Numerical analysis of debris flows along the Sichuan-Tibet railway based on an improved 3D sphere DDA model and UAV-based photogrammetry

Huang, Gang‐Hai; Lv, Guoshun; Zhang, Sheng; Huang, Dongliang; Zhao, Lianheng; Ni, Xueqian; Liu, Hongwei; Lv, Jie; Liu, Changdong

doi:10.1016/j.enggeo.2022.106722

Cited by 13 publications

(6 citation statements)

References 38 publications

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“…Ganghai Huang et al numerically simulated three potential mudslide scenarios in the Dotang area based on photogrammetry from a UAV. Recommendations for engineering disaster prevention were then made based on the simulation results [8].…”

Section: Mudslidesmentioning

confidence: 99%

Application of UAV remote sensing in natural disaster monitoring and early warning: an example of flood and mudslide and earthquake disasters

Chen

2024

HSET

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As the frequency and complexity of natural disasters increase, effective monitoring and early warning have become important for the protection of life and property. This paper discusses the role of unmanned aerial vehicle (UAV) remote sensing technology in natural disaster monitoring and early warning. The research in this paper finds that the advantages of UAVs, which carry multiple sensors and are convenient and dexterous, are conducive to natural disaster monitoring. The article then provides examples of UAV applications in different natural disaster scenarios, including monitoring, early warning, post-disaster assessment and ecological restoration in floods, mudslides and earthquakes. In flood monitoring, UAVs equipped with various sensors, such as multi-spectral sensors and infrared thermal imagers, can quickly scan flood-prone areas and transmit data in real time for timely warning and rescue. In mudslide monitoring, drones can collect data such as surface temperature, soil moisture and vegetation health to help identify signs of potential danger. In earthquake monitoring, drones can provide high-resolution images and video to assess earthquake damage and the condition of infrastructure. The future technological innovation and industry development of UAV remote sensing will continue to progress in terms of sensor technology innovation, application of machine learning and artificial intelligence, range extension, and convergence of communication technologies. The significance of this paper is to highlight the importance of UAV remote sensing in natural disaster management and to provide a vision for future research and applications. Drones will continue to play a key role in facilitating the efficiency and accuracy of natural disaster monitoring and early warning to better address potential threats.

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Section: Mudslidesmentioning

confidence: 99%

Application of UAV remote sensing in natural disaster monitoring and early warning: an example of flood and mudslide and earthquake disasters

Chen

2024

HSET

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“…Figura 3. Modelo Digital del Terreno (MDT), a la izquierda y Flujo Acumulado a la derecha Todos estos productos topográficos son de mucho interés en estudios de amenazas por inundaciones y movientes en masa, lo cual muestra la utilidad práctica de los UAV en actividades relacionadas con la gestión de riesgos y el mejoramiento de la calidad de vida de la sociedad, tal como reportan Dai et al (2022) y Huang et al (2022. Por ejemplo, los máximos valores de flujo acumulado indican la presencia de cauces y líneas de drenaje, tal como se muestra en los colores azules en la parte derecha de la figura 2.…”

Section: Modelo Digital Del Terreno (Mdt)unclassified

Uso de un vehículo aéreo no tripulado como alternativa para generar información topográfica

