Model test on the entrainment phenomenon and energy conversion mechanism of flow-like landslides

Yang, Haiqing; Xing, Shen; Wang, Q.; Li, Z.

doi:10.1016/j.enggeo.2018.03.023

Cited by 130 publications

(15 citation statements)

References 29 publications

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“…There are some differences in the names and formulas used to describe this ratio (Scheidegger 1973;Staron 2008;Lucas, Mangeney, and Ampuero 2014;Crosta et al, 2015). It has been referred to as the "Fahrboschung" (Evans and Clague 1994;Geertsema et al, 2006;Hungr 2006), "reach angle" (Scheidegger 1973;Corominas 1996) "effective friction coefficient" (Staron 2008), and "apparent friction coefficient" (Scheidegger 1973;Bouchut et al, 2015;Duan et al, 2020;Yang et al, 2018;Staron 2008;Magnarini et al, 2019;Wang, Sassa, and Fukuoka 2003;Yang et al, 2011;Legros 2002). By associating H/L with the mass of the sliding body, it has been found that a larger sliding mass body results in a lower H/L and stronger landslide movement ability.…”

Section: Introductionmentioning

confidence: 99%

“…Experiments can help to understand the movement mechanism involved in particle flows and have been widely used in research on the movement of geological hazards in recent years. They can also evaluate relevant physical parameters to improve slip distance predictions and verify numerical models (Duan et al, 2020;Iverson, George, and Logan 2016;Ng et al, 2017;H. Q. Yang et al, 2018;Y.-F. Wang et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

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The Motion and Range of Landslides According to Their Height

Duan

et al. 2021

Front. Earth Sci.

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The frequency of catastrophic geological disasters has been increasing significantly, causing tremendous losses of life and property. The study of landslide motion remains incomplete. The variables H/L (ratio of landslide height to length) are often used to describe landslide motion; however, they may also be affected by the height of the landslide itself. To better understand landslide dynamics, this paper aimed to 1) identify the process of landslide motion in relation to height; 2) understand the range of influence of sliding bodies according to height; and 3) construct a formula of landslide disaster range based on the travel distance of the slide center and changes in the center and shape of the sliding body. In this paper, medium-fine quartz sand was used in experiments to observe the movement patterns and sliding body barycenter variations occurring during landslides. We describe the changes that occur during landslides and their deposits’ morphological characteristics and barycenter variations with height. Based on these observations, a landslide model is derived. This paper proposes a new method of estimating the effects of landslides, which can help to mitigate the effects of disasters.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The Motion and Range of Landslides According to Their Height

Duan

et al. 2021

Front. Earth Sci.

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“…These procedures aim to increase the oil production to supply the necessary demand for industrial plants to crude oil [56][57][58]. Hydraulic fracturing is an essential process in petroleum industries that requires a large volume of water to create a fracturing fluid [59][60][61][62][63]. In this process, oil production has been increased by enlarging the previous and tight pores or creating new pore channels to simplify the oil mobilization through porous media [64][65][66][67][68].…”

Section: Introductionmentioning

confidence: 99%

Current Challenges and Advancements on the Management of Water Retreatment in Different Production Operations of Shale Reservoirs

Syah

Heidary

Rajabi

et al. 2021

Water

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Nowadays, water savings on industrial plants have become a significant concern for various plants and sections. It is vitally essential to propose applicable and efficient techniques to retreat produced water from onshore and offshore production units. This paper aimed to implement the PFF (Photo Fenton Flotation) method to optimize the water treatment procedure, as it is a two-stage separation technique. The measurements were recorded for the HF (hydraulic fracturing) and CEOR (chemically enhanced oil recovery) methods separately to compare the results appropriately. To assure the efficiency of this method, we first recorded the measurements for five sequential days. As a result, the total volume of 2372.5 MM m3/year of water can be saved in the HF process during the PFF treatment procedure, and only 20% of this required fresh water should be provided from other resources. On the other hand, the total volume of 7482.5 MM m3/year of water can be saved in CEOR processes during the PFF treatment procedure, and only 38% of this required fresh water should be provided from other resources. Therefore, the total water volume of 9855 MM m3 can be saved each year, indicating the efficiency of this method in supplying and saving the water volume during the production operations from oilfield units.

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“…For rock material evaluation, more than 100 block samples were collected from the face of PSRWT tunnel project and after transferring to the laboratory, several tests, e.g., density, BTS, point load strength, Schmidt hammer, p-wave velocity, and UCS were carried out based on ISRM [86]. According to several authors [54,55,[87][88][89][90][91][92], the most known There is a need to have a suitable database with the most effective parameters of TBM performance for prediction of TBM PR. The most effective parameters on TBM performance can be identified by reviewing the previous related studies.…”

Section: Case Study and Data Preparationmentioning

confidence: 99%

Supervised Machine Learning Techniques to the Prediction of Tunnel Boring Machine Penetration Rate

et al. 2019

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Predicting the penetration rate is a complex and challenging task due to the interaction between the tunnel boring machine (TBM) and the rock mass. Many studies highlight the use of empirical and theoretical techniques in predicting TBM performance. However, reliable performance prediction of TBM is of crucial importance to mining and civil projects as it can minimize the risks associated with capital costs. This study presents new applications of supervised machine learning techniques, i.e., k-nearest neighbor (KNN), chi-squared automatic interaction detection (CHAID), support vector machine (SVM), classification and regression trees (CART) and neural network (NN) in predicting the penetration rate (PR) of a TBM. To achieve this aim, an experimental database was set up, based on field observations and laboratory tests for a tunneling project in Malaysia. In the database, uniaxial compressive strength, Brazilian tensile strength, rock quality designation, weathering zone, thrust force, and revolution per minute were utilized as inputs to predict PR of TBM. Then, KNN, CHAID, SVM, CART, and NN predictive models were developed to select the best one. A simple ranking technique, as well as some performance indices, were calculated for each developed model. According to the obtained results, KNN received the highest-ranking value among all five predictive models and was selected as the best predictive model of this study. It can be concluded that KNN is able to provide high-performance capacity in predicting TBM PR. KNN model identified uniaxial compressive strength (0.2) as the most important and revolution per minutes (0.14) as the least important factor for predicting the TBM penetration rate.

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Model test on the entrainment phenomenon and energy conversion mechanism of flow-like landslides

Cited by 130 publications

References 29 publications

The Motion and Range of Landslides According to Their Height

The Motion and Range of Landslides According to Their Height

Current Challenges and Advancements on the Management of Water Retreatment in Different Production Operations of Shale Reservoirs

Supervised Machine Learning Techniques to the Prediction of Tunnel Boring Machine Penetration Rate

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