Measuring and estimating the impact pressure of debris flows on bridge piers based on large-scale laboratory experiments

Wang, Dongpo; Chen, Zheng; He, Siming; Liu, Yang; Tang, Hao

doi:10.1007/s10346-018-0944-x

Cited by 60 publications

(27 citation statements)

References 33 publications

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“…Based on the data from that study, Wu et al (2018) [2] then conducted another analysis and proposed a new optimization method to determine the rainfall intensity-duration threshold that can minimize the probability of false alarm and missed alarm combined. In addition, [3] presented a joint probability model that can capture the statistical characteristics of the maximum impact pressure and total sediment discharge of a debris flow, which can help develop the design guideline for constructing a barrier structure to mitigate the damage caused by debris flows.…”

Section: Debris Flowmentioning

confidence: 99%

A Logistic Model Predicting Occurrence Probability of Debris Flow

Wang¹

2019

AJCE

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This paper presents a logistic model for predicting the occurrence probability of debris flows based on rainfall intensity and duration. The data from a total of 354 rainfall events were used to calibrate the model, among which 249 were triggering a debris flow while 105 were not. The model will be useful to the decision making of debris flow early warning in the future. That is, given the estimated occurrence probability = 70% subject to a combination of rainfall intensity and duration, there is a 30% probability that the early warning will be a false alarm. By contrast, if decision makers decide not to issue an early warning, then there is a 70% chance leading to a missed alarm. Subsequently, integrating the consequences of missed alarm and false alarm into the equation, the respective risks can be computed, based on which decision makers can make a more robust decision whether an early warning is needed or not by choosing the scenario with a lower risk.

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Section: Debris Flowmentioning

confidence: 99%

A Logistic Model Predicting Occurrence Probability of Debris Flow

Wang¹

2019

AJCE

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Section: Similarity Principlementioning

confidence: 99%

“…Dimensional analysis is an important way to understand the physical links and relationships among the factors involved in a natural phenomenon or an engineering problem [58,65]. Dimensional analysis can clearly reveal the relationships between experimental results and experimental factors [7,58,66]. In the flume tests that we used in the present study, we accounted for the following experimental factors that describe the distribution of the flow density: the spatial coordinates (x, y), where x and y represent the distances parallel to the flume and perpendicular to the flow direction, respectively; volume of the debris flow (V 0 ); characteristic depth of the fluid (h); characteristic diameter of the particles (d); channel length (L); channel width (W); slope angle (α); fluid density (ρ f ); density of solid material (ρ s ); dynamic viscosity coefficient (µ); gravitational acceleration (g); elastic modulus of grain (E s ); coefficient of friction (c); initial flow velocity (v 0 ).…”

Section: Similarity Principlementioning

confidence: 99%

“…In addition, R e and F r appear in the distribution function for the dynamic characteristics, and must be equal in the model being developed and the actual process it represents. To achieve this goal, it's necessary to build a model of the study system that has the same scale as the system, and this is unrealistic in practice [58]. To achieve accurate experimental results, similarity methods are used [58].…”

Section: Similarity Principlementioning

confidence: 99%

“…To achieve this goal, it's necessary to build a model of the study system that has the same scale as the system, and this is unrealistic in practice [58]. To achieve accurate experimental results, similarity methods are used [58]. To meet this goal for the geometric similarity and kinematic similarity, R e and F r are often used [25,58].…”

Section: Similarity Principlementioning

confidence: 99%

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Effect of Density and Total Weight on Flow Depth, Velocity, and Stresses in Loess Debris Flows

Shu

et al. 2018

Water

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Debris flows that involve loess material produce important damage around the world. However, the kinematics of such processes are poorly understood. To better understand these kinematics, we used a flume to measure the kinematics of debris flows with different mixture densities and weights. We used sensors to measure pore fluid pressure and total normal stress. We measured flow patterns, velocities, and depths using a high-speed camera and laser range finder to identify the temporal evolution of the flow behavior and the corresponding peaks. We constructed fitting functions for the relationships between the maximum values of the experimental parameters. The hydrographs of the debris flows could be divided into four phases: increase to a first minor peak, a subsequent smooth increase to a second peak, fluctuation until a third major peak, and a final continuous decrease. The flow depth, velocity, total normal stress, and pore fluid pressure were strongly related to the mixture density and total mixture weight. We defined the corresponding relationships between the flow parameters and mixture kinematics. Linear and exponential relationships described the maximum flow depth and the mixture weight and density, respectively. The flow velocity was linearly related to the weight and density. The pore fluid pressure and total normal stress were linearly related to the weight, but logarithmically related to the density. The regression goodness of fit for all functions was >0.93. Therefore, these functions are accurate and could be used to predict the consequences of loess debris flows. Our results provide an improved understanding of the effects of mixture density and weight on the kinematics of debris flows in loess areas, and can help landscape managers prevent and design improved engineering solutions.

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Mureneinwirkungen auf Massivbrückenüberbauten

Friedl¹,

Wernhart²,

Scheidl³

et al. 2023

Beton und Stahlbetonbau

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Brückenbauwerke im alpinen Raum unterliegen verschiedenen Naturgefahren. Dazu zählen auch Muren. Murenereignisse führen nachweisbar auch zur Zerstörung von Brücken. Um Brücken für solche Einwirkungen zu bemessen, sind Angaben zur Anprallkraft von Muren notwendig. Für Murenanpralle gegen Pfeiler liegen umfangreiche, sowohl miniaturisierte als auch Realgrößenversuche vor. Für Brückenüberbauten sind bisher keine solchen Versuche bekannt. In diesem Beitrag werden miniaturisierte Murenanprallversuche an Massivbrückenquerschnitten vorgestellt. Basierend auf den Versuchsergebnissen werden Empfehlungen für die Bemessung von Massivbrücken in exponierten Lagen gegeben.

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Measuring and estimating the impact pressure of debris flows on bridge piers based on large-scale laboratory experiments

Cited by 60 publications

References 33 publications

A Logistic Model Predicting Occurrence Probability of Debris Flow

A Logistic Model Predicting Occurrence Probability of Debris Flow

Effect of Density and Total Weight on Flow Depth, Velocity, and Stresses in Loess Debris Flows

Mureneinwirkungen auf Massivbrückenüberbauten

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