et al. 2023

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Los vehículos aéreos no tripulados (UAV) se están convirtiendo en una herramienta tecnología muy versátil en diversas áreas de aplicación para actividades del desarrollo. La topografía, como área fundamental de la ingeniería, aporta información relacionada con la ubicación tridimensional de puntos en la superficie terrestre. Este trabajo tiene por objetivo generar información topográfica, usando un UAV como alternativa tecnológica a las técnicas tradicionales. La metodología consistió en la planificación y ejecución de dos vuelos fotogramétricos con el UAV EBEB SQ, instrumentado con la cámara multiespectral Sequoia. Para ello, se colocaron cinco puntos de control en el terreno, georreferenciados con estación total, usados como puntos de control en el procesamiento de las imágenes del vuelo. Las fotografías capturadas en el vuelo, fueron procesadas mediante fotogrametría con el software PIX4Dmapper, en un computador de escritorio, con procesador Intel(R) Core (TM) i9-9900K CPU 3.60GHz y 32.0 GB de RAM. Los resultados del vuelo fotogramétrico consistieron en un total de 633 fotografías RGB en un tiempo de vuelo de 36:27 minutos, para un área de cobertura de 57.7 ha. El reporte de calidad del procesamiento indicó una precisión de 2 mm en la georreferenciación de las fotografías con los puntos de control. El procesamiento fotogramétrico se ejecutó en un tiempo de 48 minutos para generar Ortofotos, Modelo Digital del Terreno (MDT) y nube de puntos tridimensional. Los productos generados alcanzaron una resolución espacial de 5 cm/pixel, con precisiones milimétricas que permitieron gestionar información topográfica secundaria como la pendiente. La nube de puntos permitió clasificar la cobertura en vegetación y suelo, para estimar la altura del dosel del cultivo de algodón con una precisión del 91 %. Como ventajas del UAV sobre las técnicas tradicionales para levantamientos topográficos se puede destacar la variedad y precisión de los productos geoespaciales y la optimización de los tiempos.

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“…In general, this kind of particle-based model provides a 3D description of the debris-flow dynamic process through discrete particles and approximately solves the Navier-Stokes (N-S) equations in discrete form (Hungr & McDougall, 2009;McDougall & Hungr, 2005), so that a large amount of debris-flow dynamic data can be recorded. Considering the complex rheology of debris-flow mass, here we use our previous three-dimensional SPH model based on Herschel-Bulkley-Papanastasiou (HBP) rheology (Han et al, 2019(Han et al, , 2021a, the socalled 3D-HBP-SPH model, the positive effect of which has been substantiated by the following studies (Huang et al, 2022;Morikawa & Asai, 2022;Yu et al, 2020). The details of the model could be referred to Han et al (2019) and Han et al (2021a), while the basics of this model is introduced in detail in the supporting information Text S1 along with this paper.…”

Section: Introductionmentioning

confidence: 99%

Exploring the Temporal-Varying and Depth-Nonlinear Velocity Profile of Debris Flows Based on A Stratification Statistical Algorithm for 3D-HBP-SPH Particles

Han

Xie²,

Zeng

et al. 2023

Preprint

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Estimation of velocity profile through mud depth is a long-standing and essential problem in debris-flow dynamics. Until now, various velocity profiles have been proposed based on the regression of experimental measurements, but these are often limited by the observation conditions, such as the number of the configured sensors. Therefore, the resulting linear velocity profiles exhibit limitations in reproducing the nonlinear behavior and its temporal variation during the debris-flow process. In this study, we present a novel approach to explore debris-flow velocity profile in detail upon our previous 3D-HBP-SPH numerical model, i.e., the three-dimensional Smoothed Particle Hydrodynamic model incorporating with the Herschel-Bulkley-Papanastasiou rheology. Specifically, we propose a stratification statistical algorithm for interpreting the details of SPH particles, which enables the recording of temporal velocities of debris flow at different mud depths. To regress the velocity profile, we introduce a logarithmic-based nonlinear function with two empirical parameters, that controlling the shape of velocity profile and concerning its temporal evolution. We verify the proposed velocity profile and explore its sensitivity using 34 sets of velocity data from three individual flume experiments in previous literatures. Our results demonstrate that the proposed temporal-varying and depth-nonlinear velocity profile outperforms the previous ones.

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Numerical analysis of debris flows along the Sichuan-Tibet railway based on an improved 3D sphere DDA model and UAV-based photogrammetry

Cited by 13 publications

References 38 publications

Application of UAV remote sensing in natural disaster monitoring and early warning: an example of flood and mudslide and earthquake disasters

Application of UAV remote sensing in natural disaster monitoring and early warning: an example of flood and mudslide and earthquake disasters

Uso de un vehículo aéreo no tripulado como alternativa para generar información topográfica

Exploring the Temporal-Varying and Depth-Nonlinear Velocity Profile of Debris Flows Based on A Stratification Statistical Algorithm for 3D-HBP-SPH Particles

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