Field experimental verifications of 3D DDA and its applications to kinematic evolutions of rockfalls

Liu, Guoyang; Zhong, Zhirui; Ma, Ke; Bo, Wu; Zhao, Penghui; Li, Yexin; Zhang, Zhenping; Zhang, Peng

doi:10.1016/j.ijrmms.2024.105687

Cited by 3 publications

(1 citation statement)

References 47 publications

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“…Surface displacement monitoring data consist of the most intuitive and effective information for characterizing slope conditions (Dong et al, 2022;Liu et al, 2024). A large number of landslide monitoring data indicate that the evolution processes of various gradient landslides not only have unique characteristics but also have common laws (Zhao et al, 2021;Li et al, 2023); that is, the displacement-time curves of the monitored slopes typically exhibit a three-stage evolution pattern characterized by initial deformation, sustained deformation, and accelerated deformation (Benac et al, 2011;Niu et al, 2012;Pei et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

An improved radial basis function neural network for displacement prediction of a reservoir slope

Zhang,

Luo,

Liu

et al. 2024

Front. Earth Sci.

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Landslide prediction necessitates viewing the past, present, and future states of a slope as a constantly changing dialectical unity, with prediction laws derived from known past and present information. Through in-depth analysis of the structure and training methods of radial basis function (RBF) neural networks, an optimization method of RBF network diffusion velocity function based on the particle swarm optimization (PSO) algorithm was introduced in this study, aiming at the problem of limited coverage of spread value range determined by the empirical value or trial calculation method, so as to realize the large-scale and efficient search of RBF network diffusion function. To address the problem that the prediction accuracy of the data-driven model based on displacement increment sequences built by RBF intelligent algorithm is difficult to be guaranteed when the displacement increment mutation point exists, the PSO-RBF intelligent coupling model based on gray system theory pre-processing is constructed to improve the prediction accuracy of the model from the perspective of improving the prediction accuracy of displacement increment mutation points. Taking the data from ZG88 monitoring point of Shuping landslide as a case study, the slope displacement prediction analysis is carried out. The results demonstrate that the optimization method for RBF network diffusion velocity parameters based on PSO can efficiently and accurately identify the global optimal value within the range of 0–1,000. The computation process takes approximately 13 min, significantly enhancing the calculation efficiency. The RBF mixed model, incorporating gray system theory, leverages the valuable information extracted from prior calculations of the GM(1,1) model group. This integration enhances prediction accuracy compared with that achieved by the singular PSO-RBF method. The developed algorithms and research results may be expected to be applied in practical engineering.

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Section: Introductionmentioning

confidence: 99%

An improved radial basis function neural network for displacement prediction of a reservoir slope

Zhang,

Luo,

Liu

et al. 2024

Front. Earth Sci.

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Numerical Analysis of Internal Force Distribution in Combining Supporting Structures for Expansive Soil High Slope along Railway

Yan,

Zhang,

Shen

et al. 2024

Buildings

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To simulate the influence of rainfall on the internal forces of expansive soil slope retaining structures, an approximate calculation method for the humidity stress field of expansive soil is proposed in this study. Considering both rainy and non-rainy conditions, on a high expansive soil slope, a numerical model is constructed for a combining supporting structure, which is composed of upper and lower anti-sliding piles and anchor rods/cable frames. Furthermore, the distribution of internal forces in the retaining structure is studied, and design optimization is performed. The research findings reveal that the bending moment profile along the longitudinal axis of the lower anti-sliding pile demonstrates a distinctive “W” pattern, which is characterized by initial reduction, following augmentation, a subsequent reduction, and final amplification. In contrast, the upper anti-sliding pile experiences an ascending trend, followed by a descending trend, and then a subsequent ascending trend. Interestingly, the introduction of rainfall grants an escalation in both the shear force exerted along the entire length of the upper and lower anti-sliding piles and the bending moment encountered by the lower anti-sliding pile. The determination of the internal force distribution of the expansive soil slope retaining structures under different conditions, using the proposed calculation method, provides a further optimization in their design.

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Monitoring Surface Deformations in a Fossil Landslide Zone and Identifying Potential Failure Mechanisms: A Case Study of Gümüşhane State Hospital

Alemdag,

Yalvaç,

Bjelotomić Oršulić

et al. 2024

Sensors

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The escalating occurrence of landslides has drawn increasing attention from the scientific community, primarily driven by a combination of natural phenomena such as unpredictable seismic events, intensified precipitation, and rapid snowmelt attributable to climate fluctuations, compounded by inadequacies in engineering practices during site selection. Within the scope of this investigation, contemporary geodetic techniques using the GNSS were employed to monitor structural and surface deformations in and around a hospital edifice situated within an ancient fossil landslide region. Additionally, inclinometer measurements facilitated the determination of slip circle parameters. A subsequent analysis integrated these datasets to scrutinize both the hospital structure and its surrounding slopes. In addition to the finite element method, four different limit equilibrium methods (Bishop, GLE–Morgenstern–Price, Spencer, and Janbu) were used in the evaluation of stability. Since the safety number determined in all analyses was <1, it was determined that the slope containing the hospital building was unstable. The movement has occurred again due to the additional load created by the hospital building built on the currently stable slope, the effect of surface and groundwater, and the improperly designed road route. As a result of geodetic monitoring, it was determined that the sliding speed on the surface was in the N-E direction and was approximately 3 cm, and this situation almost coincided with inclinometer measurements.

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Field experimental verifications of 3D DDA and its applications to kinematic evolutions of rockfalls

Cited by 3 publications

References 47 publications

An improved radial basis function neural network for displacement prediction of a reservoir slope

An improved radial basis function neural network for displacement prediction of a reservoir slope

Numerical Analysis of Internal Force Distribution in Combining Supporting Structures for Expansive Soil High Slope along Railway

Monitoring Surface Deformations in a Fossil Landslide Zone and Identifying Potential Failure Mechanisms: A Case Study of Gümüşhane State Hospital

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