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  • Open Access

    ARTICLE

    Deep Learning Framework for Landslide Severity Prediction and Susceptibility Mapping

    G. Bhargavi*, J. Arunnehru

    Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 1257-1272, 2023, DOI:10.32604/iasc.2023.034335 - 05 January 2023

    Abstract Landslides are a natural hazard that is unpredictable, but we can prevent them. The Landslide Susceptibility Index reduces the uncertainty of living with landslides significantly. Planning and managing landslide-prone areas is critical. Using the most optimistic deep neural network techniques, the proposed work classifies and analyses the severity of the landslide. The selected experimental study area is Kerala’s Idukki district. A total of 3363 points were considered for this experiment using historic landslide points, field surveys, and literature searches. The primary triggering factors slope degree, slope aspect, elevation (altitude), normalized difference vegetation index (NDVI), and… More >

  • Open Access

    ARTICLE

    Analysis of the Mechanisms Underpinning Rainstorm-Induced Landslides

    Shaojie Feng*, Leipeng Liu, Chen Gao, Hang Hu

    FDMP-Fluid Dynamics & Materials Processing, Vol.19, No.5, pp. 1189-1201, 2023, DOI:10.32604/fdmp.2023.023637 - 30 November 2022

    Abstract The present study considers the damage mechanisms and the rainfall infiltration process responsible for landslide phenomena which originate from accumulation slopes. Accordingly, a physical test model is developed for different slopes and different rainfall conditions. Moreover, a three-dimensional laser scanner and a camera are used to monitor the slope erosion and the landslide dynamic evolution. Using this approach, the time variation curves of volumetric water content, pore water pressure, soil pressure, slope deformation, and damage are determined. The results show that under similar conditions, similar trends of the pore water pressure are achieved for different More > Graphic Abstract

    Analysis of the Mechanisms Underpinning Rainstorm-Induced Landslides

  • Open Access

    ARTICLE

    Application of Smoothed Particle Hydrodynamics (SPH) for the Simulation of Flow-Like Landslides on 3D Terrains

    Binghui Cui1,*, Liaojun Zhang2

    CMES-Computer Modeling in Engineering & Sciences, Vol.135, No.1, pp. 357-376, 2023, DOI:10.32604/cmes.2022.022309 - 29 September 2022

    Abstract Flow-type landslide is one type of landslide that generally exhibits characteristics of high flow velocities, long jump distances, and poor predictability. Simulation of its propagation process can provide solutions for risk assessment and mitigation design. The smoothed particle hydrodynamics (SPH) method has been successfully applied to the simulation of two-dimensional (2D) and three-dimensional (3D) flow-like landslides. However, the influence of boundary resistance on the whole process of landslide failure is rarely discussed. In this study, a boundary condition considering friction is proposed and integrated into the SPH method, and its accuracy is verified. Moreover, the… More > Graphic Abstract

    Application of Smoothed Particle Hydrodynamics (SPH) for the Simulation of Flow-Like Landslides on 3D Terrains

  • Open Access

    ARTICLE

    Transforming Hand Drawn Wireframes into Front-End Code with Deep Learning

    Saman Riaz1, Ali Arshad2, Shahab S. Band3,*, Amir Mosavi4

    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 4303-4321, 2022, DOI:10.32604/cmc.2022.024819 - 21 April 2022

    Abstract The way towards generating a website front end involves a designer settling on an idea for what kind of layout they want the website to have, then proceeding to plan and implement each aspect one by one until they have converted what they initially laid out into its Html front end form, this process can take a considerable time, especially considering the first draft of the design is traditionally never the final one. This process can take up a large amount of resource real estate, and as we have laid out in this paper, by… More >

  • Open Access

    ARTICLE

    Twisted Pair Cable Fault Diagnosis via Random Forest Machine Learning

    N. B. Ghazali1, F. C. Seman1,*, K. Isa1, K. N. Ramli1, Z. Z. Abidin1, S. M. Mustam1, M. A. Haek2, A. N. Z. Abidin2, A. Asrokin2

    CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 5427-5440, 2022, DOI:10.32604/cmc.2022.023211 - 14 January 2022

    Abstract Applying the fault diagnosis techniques to twisted pair copper cable is beneficial to improve the stability and reliability of internet access in Digital Subscriber Line (DSL) Access Network System. The network performance depends on the occurrence of cable fault along the copper cable. Currently, most of the telecommunication providers monitor the network performance degradation hence troubleshoot the present of the fault by using commercial test gear on-site, which may be resolved using data analytics and machine learning algorithm. This paper presents a fault diagnosis method for twisted pair cable fault detection based on knowledge-based and… More >

