A coupled poroplastic damage model accounting for cracking effects on both hydraulic and mechanical properties of unsaturated media

Damage induced by microcracking affects not only the mechanical behaviour of geomaterials but also their hydraulic properties. Evaluating these impacts is important for many engineering applications, such as the safety assessment of radioactive waste disposal facilities. This paper presents a new constitutive model accounting simultaneously for the impact of damage on hydraulic and mechanical properties of unsaturated poroplastic geomaterials. The hydro‐mechanical coupling is formulated by means of the thermodynamic framework for partially saturated media, extended by taking into account isotropic damage and plasticity. State and complementary laws are governed by the so‐called plastic effective stress and equivalent pore pressure. Assuming a bimodal pore size distribution for cracked porous media, the hydraulic part (water retention curve and hydraulic conductivity) is modelled using phenomenological functions of damage variable. The participation of damage on both mechanical and hydraulic part enables this model to describe bilateral couplings between them. This coupled model is then validated against a number of experimental data obtained from Callovo‐Oxfordian argillite, which is the possible host rock for a radioactive waste disposal in France. Parametric studies are also carried out to check the consistency and to better demonstrate the bilateral couplings in the model. Copyright © 2015 John Wiley & Sons, Ltd.     

Landslide susceptibility analysis in the Hoa Binh province of Vietnam using statistical index and logistic regression

The purpose of this study is to evaluate and compare the results of applying the statistical index and the logistic regression methods for estimating landslide susceptibility in the Hoa Binh province of Vietnam. In order to do this, first, a landslide inventory map was constructed mainly based on investigated landslide locations from three projects conducted over the last 10 years. In addition, some recent landslide locations were identified from SPOT satellite images, fieldwork, and literature. Secondly, ten influencing factors for landslide occurrence were utilized. The slope gradient map, the slope curvature map, and the slope aspect map were derived from a digital elevation model (DEM) with resolution 20 × 20 m. The DEM was generated from topographic maps at a scale of 1:25,000. The lithology map and the distance to faults map were extracted from Geological and Mineral Resources maps. The soil type and the land use maps were extracted from National Pedology maps and National Land Use Status maps, respectively. Distance to rivers and distance to roads were computed based on river and road networks from topographic maps. In addition, a rainfall map was included in the models. Actual landslide locations were used to verify and to compare the results of landslide susceptibility maps. The accuracy of the results was evaluated by ROC analysis. The area under the curve (AUC) for the statistical index model was 0.946 and for the logistic regression model, 0.950, indicating an almost equal predicting capacity.     

An Improved Method Based on Shallow Ice Approximation to Calculate Ice Thickness along Flow-Line and Volume of Mountain Glaciers

To evaluate the water storage and project the future evolution of glaciers, the ice-thickness of glaciers is an essential input. However, direct measurements of ice thickness are labo-rious, not feasible everywhere, and necessarily restricted to a small number of glaciers. In this article, we develop a simple method to estimate the ice-thickness along flow-line of mountain glaciers. Different from the traditional method based on shallow ice approximation (SIA), which gives a relationship be-tween ice thickn...     

Determination of importance for comprehensive topographic factors on landslide hazard mapping using artificial neural network

A landslide is one of the natural disasters that occur in Malaysia. In addition to the geological factor and the rain as triggering factor, topographic factors such as elevation, slope angle, slope aspect, and curvature are considered as the main causes of landslides. The study in this paper was conducted in three stages. The first stage involved the extraction of extra topographic factors. Previous landslide studies had identified only four of the topographic factors. However, eight new additional factors have also been identified in this study. They are general curvature, longitudinal curvature, tangential curvature, cross-section curvature, surface area, diagonal line length, surface roughness, and rugosity. At this stage, 13 factors were extracted from the digital elevation model. The second stage involved specifying the importance of each factor. The multilayer perceptron network and backpropagation algorithm were used to specify the weight of each factor. Results were verified using the receiver operating characteristics based on the area under the curve method in the third stage. The results indicated 76.07 % accuracy in predicting of landslides, with slope angle as the most important factor while the tangential curvature has the least importance.     

Biomass estimation of Sonneratia caseolaris (l.) Engler at a coastal area of Hai Phong city (Vietnam) using ALOS-2 PALSAR imagery and GIS-based multi-layer perceptron neural networks

This study tested the use of machine learning techniques for the estimation of above-ground biomass (AGB) of Sonneratia caseolaris in a coastal area of Hai Phong city, Vietnam. We employed a GIS database and multi-layer perceptron neural networks (MLPNN) to build and verify an AGB model, drawing upon data from a survey of 1508 mangrove trees in 18 sampling plots and ALOS-2 PALSAR imagery. We assessed the model’s performance using root-mean-square error, mean absolute error, coefficient of determination (R²), and leave-one-out cross-validation. We also compared the model’s usability with four machine learning techniques: support vector regression, radial basis function neural networks, Gaussian process, and random forest. The MLPNN model performed well and outperformed the machine learning techniques. The MLPNN model-estimated AGB ranged between 2.78 and 298.95 Mg ha^?1 (average = 55.8 Mg ha^?1); below-ground biomass ranged between 4.06 and 436.47 Mg ha^?1 (average = 81.47 Mg ha^?1), and total carbon stock ranged between 3.22 and 345.65 Mg C ha^?1 (average = 64.52 Mg C ha^?1). We conclude that ALOS-2 PALSAR data can be accurately used with MLPNN models for estimating mangrove forest biomass in tropical areas.     

