Three-dimensional discontinuous deformation analysis (3-D DDA) using a new contact resolution algorithm

This paper presents a new contact calculating algorithm for contacts between two polyhedra with planar boundaries in the three-dimensional discontinuous deformation analysis (3-D DDA). In this algorithm, all six type contacts in 3-D (vertex-to-face, vertex-to-edge, vertex-to-vertex, face-to-face, edge-to-edge, and edge-to-face) are simply transformed into the form of point-to-face contacts. The presented algorithm is a simple and efficient method and it can be easily coded into a computer program. In this paper, formulations of normal contact, shear contact and frictional force submatrices based on the new method are derived and the algorithm has been programmed in VC⁺⁺. Examples are provided to demonstrate the new contact rule between two blocks.     

Landslide susceptibility modeling based on ANFIS with teaching-learning-based optimization and Satin bowerbird optimizer

As threats of landslide hazards have become gradually more severe in recent decades,studies on landslide prevention and mitigation have attracted widespread attention in relevant domains.A hot research topic has been the ability to predict landslide susceptibility,which can be used to design schemes of land exploitation and urban development in mountainous areas.In this study,the teaching-learning-based optimization(TLBO)and satin bowerbird optimizer(SBO)algorithms were applied to optimize the adaptive neuro-fuzzy inference system(ANFIS)model for landslide susceptibility mapping.In the study area,152 landslides were identified and randomly divided into two groups as training(70%)and validation(30%)dataset.Additionally,a total of fifteen landslide influencing factors were selected.The relative importance and weights of various influencing factors were determined using the step-wise weight assessment ratio analysis(SWARA)method.Finally,the comprehensive performance of the two models was validated and compared using various indexes,such as the root mean square error(RMSE),processing time,convergence,and area under receiver operating characteristic curves(AUROC).The results demonstrated that the AUROC values of the ANFIS,ANFIS-TLBO and ANFIS-SBO models with the training data were 0.808,0.785 and 0.755,respectively.In terms of the validation dataset,the ANFISSBO model exhibited a higher AUROC value of 0.781,while the AUROC value of the ANFIS-TLBO and ANFIS models were 0.749 and 0.681,respectively.Moreover,the ANFIS-SBO model showed lower RMSE values for the validation dataset,indicating that the SBO algorithm had a better optimization capability.Meanwhile,the processing time and convergence of the ANFIS-SBO model were far superior to those of the ANFIS-TLBO model.Therefore,both the ensemble models proposed in this paper can generate adequate results,and the ANFIS-SBO model is recommended as the more suitable model for landslide susceptibility assessment in the study area considered due to its excellent accuracy and efficiency.     

New chelating resin for preconcentration and determination of molybdenum by inductive couple plasma atomic emission spectroscopy

A chelating resin is prepared by condensation polymerization of aniline with formaldehyde and characterized by Fourier transform infrared spectrometer, elemental analysis and thermogravimetric analysis and studied for the preconcentration and determination of trace Molybdate ion from environmental water sample using inductive couple plasma atomic emission spectroscopy. The optimum pH value for sorption of the metal ion was 5. The sorption capacity of functionalized resin is 3.1 mg/g. The chelating sorbent can be reused for 20 cycles of sorption-desorption without any significant change in sorption capacity. The best desorption of the metal ions from resin was obtained by 0.5 mol/L nitric acid as eluting agent. The profile of molybdenum uptake on this sorbent reflects good accessibility of the chelating sites in the aniline-formaldehyde. Scatchard analysis revealed that the homogeneous binding sites were formed in the polymers. The equilibrium adsorption data of Molybdate ion modified resin were analyzed by five isotherm models such as Langmuir, Freundlich and Temkin. Langmuir isotherm parameters obtained from the four Langmuir linear equations by using linear method. Based on the Langmuir isotherm analysis, the monolayer adsorption capacity was determined to be 4.03 mg/g at 20 °C. The method was applied for molybdenum ions determination from river water sample.     