  • Open Access

    ARTICLE

    Application of the Navier-Stokes Equations to the Analysis of the Landslide Sediments Permeability and Related Seepage Effects

    Meng Song1,*, Yuncai Liu2, Zhen Wang1

    FDMP-Fluid Dynamics & Materials Processing, Vol.18, No.2, pp. 313-327, 2022, DOI:10.32604/fdmp.2022.017737 - 16 December 2021

    Abstract The purpose of the study is to implement a new model based on the Navier-Stokes equations for the characterization of landslide sediments interacting with a moving fluid. The model is implemented by combining Hypermesh, the LS-DYNA software and MATLAB. The results show that the main factors affecting the permeability of landslide sediments are the genetic mechanism, the structure and composition of materials, material lithology, and stress. The characteristics and mechanism of permeability changes are determined by adjusting the water levels of fluids. It is found that the permeability of landslide sediments increases at the front More >

  • Open Access

    ARTICLE

    Predicting Concrete Compressive Strength Using Deep Convolutional Neural Network Based on Image Characteristics

    Sanghyo Lee1, Yonghan Ahn2, Ha Young Kim3, *

    CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 1-17, 2020, DOI:10.32604/cmc.2020.011104 - 23 July 2020

    Abstract In this study, we examined the efficacy of a deep convolutional neural network (DCNN) in recognizing concrete surface images and predicting the compressive strength of concrete. A digital single-lens reflex (DSLR) camera and microscope were simultaneously used to obtain concrete surface images used as the input data for the DCNN. Thereafter, training, validation, and testing of the DCNNs were performed based on the DSLR camera and microscope image data. Results of the analysis indicated that the DCNN employing DSLR image data achieved a relatively higher accuracy. The accuracy of the DSLR-derived image data was attributed… More >

  • Open Access

    ARTICLE

    On Harmonic and Ev-Degree Molecular Topological Properties of DOX, RTOX and DSL Networks

    Murat Cancan1, *

    CMC-Computers, Materials & Continua, Vol.59, No.3, pp. 777-786, 2019, DOI:10.32604/cmc.2019.06596

    Abstract Topological indices enable to gather information for the underlying topology of chemical structures and networks. Novel harmonic indices have been defined recently. All degree based topological indices are defined by using the classical degree concept. Recently two novel degree concept have been defined in graph theory: ve-degree and ev-degree. Ve-degree Zagreb indices have been defined by using ve-degree concept. The prediction power of the ve-degree Zagreb indices is stronger than the classical Zagreb indices. Dominating oxide, silicate and oxygen networks are important network models in view of chemistry, physics and information science. Physical and mathematical More >

  • Open Access

    ARTICLE

    Modelling of Landslides: An SPH Approach

    M. Pastor1, T. Blanc1, V. Drempetic1 , P. Dutto1 , M. Martín Stickle1, A.Yagüe1

    CMES-Computer Modeling in Engineering & Sciences, Vol.109-110, No.2, pp. 183-220, 2015, DOI:10.3970/cmes.2015.109.183

    Abstract This paper presents a model (mathematical, rheological and numerical) for triggering and propagation of landslides presenting coupling between the solid skeleton and the pore fluid. The model consists of two sub models, a depth integrated model incorporating the propagation equations, and a 1D model describing pore pressure evolution. The depth integrated sub model is discretized using a set of SPH nodes, each one having an associated finite difference mesh for discretizing the pore pressure evolution. The model we propose differs from other depth integrated models with coupled pore pressures proposed in the past in the More >

  • Open Access

    ARTICLE

    Gauss Process Based Approach for Application on Landslide Displacement Analysis and Prediction

    Zaobao Liu1,2, Weiya Xu1, Jianfu Shao2

    CMES-Computer Modeling in Engineering & Sciences, Vol.84, No.2, pp. 99-122, 2012, DOI:10.3970/cmes.2012.084.099

    Abstract In this paper, the Gauss process is proposed for application on landslide displacement analysis and prediction with dynamic crossing validation. The prediction problem using noisy observations is first introduced. Then the Gauss process method is proposed for modeling non-stationary series of landslide displacements based on its ability to model noisy data. The monitoring displacement series of the New Wolong Temple Landslide is comparatively studied with other methods as an instance to implement the strategy of the Gauss process for predicting landslide displacement. The dynamic crossing validation method is adopted to manage the displacement series so… More >

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