Exploring the Deformation and Failure Characteristics of Interbedded Anti-inclined Rock Slopes: Insights from Physical Modelling Tests

Zhang, Bocheng; Tang, Huiming; Sumi, Siddiqua; Ding, Bingdong; Zhang, Long; Ning, Yibing

doi:10.1007/s00603-023-03625-4

Cited by 3 publications

(2 citation statements)

References 55 publications

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“…Landslides are a complex and dynamic geological phenomenon that can occur in various geological settings, resulting in the loss of human lives, damage to infrastructure, and the disruption of the natural environment [1][2][3][4][5]. More than 4200 potential landslides within the Three Gorges Reservoir Area (TGRA), located 600 km along the Yangtze River behind the Three Gorges Dam, could occur due to seasonal rainfall and periodic fluctuations in reservoir water levels (RWLs).…”

Section: Introductionmentioning

confidence: 99%

A Novel Approach to Enhance Landslide Displacement Prediction with Finer Monitoring Data: A Case Study of the Baijiabao Landslide

Xia,

Tang,

Glade

2024

Remote Sensing

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Rainfall and reservoir water level are commonly regarded as the two major influencing factors for reservoir landslides and are employed for landslide displacement prediction, yet their daily data are readily available with current monitoring technology, which makes a more refined analysis possible. However, until now, few efforts have been made to predict landslide displacements using daily data, which is likely to substantially improve accuracy and is crucial for landslide early warning. A novel feature enhancement approach for extracting critical characteristics from daily rainfall and reservoir water level data for use in landslide displacement prediction is proposed in this study. Six models, including gated recurrent units (GRUs), long short-term memory (LSTM), and support vector regression (SVR) with an unenhanced dataset and GRU-E, LSTM-E, and SVR-E with an enhanced dataset, were employed for displacement predictions at four GPS monitoring stations on the Baijiabao landslide, a typical step-like reservoir landslide. The results show that the accuracy values of all the enhanced models were significantly improved, and the GRU-E model achieved the most significant improvement, with the RMSE decreasing by 24.39% and R2 increasing by 0.2693, followed by the LSTM-E and SVR-E models. Further, the GRU-E model consistently outperformed the other models, achieving the highest R2 of 0.6265 and the lowest RMSE of 16.5208 mm, significantly superior than the others. This study indicates the feasibility of improving the accuracy of landslide monthly displacement predictions with finer monitoring data and provides valuable insights for future research.

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Section: Introductionmentioning

confidence: 99%

A Novel Approach to Enhance Landslide Displacement Prediction with Finer Monitoring Data: A Case Study of the Baijiabao Landslide

Xia,

Tang,

Glade

2024

Remote Sensing

Self Cite

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“…By combining indoor tests and numerical simulations, the deformation and failure mechanism of an interbedded structure slope has been explored. On this basis, the failure evolution process of the slope is divided into stages with different characteristics [12][13][14][15]. Safety evaluation is also the research focus in the field of slope engineering [16].…”

Section: Introductionmentioning

confidence: 99%

Investigation into the Characteristics of Expansion and Compression Deformation of Interbedded Weak Expansive Rocks in Water Immersion

Wang,

Li,

Qin

et al. 2024

Buildings

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In order to investigate the deformation characteristics of interbedded weak expansive rocks in water immersion, the sandstone–mudstone interbedded structures were taken as the object of this study. A total of 27 sets of indoor immersion tests were designed with three influencing factors, namely, the layer thickness ratios of sandstone and mudstone (1:1, 2:1, 3:1), the occurrence of the rock layers (flat, oblique, and vertical), and the overburden loadings (0 kPa, 12.5 kPa, and 25 kPa). Tests were conducted to obtain the deformation time series data of the samples during the immersion loading process. Based on this, the influence pattern of each influencing factor on the sample deformation was analyzed individually. The results show that with the increase in overburden loading and rock inclination angle, the sample develops from expansion deformation to compression deformation. Changes in the layer thickness ratio will not change the deformation trend of the sample, and the decrease in the relative mudstone content will only reduce the absolute value of the sample’s expansion and compression deformation. The deformation stability rate of the sample under load is 5~7 times that under no load. The increase in layer thickness ratio and rock inclination angle will lead to different degrees of attenuation of sample expansion force in the range of 8.91~38.68% and 51.00~58.83%, respectively. The research results of this paper can provide a meaningful reference for the design and maintenance of a high-speed railway subgrade in a weak expansive rock area with an interbedded structure.

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Exploring the toppling deformation mechanisms and failure modes of anti-dip layered rocky slopes: insights from physical model experiments

Zhao,

Yang,

Zhang

et al. 2024

Landslides

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Exploring the Deformation and Failure Characteristics of Interbedded Anti-inclined Rock Slopes: Insights from Physical Modelling Tests

Cited by 3 publications

References 55 publications

A Novel Approach to Enhance Landslide Displacement Prediction with Finer Monitoring Data: A Case Study of the Baijiabao Landslide

A Novel Approach to Enhance Landslide Displacement Prediction with Finer Monitoring Data: A Case Study of the Baijiabao Landslide

Investigation into the Characteristics of Expansion and Compression Deformation of Interbedded Weak Expansive Rocks in Water Immersion

Exploring the toppling deformation mechanisms and failure modes of anti-dip layered rocky slopes: insights from physical model experiments

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