A5013263653- Geotechnical Engineering and Analysis
- Landslides and related hazards
- Dam Engineering and Safety
- Soil and Unsaturated Flow
- Geotechnical Engineering and Soil Stabilization
- Geotechnical Engineering and Underground Structures
- Geotechnical Engineering and Soil Mechanics
- Rock Mechanics and Modeling
- Cryospheric studies and observations
- Probabilistic and Robust Engineering Design
- Tree Root and Stability Studies
- Advanced Algorithms and Applications
- Advanced Computational Techniques and Applications
- Geomechanics and Mining Engineering
- Industrial Technology and Control Systems
- Drilling and Well Engineering
- Simulation and Modeling Applications
- Advanced Measurement and Detection Methods
- Customer Service Quality and Loyalty
- Innovation Diffusion and Forecasting
- Hydraulic Fracturing and Reservoir Analysis
- Consumer Retail Behavior Studies
- Geoscience and Mining Technology
- Particle physics theoretical and experimental studies
- Laser Material Processing Techniques
University of Newcastle Australia
2015-2024
Beihang University
2013-2024
Nanchang University
2016-2024
Wuxi People's Hospital
2024
Nanjing Medical University
2024
Colorado School of Mines
2008-2023
Dalhousie University
2009-2023
Guizhou Water Conservancy and Hydropower Survey and Design Institute
2023
ARC Centre of Excellence for Enabling Eco-Efficient Beneficiation of Minerals
2013-2022
Seventh People's Hospital of Dalian
2022
The paper investigates the probability of failure slopes using both traditional and more advanced probabilistic analysis tools. method, called random finite-element uses elastoplasticity in a model combined with field theory Monte-Carlo framework. first-order reliability computes index which is shortest distance (in units directional equivalent standard deviations) from mean-value point to limit state surface estimates index. Numerical results show that simplified analyses spatial...
Machine learning algorithms are an important measure with which to perform landslide susceptibility assessments, but most studies use GIS-based classification methods conduct zonation. This study presents a machine approach based on the C5.0 decision tree (DT) model and K-means cluster algorithm produce regional map. Yanchang County, typical landslide-prone area located in northwestern China, was taken as of interest introduce proposed application procedure. A inventory containing 82...
In some studies on landslide susceptibility mapping (LSM), boundary and spatial shape characteristics have been expressed in the form of points or circles inventory instead accurate polygon form. Different expressions boundaries shapes may lead to substantial differences distribution predicted indexes (LSIs); moreover, presence irregular introduces uncertainties into LSM. To address this issue by accurately drawing polygonal based LSM, uncertainty patterns LSM modelling under two different...
To perform landslide susceptibility prediction (LSP), it is important to select appropriate mapping unit and landslide-related conditioning factors. The efficient automatic multi-scale segmentation (MSS) method proposed by the authors promotes application of slope units. However, LSP modeling based on these units has not been performed. Moreover, heterogeneity factors in neglected, leading incomplete input variables modeling. In this study, extracted MSS are used construct modeling,...
Most literature related to landslide susceptibility prediction only considers a single type of landslide, such as colluvial rock fall or debris flow, rather than different types, which greatly affects performance. To construct efficient considering Huichang County in China is taken example. Firstly, 105 falls, 350 landslides and 11 environmental factors are identified. Then four machine learning models, namely logistic regression, multi-layer perception, support vector C5.0 decision tree...
Abstract The numerical simulation and slope stability prediction are the focus of disaster research. Recently, machine learning models commonly used in prediction. However, these have some problems, such as poor nonlinear performance, local optimum incomplete factors feature extraction. These issues can affect accuracy Therefore, a deep algorithm called Long short-term memory (LSTM) has been innovatively proposed to predict stability. Taking Ganzhou City China study area, landslide inventory...
Due to the similarity of conditioning factors, aggregation feature landslides and multi-temporal landslide inventory, spatial temporal effects need be considered in susceptibility prediction (LSP). The ignorance this issue will result some biases time-invariance susceptibility. Hence, a novel framework has been proposed update by simultaneously considering at regional scale. In framework, inventory Chongyi County divided into pre- fresh-landslide inventories. According LSP results predicted...
The accuracy of landslide susceptibility prediction (LSP) mainly depends on the precision spatial position. However, position error survey is inevitable, resulting in considerable uncertainties LSP modeling. To overcome this drawback, study explores influence positional errors uncertainties, and then innovatively proposes a semi-supervised machine learning model to reduce error. This paper collected 16 environmental factors 337 landslides with accurate positions taking Shangyou County China...