstract

During the European flood of 2021, large debris accumulations were observed at numerous bridges, causing backwater rise, increased upstream flooding, and extended damage. To date, debris accumulation studies mainly focused on debris consisting of logs, at bridge piers or debris racks. However, during the 2021 flood, debris contained a large share of man-made materials in various shapes, often reaching the bridge deck and railing. Therefore, flume experiments on debris accumulation at bridges were conducted at three laboratories in Belgium, Germany and the Netherlands. Hereby, we investigated how backwater rise depends on flow conditions and on debris composition – using debris mixtures of 75% logs with either 25% cubes or 25% plates. Results showed that mixtures with plates caused 1.8 – 2.9 times more backwater rise than those with cubes. This means that previous studies on natural log accumulations may substantially underestimate backwater rise at debris accumulations with e.g. building rubble during flood events. Almost no backwater rise occurred below approximately Fr = 0.2, after which backwater rise increased with the Froude number. Comparison of results between labs agreed relatively well, with backwater rise under the same conditions varying often by 10% to 35%. However, the results of a single series of experiments were higher by up to a factor 2.5. This implies that any multi-flume or multi-lab study should ensure sufficient overlap between test conditions, rather than a pure workload split. Moreover, the observed inter-lab variability implies that multi-lab setups can increase confidence for the generalization of test results to real-world conclusions.