Numerical simulation of backward erosion piping in heterogeneous fields
Backward erosion piping (BEP) is one of the major causes of seepage failures in levees. Seepage fields dictate the BEP behaviors and are influenced by the heterogeneity of soil properties. To investigate the effects of the heterogeneity on the seepage failures, we develop a numerical algorithm and c...
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ndltd-arizona.edu-oai-arizona.openrepository.com-10150-6243642017-06-25T03:00:33Z Numerical simulation of backward erosion piping in heterogeneous fields Liang, Yue Yeh, Tian-Chyi Jim Wang, Yu-Li Liu, Mingwei Wang, Junjie Hao, Yonghong Univ Arizona, Dept Hydrol & Atmospher Sci National Engineering Research Center for Inland Waterway Regulation, Chongqing Jiaotong University; Chongqing China Department of Hydrology and Atmospheric Sciences; University of Arizona; Tucson Arizona USA Department of Hydrology and Atmospheric Sciences; University of Arizona; Tucson Arizona USA National Engineering Research Center for Inland Waterway Regulation, Chongqing Jiaotong University; Chongqing China National Engineering Research Center for Inland Waterway Regulation, Chongqing Jiaotong University; Chongqing China Key Laboratory for Water Environment and Resources; Tianjin Normal University; Tianjin China input uncertainty calibration Bayesian uncertainty quantification Backward erosion piping (BEP) is one of the major causes of seepage failures in levees. Seepage fields dictate the BEP behaviors and are influenced by the heterogeneity of soil properties. To investigate the effects of the heterogeneity on the seepage failures, we develop a numerical algorithm and conduct simulations to study BEP progressions in geologic media with spatially stochastic parameters. Specifically, the void ratio e, the hydraulic conductivity k, and the ratio of the particle contents r of the media are represented as the stochastic variables. They are characterized by means and variances, the spatial correlation structures, and the cross correlation between variables. Results of the simulations reveal that the heterogeneity accelerates the development of preferential flow paths, which profoundly increase the likelihood of seepage failures. To account for unknown heterogeneity, we define the probability of the seepage instability (PI) to evaluate the failure potential of a given site. Using Monte-Carlo simulation (MCS), we demonstrate that the PI value is significantly influenced by the mean and the variance of ln k and its spatial correlation scales. But the other parameters, such as means and variances of e and r, and their cross correlation, have minor impacts. Based on PI analyses, we introduce a risk rating system to classify the field into different regions according to risk levels. This rating system is useful for seepage failures prevention and assists decision making when BEP occurs. 2017-04 Article Numerical simulation of backward erosion piping in heterogeneous fields 2017, 53 (4):3246 Water Resources Research 00431397 10.1002/2017WR020425 http://hdl.handle.net/10150/624364 http://arizona.openrepository.com/arizona/handle/10150/624364 Water Resources Research en http://doi.wiley.com/10.1002/2017WR020425 © 2017. American Geophysical Union. All Rights Reserved. AMER GEOPHYSICAL UNION |
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input uncertainty calibration Bayesian uncertainty quantification |
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input uncertainty calibration Bayesian uncertainty quantification Liang, Yue Yeh, Tian-Chyi Jim Wang, Yu-Li Liu, Mingwei Wang, Junjie Hao, Yonghong Numerical simulation of backward erosion piping in heterogeneous fields |
description |
Backward erosion piping (BEP) is one of the major causes of seepage failures in levees. Seepage fields dictate the BEP behaviors and are influenced by the heterogeneity of soil properties. To investigate the effects of the heterogeneity on the seepage failures, we develop a numerical algorithm and conduct simulations to study BEP progressions in geologic media with spatially stochastic parameters. Specifically, the void ratio e, the hydraulic conductivity k, and the ratio of the particle contents r of the media are represented as the stochastic variables. They are characterized by means and variances, the spatial correlation structures, and the cross correlation between variables. Results of the simulations reveal that the heterogeneity accelerates the development of preferential flow paths, which profoundly increase the likelihood of seepage failures. To account for unknown heterogeneity, we define the probability of the seepage instability (PI) to evaluate the failure potential of a given site. Using Monte-Carlo simulation (MCS), we demonstrate that the PI value is significantly influenced by the mean and the variance of ln k and its spatial correlation scales. But the other parameters, such as means and variances of e and r, and their cross correlation, have minor impacts. Based on PI analyses, we introduce a risk rating system to classify the field into different regions according to risk levels. This rating system is useful for seepage failures prevention and assists decision making when BEP occurs. |
author2 |
Univ Arizona, Dept Hydrol & Atmospher Sci |
author_facet |
Univ Arizona, Dept Hydrol & Atmospher Sci Liang, Yue Yeh, Tian-Chyi Jim Wang, Yu-Li Liu, Mingwei Wang, Junjie Hao, Yonghong |
author |
Liang, Yue Yeh, Tian-Chyi Jim Wang, Yu-Li Liu, Mingwei Wang, Junjie Hao, Yonghong |
author_sort |
Liang, Yue |
title |
Numerical simulation of backward erosion piping in heterogeneous fields |
title_short |
Numerical simulation of backward erosion piping in heterogeneous fields |
title_full |
Numerical simulation of backward erosion piping in heterogeneous fields |
title_fullStr |
Numerical simulation of backward erosion piping in heterogeneous fields |
title_full_unstemmed |
Numerical simulation of backward erosion piping in heterogeneous fields |
title_sort |
numerical simulation of backward erosion piping in heterogeneous fields |
publisher |
AMER GEOPHYSICAL UNION |
publishDate |
2017 |
url |
http://hdl.handle.net/10150/624364 http://arizona.openrepository.com/arizona/handle/10150/624364 |
work_keys_str_mv |
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1718463330118533120 |