Simulation of landslide-induced surges and analysis of impact on dam based on stability evaluation of reservoir bank slope

Liu, Yaoru; Wang, Xiaoming; Wu, Zheng; Zhu, He; Yang, Qiang

doi:10.1007/s10346-018-1001-5

Cited by 23 publications

(8 citation statements)

References 21 publications

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“…Geophysical Research Letters interface (Liu et al, 2018;Mountjoy et al, 2018;Wang et al, 2016). We will discuss the contribution of the collapse of river deltas in generating the tsunami in the discussion section.…”

Section: 1029/2019gl082717mentioning

confidence: 99%

Source Model for the Tsunami Inside Palu Bay Following the 2018 Palu Earthquake, Indonesia

Gusman

Supendi

Nugraha

et al. 2019

Geophysical Research Letters

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On 28 September 2018, a strike‐slip earthquake occurred in Palu, Indonesia, and was followed by a series of tsunami waves that devastated the coast of Palu Bay. The tsunami was recorded at the Pantoloan tide gauge station with a peak amplitude of ~2 m above the water level and struck at high tide. We use the Pantoloan tsunami waveform and synthetic aperture rada displacement data in a joint inversion to estimate the vertical displacement around the narrow bay. Our inversion result suggests that the middle of the bay was uplifted up to 0.8 m, while the other parts of the bay subsided by up to 1 m. However, this seafloor displacement model alone cannot fully explain the observed tsunami inundation. The observed tsunami inundation heights and extents could be reproduced by a tsunami inundation simulation with a source model that combined the estimated vertical displacement with multiple subaerial‐submarine landslides.

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Section: 1029/2019gl082717mentioning

confidence: 99%

Source Model for the Tsunami Inside Palu Bay Following the 2018 Palu Earthquake, Indonesia

Gusman

Supendi

Nugraha

et al. 2019

Geophysical Research Letters

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“…Based on monitoring data, a faulted rock mass with an approximate volume of 3 × 10 7 m 3 showed signs of general displacement [26]. Although there is a very small probability, should the mass drop into the reservoir, it would generate large waves which may well destroy the nearby arch dam ( [27]). In this situation, estimating the characteristics of the potential waves from information on the potential landslide (which is still at rest on the slope) is more than warranted.…”

Section: Introductionmentioning

confidence: 99%

Using a Data Driven Approach to Predict Waves Generated by Gravity Driven Mass Flows

Meng

Ancey

2020

Water

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When colossal gravity-driven mass flows enter a body of water, they may generate waves which can have destructive consequences on coastal areas. A number of empirical equations in the form of power functions of several dimensionless groups have been developed to predict wave characteristics. However, in some complex cases (for instance, when the mass striking the water is made up of varied slide materials), fitting an empirical equation with a fixed form to the experimental data may be problematic. In contrast to previous empirical equations that specified the mathematical operators in advance, we developed a purely data-driven approach which relies on datasets and does not need any assumptions about functional form or physical constraints. Experiments were carried out using Carbopol Ultrez 10 (a viscoplastic polymeric gel) and polymer–water balls. We selected an artificial neural network model as an example of a data-driven approach to predicting wave characteristics. We first validated the model by comparing it with best-fit empirical equations. Then, we applied the proposed model to two scenarios which run into difficulty when modeled using those empirical equations: (i) predicting wave features from subaerial landslide parameters at their initial stage (with the mass beginning to move down the slope) rather than from the parameters at impact; and (ii) predicting waves generated by different slide materials, specifically, viscoplastic slides, granular slides, and viscoplastic–granular mixtures. The method proposed here can easily be updated when new parameters or constraints are introduced into the model.

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“…The time series of dam horizontal displacement is one of the main projects of dam deformation safety monitoring 21 . Studying the variation characteristics of the horizontal displacement time series of dams and establishing a reliable displacement numerical model for the analysis of dam deformation behavior are of great importance 22,23 . According to the existing theory, the horizontal displacement of concrete dams is mainly composed of three components: water pressure

δ_{H}

, temperature

δ_{T}

, and aging

δ_{θ}

24 .…”

Section: Overall Frameworkmentioning

confidence: 99%

Hybrid model for concrete dam deformation in consideration of residual correction by frequency division

Wei

Luo

et al. 2022

Structural Contr & Hlth

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The traditional statistical models for concrete dam deformation do not consider the effective contribution to residual sequence and thus results in the low accuracy of deformation prediction of the monitoring model and in the insufficient prediction of dam deformation behavior.This study proposes a combined model for concrete dam deformation, considering residual correction by frequency division. The horizontal displacement of concrete dams is driven by water pressure, temperature, and aging factors. In this study, an accurate value of water pressure deformation is obtained by finite element calculation, and then the hybrid model is constructed. However, the temperature and aging components hardly obtain accurate information values from the traditional model. Therefore, a combined model is constructed to extract additional detailed information that characterizes the dam deformation from the residual sequence. Ensemble empirical mode decomposition is selected to analyze and denoise the residual sequence adaptively, and the consecutive mean square error is used to determine the boundary points of highand low-frequency components. Then, the combined model based on the extreme learning machine method optimized by particle swarm optimization and seasonal autoregressive integrated moving average is constructed for highand low-frequency sequences. Lastly, the prediction results of different subitems are organically integrated. Results of a case study in engineering demonstrate that the accuracy of the combined model for concrete dam deformation is higher than that of the classical numerical model, and the model can effectively overcome noise interference in the monitoring sequence. The proposed combined model for deformation is reliable for analyzing and judging the dam monitoring time series, which can provide new ideas and reference for the data processing of dam deformation monitoring.

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Simulation of landslide-induced surges and analysis of impact on dam based on stability evaluation of reservoir bank slope

Cited by 23 publications

References 21 publications

Source Model for the Tsunami Inside Palu Bay Following the 2018 Palu Earthquake, Indonesia

Source Model for the Tsunami Inside Palu Bay Following the 2018 Palu Earthquake, Indonesia

Using a Data Driven Approach to Predict Waves Generated by Gravity Driven Mass Flows

Hybrid model for concrete dam deformation in consideration of residual correction by frequency division

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