Assessing the Implications of Sand Boils for Backward Erosion Piping Risk

Schaefer, Jeffrey A.; O’Leary, Timothy M.; Robbins, Bryant A.

doi:10.1061/9780784480724.012

Cited by 6 publications

(6 citation statements)

References 3 publications

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“…Similar conditions have been reported in laboratory experiments by Van Beek et al., 37 where equilibrium was observed in pipe formation and the head was required to be increased to study further pipe progression. Similarly, Schaefer et al 15 . report piping in multiple embankments along the river Mississippi where pipe initiation had not progressed to failure.…”

Section: Laboratory Investigation Of 2d Seepage Flow In a Domain Consisting A Predefined Piping Zonementioning

confidence: 98%

“…Contrary to initial belief that once BEP initiates at the downstream face, it progresses rapidly upstream and causes failure, 13 recent laboratory studies 14 suggest that this is not always the case and the rate of BEP progression is dependent on grain size. Furthermore, field observations 15 of sand boils on the Mississippi river provide evidence that not all BEP initiations progress to failure. These findings motivate the need and possibility of designing a method for the detection of the extent of BEP.…”

Section: Introductionmentioning

confidence: 99%

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Hamiltonian Monte Carlo for Simultaneous Interface and Spatial Field Detection (HMCSISFD) and its application to a piping zone interface detection problem

Koch

Osugi

Fujisawa

et al. 2021

Num Anal Meth Geomechanics

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In practice, inverse problems related to explicit interface detection rely on scarcely available information of the background continuous spatial fields. To accurately carry on inversion in such cases, a method called Hamiltonian Monte Carlo for Simultaneous Interface and Spatial Field Detection (HMCSISFD) is developed. Update of the interface parameters is done in a reversible mesh moving framework leading to a change in domain geometry which forces a recomputation of the spatial field covariance function as it is domain dependent. The eigenvalue problem and its derivatives w.r.t. the parameters are solved again on the updated domain to enable dimensionality reduction of the spatial field parameters through the use of the truncated Karhunen‐Loève expansion. This simultaneous parameter update procedure is applied to the detection of the interface and spatial field properties of a piping zone. Inversion is done considering hydraulic head and discharge rate data, with a priori known observation noise, from a carefully designed laboratory seepage flow experiment in a domain consisting a predefined piping zone. The static pipe in the experiment is representative of a pipe in equilibrium beneath an embankment/levee where the current hydraulic gradient isn't sufficient to cause further pipe progression. Markov chains obtained from the HMCSISFD method show convergence and the true interface and spatial field parameters are found to lie well within the 95% high probability density regions and the 0.05 and 0.95 quantiles, respectively, provided a proper choice for the observation noise is made.

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Section: Laboratory Investigation Of 2d Seepage Flow In a Domain Consisting A Predefined Piping Zonementioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

Hamiltonian Monte Carlo for Simultaneous Interface and Spatial Field Detection (HMCSISFD) and its application to a piping zone interface detection problem

Koch

Osugi

Fujisawa

et al. 2021

Num Anal Meth Geomechanics

View full text Add to dashboard Cite

show abstract

“…The Sellmeijer model was validated to perform good predictions for all laboratory scales with 2D configurations, but overpredicts the critical gradient in a 3D configuration [23] even after being recently adapted based on the results of additional experiments [24]. The Hoffmans model is used to calculate the critical hydraulic gradient under the condition that backward erosion leads to dike failure, but only a small percentage of BEP initiation cases can progress to failure conditions through field evidence [25,26]. For these reasons, assessing the hydraulic conditions related to the stages of BEP is of interest.…”

Section: Literature Reviewmentioning

confidence: 99%

Modelling Initiation Stage of Backward Erosion Piping through Analytical Models

Luo

Rice

Peng

et al. 2022

Land

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Backward erosion piping, which is one of the leading causes of levee and dam failures, is more likely to occur when a flow is concentrated on a defect with an overlying low-permeability layer and a shallow erosion channel forming and progressing towards a seepage source. Two analytical models are presented to allow assessment of the seepage regime on the initiation stage of BEP. The variations between the two models are analysed, indicating that the effects of soil loosening have a major role in the assessment of BEP mechanisms. This paper also describes the reliability of using Xiao’s model in 3D analysis, which includes results and parametric analysis. Although the head in front of the tip of the erosion channel can be predicted by this accepted calculation model, which is essential for laboratory work on this topic, the model also has range and boundary limitations that need to be addressed for general prediction when BEP progresses with channel development.

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“…Among them, sand boils, also known as sand volcanoes, are one of the most common and dangerous hazards, especially during floods, which occur when the velocity of water flowing from the flood side to the protected side is sufficiently large to erode the soil and allow sand and water to seep through the soil surface. These formations provide evidence for the presence of weak points in a levee [2], [3], indicating the potential for dangerous soil erosion. Hence, it is crucial to detect them precisely to identify new sand boils and monitor their growth for flood-fighting and levee-monitoring purposes during floods.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Approach for Accurate Segmentation of Sand Boils in Levee Systems

Panta,

Hoque,

Niles

et al. 2023

IEEE Access

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Sand boils can contribute to the liquefaction of a portion of the levee, leading to levee failure. Accurately detecting and segmenting sand boils is crucial for effectively monitoring and maintaining levee systems. This paper presents SandBoilNet, a fully convolutional neural network with skip connections designed for accurate pixel-level classification or semantic segmentation of sand boils from images in levee systems. In this study, we explore the use of transfer learning for fast training and detecting sand boils through semantic segmentation. By utilizing a pretrained CNN model with ResNet50V2 architecture, our algorithm effectively leverages learned features for precise detection. We hypothesize that controlled feature extraction using a deeper pretrained CNN model can selectively generate the most relevant feature maps adapting to the domain, thereby improving performance. Experimental results demonstrate that SandBoilNet outperforms state-of-the-art semantic segmentation methods in accurately detecting sand boils, achieving a Balanced Accuracy (BA) of 85.52%, Macro F1-score (MaF1) of 73.12%, and an Intersection over Union (IoU) of 57.43% specifically for sand boils. This proposed approach represents a novel and effective solution for accurately detecting and segmenting sand boils from levee images toward automating the monitoring and maintenance of levee infrastructure.

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Assessing the Implications of Sand Boils for Backward Erosion Piping Risk

Cited by 6 publications

References 3 publications

Hamiltonian Monte Carlo for Simultaneous Interface and Spatial Field Detection (HMCSISFD) and its application to a piping zone interface detection problem

Hamiltonian Monte Carlo for Simultaneous Interface and Spatial Field Detection (HMCSISFD) and its application to a piping zone interface detection problem

Modelling Initiation Stage of Backward Erosion Piping through Analytical Models

Deep Learning Approach for Accurate Segmentation of Sand Boils in Levee Systems

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