Modeling cyclic shearing of sands in the semifluidized state

Liquefaction is associated with the loss of mean effective stress and increase of the pore water pressure in saturated granular materials due to their contractive tendency under cyclic shear loading. The loss of mean effective stress is linked to loss of grain contacts, bringing the granular material to a “semifluidized state” and leading to development and accumulation of large cyclic shear strains. Constitutive modeling of the cyclic stress-strain response in earthquake-induced liquefaction and post-liquefaction is complex and yet very important for stress-deformation and performance-based analysis of sand deposits. A new state internal variable named strain liquefaction factor is introduced that evolves at low mean effective stresses, and its constitutive role is to reduce the plastic shear stiffness and dilatancy while maintaining the same plastic volumetric strain rate in the semifluidized state. This new constitutive ingredient is added to an existing critical state compatible, bounding surface plasticity reference model, that is well established for constitutive modeling of cyclic response of sands in the pre-liquefaction state. The roles of the key components of the proposed formulation are examined in a series of sensitivity analyses. Their combined effects in improving the performance of the reference model are examined by simulating undrained cyclic simple shear tests on Ottawa sand, with focus on reproducing the increasing shear strain amplitude as well as its saturation in the post-liquefaction response.     

Band Selection and Dimension Estimation for Hyperspectral Imagery—a New Approach Based on Invasive Weed Optimization

Currently, hyperspectral images have potential applications in many scientific areas due to the high spectral resolution. Extracting suitable and adequate bands/features from high dimensional data is a crucial task to classify such data. To overcome this issue, dimension reduction techniques have direct effects to improve the efficiency of classifiers on hyperspectral images. One common approach for decreasing the dimensionality is the feature/band selection by considering the optimum dimensionality of the hyperspectral imagery. In this paper, a new method was proposed to select optimal band for classification application, based on a metaheuristic Invasive Weed Optimization (IWO) algorithm. In this regard, the K-nearest neighbour (K-NN) technique was used as the classifier. Moreover, as a by-product of our band selection method, a new method was proposed to estimate an optimum dimension of the reduced hyperspectral images for better classification. Experimental results over three real-world hyperspectral datasets clearly showed that the proposed IWO-based band selection algorithm of this study led to the significant progress in selecting suitable bands for classification applications and estimation of optimum dimensionality of these datasets. In this regard, the overall accuracy (OA) of classification of the proposed IWO-based band selection algorithm was 92.02, 93.57, and 89.72 % for each dataset, respectively. Moreover, results reveal the superiority of the proposed IWO-based band selection algorithm against the other algorithms including GA, SA, ACO, and PSO for band selection purpose.     

Collaborative personalized multi‐criteria wayfinding for wheelchair users in outdoors

While finding the optimal route for users with physical disabilities and personalizing routes for each user, on the one hand, and collaborative wayfinding, on the other hand, have been addressed in the pedestrian navigation systems literature, there has not been much research on combining the two activities. The problem associated with wayfinding approaches solely based on information about network segments and personal preferences is that the information about segments in the database may not, correctly and/or adequately address user preferences. The problem associated with wayfinding approaches solely based on the ratings given to routes by wheelchair users is the lack of rates (or scores) for all possible routes between all possible origin‐destination pairs in the network. This article discusses an approach to combine these two approaches for wayfinding to augment each other's shortcomings. To evaluate the personalized wayfinding approach, we utilize a route index, called a comparison index. The results show that with a P‐value of 9%, the routes obtained from our approach are more accessible than the routes obtained from another approach developed in another study. To evaluate the collaborative wayfinding approach, a Monte Carlo simulation was conducted which reflects the updates in routes as users' feedbacks become available.     

A Modular Support Vector Machine for Active Learning of Urban Remote Sensing Images Classification in Algeria

In this work, we present a new strategy of active learning, based on a modular version of support vector machine (MSVM) applied to urban remote sensing images in Algeria. In general, the training set is highly imbalanced, which gives more complex models; this difficulty is solved by dividing the problem at hand into a set of sub-problems, where each sub-model could be simpler to solve. The support vector machine is introduced to solve the problem of classification based on image remote sensing data related to atmospheric conditions and illumination reflectance. The aim of the proposed method is to improve the accuracy in order to understand the correlated elements of urban structures (the site, the built, the parcels, the network, the space), to generate the final classification result. In particular, we propose a new method based on the modular support vector machine (MSVM) adopted to active learning method, using three different clustering methods (i) k-means, (ii) fuzzy c-means (FCM), and (iii) Gustafson–Kessel (GKclust). Experimental results obtained on two QuickBird multispectral images of Sétif and Batna cities in the eastern of Algeria confirm the capabilities of the proposed methods based on the ensemble of model trained with different task decomposition compared to a traditional model using active learning. This method improves each class presents a main register in urban structure tissues.     

Inverse modelling for freshwater lens in small islands: Kish Island,Persian Gulf

A number of challenges including instability, nonconvergence, nonuniqueness, nonoptimality, and lack of a general guideline for inverse modelling have limited the application of automatic calibration by generic inversion codes in solving the saltwater intrusion problem in real‐world cases. A systematic parameter selection procedure for the selection of a small number of independent parameters is applied to a real case of saltwater intrusion in a small island aquifer system in the semiarid region of the Persian Gulf. The methodology aims at reducing parameter nonuniqueness and uncertainty and the time spent on inverse modelling computations. Subsequent to the automatic calibration of the numerical model, uncertainty is analysed by constrained nonlinear optimization of the inverse model. The results define the percentage of uncertainty in the parameter estimation that will maintain the model inside a user‐defined neighbourhood of the best possible calibrated model. Sensitivity maps of both pressure and concentration for the small island aquifer system are also developed. These sensitivity maps indicate higher sensitivity of pressure to model parameters compared with concentration. These sensitivity maps serve as a benchmark for correlation analysis and also assist in the selection of observations points of pressure and concentration in the calibration process. Copyright © 2012 John Wiley & Sons, Ltd.     

Estimating trends of the Mediterranean Sea level changes from tide gauge and satellite altimetry data (1993-2015)

The impact of climate change on sea level has received a great deal of attention by scientists worldwide.In this context,the problem of sea levels on global and regional scales have been analyzed in a number of studies based on tide gauges observations and satellite altimetry measurements.This study focuses on trend estimates from 18 high-quality tide gauge stations along the Mediterranean Sea coast.The seasonal Mann-Kendall test was run at a 5% significance level for each of the 18 stations for the period of 1993-2015(satellite altimetry era).The results of this test indicate that the trends for 17 stations were statistically significant and showed an increase(no significant trend was observed only at one station).The rates of sea level change for the 17 stations that exhibit significant trends,estimated using seasonal Sen's approach,range after correction for Vertical Land Motion(VLM)from 1.48 to 8.72 mm/a for the period1993-201 5.Furthermore,the magnitude of change at the location of each tide gauge station was estimated using the satellite altimetry measurements.Thus,the results obtained agree with those from the tide-gauge data analysis.