Landslide Hazard Assessment Using Random SubSpace Fuzzy Rules Based Classifier Ensemble and Probability Analysis of Rainfall Data: A Case Study at Mu Cang Chai District,Yen Bai Province (Viet Nam)

Landslide hazard assessment at the Mu Cang Chai district; Yen Bai province (Viet Nam) has been done using Random SubSpace fuzzy rules based Classifier Ensemble (RSSCE) method and probability analysis of rainfall data. RSSCE which is a novel classifier ensemble method has been applied to predict spatially landslide occurrences in the area. Prediction of temporally landslide occurrences in the present study has been done using rainfall data for the period 2008–2013. A total of fifteen landslide influencing factors namely slope, aspect, curvature, plan curvature, profile curvature, elevation, land use, lithology, rainfall, distance to faults, fault density, distance to roads, road density, distance to rivers, and river density have been utilized. The result of the analysis shows that RSSCE and probability analysis of rainfall data are promising methods for landslide hazard assessment. Finally, landslide hazard map has been generated by integrating spatial prediction and temporal probability analysis of landslides for the land use planning and landslide hazard management.     

Landslide susceptibility assesssment in the Uttarakhand area (India) using GIS: a comparison study of prediction capability of naïve bayes,multilayer perceptron neural networks,and functional trees methods

The objective of this study is to make a comparison of the prediction performance of three techniques, Functional Trees (FT), Multilayer Perceptron Neural Networks (MLP Neural Nets), and Naïve Bayes (NB) for landslide susceptibility assessment at the Uttarakhand Area (India). Firstly, a landslide inventory map with 430 landslide locations in the study area was constructed from various sources. Landslide locations were then randomly split into two parts (i) 70 % landslide locations being used for training models (ii) 30 % landslide locations being employed for validation process. Secondly, a total of eleven landslide conditioning factors including slope angle, slope aspect, elevation, curvature, lithology, soil, land cover, distance to roads, distance to lineaments, distance to rivers, and rainfall were used in the analysis to elucidate the spatial relationship between these factors and landslide occurrences. Feature selection of Linear Support Vector Machine (LSVM) algorithm was employed to assess the prediction capability of these conditioning factors on landslide models. Subsequently, the NB, MLP Neural Nets, and FT models were constructed using training dataset. Finally, success rate and predictive rate curves were employed to validate and compare the predictive capability of three used models. Overall, all the three models performed very well for landslide susceptibility assessment. Out of these models, the MLP Neural Nets and the FT models had almost the same predictive capability whereas the MLP Neural Nets (AUC = 0.850) was slightly better than the FT model (AUC = 0.849). The NB model (AUC = 0.838) had the lowest predictive capability compared to other models. Landslide susceptibility maps were final developed using these three models. These maps would be helpful to planners and engineers for the development activities and land-use planning.     

Spatial prediction models for shallow landslide hazards: a comparative assessment of the efficacy of support vector machines,artificial neural networks,kernel logistic regression,and logistic model tree

Preparation of landslide susceptibility maps is considered as the first important step in landslide risk assessments, but these maps are accepted as an end product that can be used for land use planning. The main objective of this study is to explore some new state-of-the-art sophisticated machine learning techniques and introduce a framework for training and validation of shallow landslide susceptibility models by using the latest statistical methods. The Son La hydropower basin (Vietnam) was selected as a case study. First, a landslide inventory map was constructed using the historical landslide locations from two national projects in Vietnam. A total of 12 landslide conditioning factors were then constructed from various data sources. Landslide locations were randomly split into a ratio of 70:30 for training and validating the models. To choose the best subset of conditioning factors, predictive ability of the factors were assessed using the Information Gain Ratio with 10-fold cross-validation technique. Factors with null predictive ability were removed to optimize the models. Subsequently, five landslide models were built using support vector machines (SVM), multi-layer perceptron neural networks (MLP Neural Nets), radial basis function neural networks (RBF Neural Nets), kernel logistic regression (KLR), and logistic model trees (LMT). The resulting models were validated and compared using the receive operating characteristic (ROC), Kappa index, and several statistical evaluation measures. Additionally, Friedman and Wilcoxon signed-rank tests were applied to confirm significant statistical differences among the five machine learning models employed in this study. Overall, the MLP Neural Nets model has the highest prediction capability (90.2 %), followed by the SVM model (88.7 %) and the KLR model (87.9 %), the RBF Neural Nets model (87.1 %), and the LMT model (86.1 %). Results revealed that both the KLR and the LMT models showed promising methods for shallow landslide susceptibility mapping. The result from this study demonstrates the benefit of selecting the optimal machine learning techniques with proper conditioning selection method in shallow landslide susceptibility mapping.     

Mechanism of two rapid and long-runout landslides in the 16 April 2016 Kumamoto earthquake using a ring-shear apparatus and computer simulation (LS-RAPID)

Around hundred landslides were triggered by the Kumamoto earthquakes in April 2016, causing fatalities and serious damage to properties in Minamiaso village, Kumamoto Prefecture, Japan. The landslides included many rapid and long-runout landslides which were responsible for much of the damage. To understand the mechanism of these earthquake-triggered landslides, we carried out field investigations with an unmanned aerial vehicle to obtain DSM and took samples from two major landslides (Takanodai landslide and Aso-ohashi landslide) to measure parameters of the initiation and the motion of landslides. A series of ring-shear tests and computer simulations were conducted using a measured Kumamoto earthquake acceleration record from KNet station KMM005, 10 km west of Aso-ohashi landslide. The research results supported our assumed mechanism of sliding-surface liquefaction for the rapid and long-runout motion of these landslides.