Implementation of the finite element method in the three‐dimensional discontinuous deformation analysis (3D‐DDA)

A modified three‐dimensional discontinuous deformation analysis (3D‐DDA) method is derived using four‐noded tetrahedral elements to improve the accuracy of current 3D‐DDA algorithm in practical applications. The analysis program for the modified 3D‐DDA method is developed in a C++ environment and its accuracy is illustrated through comparisons with several analytical solutions that are available for selected problems. The predicted solutions for these problems using the modified 3D‐DDA approach all show satisfactory agreement with the corresponding analytical results. Results presented in this paper demonstrate that the modified 3D‐DDA method with discontinuous modeling capabilities offers a useful computational tool to determine stresses and deformations in practical problems involving fissured elastic media with reasonable accuracy. Copyright © 2008 John Wiley & Sons, Ltd.     

Discontinuous deformation analysis with second‐order finite element meshed block

The discontinuous deformation analysis (DDA) with second‐order displacement functions was derived based on six‐node triangular mesh in order to satisfy the requirement for the accurate calculations in practical applications. The matrices of equilibrium equations for the second‐order DDA were given in detail for program coding. By close comparison with widely used finite element method and closed form solutions, the advantages of the modified DDA were illustrated. The program coding was carried out in C++ environment and the new code applied to three examples with known analytical solutions. A very good agreement was achieved between the analytical and numerical results produced by the modified DDA code. Copyright © 2006 John Wiley & Sons, Ltd.     

Deep learning neural networks for spatially explicit prediction of flash flood probability

Flood probability maps are essential for a range of applications, including land use planning and developing mitigation strategies and early warning systems. Th...     

Multifractality as a Measure of Spatial Distribution of Geochemical Patterns

To quantify the spatial distribution of geochemical elements, the multifractality indices for Zn, Cu, Pt, Pd, Cr, Ni, Co, Pb, and As in lake-sediment samples in the Shining Tree area in the Abitibi area of Ontario are determined. The characterization of multifractal distribution patterns is based on the box-counting moment method and involves three functions: a mass exponent function (q); Coarse Hölder Exponent (q); and fractal dimension spectrum f( (q)). Properties of these functions at different values of q, characterize the spatial distribution of the variable under study. It is shown that the degree of multifractality defined by (1) can be used as a measure of irregularity of geochemical spatial dispersion patterns. The variations of Zn and Cu in the study area are characterized by relatively low degree of multifractality, whereas those for Pt, Pd, Cr, Ni, and Co; and particularly for As and Pb are characterized by higher multifractality indices.In the case of Zn and Cu, singularity spectra are close to a monofractal compared to the ones for As an Pb. The determination of multifractality indices allows us, in a quantitative way, to study the pattern of metal dispersions and link them to different physical processes, such as metal adsorption by organic material or glaciogenic processes.     

Evaluation of deep learning algorithms for national scale landslide susceptibility mapping of Iran

The identification of landslide-prone areas is an essential step in landslide hazard assessment and mitigation of landslide-related losses.In this study,we applied two novel deep learning algorithms,the recurrent neural network(RNN)and convolutional neural network(CNN),for national-scale landslide susceptibility mapping of Iran.We prepared a dataset comprising 4069 historical landslide locations and 11 conditioning factors(altitude,slope degree,profile curvature,distance to river,aspect,plan curvature,distance to road,distance to fault,rainfall,geology and land-sue)to construct a geospatial database and divided the data into the training and the testing dataset.We then developed RNN and CNN algorithms to generate landslide susceptibility maps of Iran using the training dataset.We calculated the receiver operating characteristic(ROC)curve and used the area under the curve(AUC)for the quantitative evaluation of the landslide susceptibility maps using the testing dataset.Better performance in both the training and testing phases was provided by the RNN algorithm(AUC=0.88)than by the CNN algorithm(AUC=0.85).Finally,we calculated areas of susceptibility for each province and found that 6%and 14%of the land area of Iran is very highly and highly susceptible to future landslide events,respectively,with the highest susceptibility in Chaharmahal and Bakhtiari Province(33.8%).About 31%of cities of Iran are located in areas with high and very high landslide susceptibility.The results of the present study will be useful for the development of landslide hazard mitigation strategies.     

Zoning map for drought prediction using integrated machine learning models with a nomadic people optimization algorithm

Natural Hazards - The modelling of drought is of utmost importance for the efficient management of water resources. This article used the adaptive neuro-fuzzy interface system (ANFIS), multilayer...