Adaptive state estimation of groundwater contaminant boundary input flux in a 2-dimensional aquifer

In many circumstances involving heat and mass transfer issues,it is considered impractical to measure the input flux and the resulting state distribution in the domain.Therefore,the need to develop techniques to provide solutions for such problems and estimate the inverse mass flux becomes imperative.Adaptive state estimator(ASE)is increasingly becoming a popular inverse estimation technique which resolves inverse problems by incorporating the semi-Markovian concept into a Bayesian estimation technique,thereby developing an inverse input and state estimator consisting of a bank of parallel adaptively weighted Kalman filters.The ASE is particularly designed for a system that encompasses independent unknowns and/or random switching of input and measurement biases.The present study describes the scheme to estimate the groundwater input contaminant flux and its transient distribution in a conjectural two-dimensional aquifer by means of ASE,which in particular is because of its unique ability to efficiently handle the process noise giving an estimation of keeping the relative error range within 10%in 2-dimensional problems.Numerical simulation results show that the proposed estimator presents decent estimation performance for both smoothly and abruptly varying input flux scenarios.Results also show that ASE enjoys a better estimation performance than its competitor,Recursive Least Square Estimator(RLSE)due to its larger error tolerance in greater process noise regimes.ASE's inherent deficiency of being slower than the RLSE,resulting from the complexity of algorithm,was also noticed.The chosen input scenarios are tested to calculate the effect of input area and both estimators show improved results with an increase in input flux area especially as sensors are moved closer to the assumed input location.     

Quantification of source-to-site distance uncertainty in ground motion models

Natural Hazards - Among different input data, source-to-site distance plays a major role in the results of ground motion models (GMMs). In order to determine the source-to-site distance, geometric...     

Bearing capacity of non-associative coaxial granular materials by upper bound limit analysis and finite elements

In this paper, an upper bound estimate of the limit load on non-associative coaxial granular materials is presented. The kinematic approach of the upper bound limit analysis has been utilised. The failure mechanism is assumed to coincide with the direction of the shear bands at every point throughout the body. The shear band orientation in non-associative coaxial materials, i.e. those with the same major principal stress and major principal strain increment directions, can be found based on the angle of dilation and the major principal stress direction. Therefore, having known the stress field at limiting equilibrium, the orientation of the shear bands and hence, the failure mechanism can be obtained. In this study, the stress field is first determined by the method of stress characteristics. Then, the finite element interpolation technique is used to interpolate the stress field and to find the orientation of the shear bands at every point within the field. Once the failure mechanism and the stress state at every point along velocity discontinuities have been found, the upper bound limit analysis has been performed to estimate the limit load.     

Dynamic earth pressure on rigid retaining walls induced by a neighboring machine foundation,by the meshless local Petrov-Galerkin method

Dynamic earth pressure induced by machine foundations on a neighboring retaining wall is analyzed with emphasis on factors which control the intensity and location of the design forces. The meshless local Petrov-Galerkin(MLPG) method is used to analyze the problem for a variety of retaining wall and machine foundation geometries. The soil medium is assumed to be homogeneous and visco-elastic. The machine foundation is idealized as a harmonic sinusoidal dynamic force often encountered in practice. A number of analyses have been made to reveal the effect of the loading frequency, the location and size of the foundation and the soil shear wave velocity on the distribution and magnitude of the dynamic earth pressure. Results indicate that there is a critical frequency and a critical location for which the passive pressure takes the maxima in the entire duration of the dynamic load.     

New structural implications for the central Sahara (Algeria), from the revisited Upper Carboniferous “Hassi Bachir” Formation: Paleomagnetic constraints

Paleomagnetic investigations of the folded Upper Namurian–Lower Moscovian “Hassi Bachir” Formation cropping out in the “Ahnet” basin (Central Sahara, Algeria) yield two magnetic components. A pre-folding primary magnetization (D = 136.1°, I = 22.0°, k = 217, α₉₅ = 2.6°) enables us to define a paleomagnetic pole (32.8°S, 55.7°E, K = 328 and A₉₅ = 2.0°) which better constrains a paleopole that was determined by Daly and Irving [Daly, L., Irving, E., 1983. Paléomagnétisme des roches carbonifères du Sahara central; analyse des aimantations juxtaposées; configurations de la Pangée. Ann. Geophys. 1, 207–216] for the same formation. A secondary component consists in a synfolding remagnetization and shows that post-Permian tectonics account for at least about half of the total folding in the studied area. This indicates that Mesozoic folding noted 150 km to the West in the Reggane basin [Smith, B., Derder, M.E.M., Henry, B., Bayou, B., Amenna, M., Djellit, H., Yelles, A.K., Garces, M., Beamud, E., Callot, J.P., Eschard, R., Chambers, A., Aifa, T., Ait Ouali, R., Gandriche, H., 2006. Relative importance of the Hercynian and post-Jurassic tectonic phases in the Saharan platform: a palaeomagnetic study of Jurassic sills in the Reggane basin (Algeria). Geophys. J. Int. 167, 380–396] is not local and affected at least the entire north-western part of the Hoggar area. This reconfirms that the folding of the Paleozoic cover in the Sahara platform should not be restricted to the Hercynian orogeny.     

Effect of soil cohesion and friction angles on reverse faults

Severe faults have caused many earthquakes around the world throughout history.More recently,earthquakes have occurred in Taiwan,China(Chi-Chi fault),and elsewhere,causing loss of lives and destroying many buildings and structures.These tectonic movements have gained attention from engineers,and in the past 15 years,the focus has been on faulting mechanisms.In this study,a physical model(1 g)was fabricated and used to evaluate the impact of a reverse fault in a field with a tunnel.In the 1 g model,researchers installed additional gauges on the tunnel,so that all the displacements could be adjusted,and all the responses could be monitored during faulting.An experimental study of various soil properties(cohesion and friction angles)in reverse faults on the tunnel lining were carried out and are described herein.A comparison of results for different levels of soil cohesion revealed that it can dramatically reduce the displacement by as much as 40%,and that friction angles of 27ocan record approximately 60%more displacements than at 37o.Furthermore,a comparison of fault angles of 30oand 60oindicates that the displacements can be different by more than 43%in cohesionless soil and about 64%for a friction angle of 27o.     

Large‐scale regional delineation of riparian vegetation in the arid and semi‐arid Pilbara region,WA

Multiscene Landsat 5 TM imagery, Principal Component Analysis, and the Normalized Difference Vegetation Index were used to produce the first region‐scale map of riparian vegetation for the Pilbara (230,000 km²), Western Australia. Riparian vegetation is an environmentally important habitat in the arid and desert climate of the Pilbara. These habitats are supported by infrequent flow events and in some locations by groundwater discharge. Our analysis suggests that riparian vegetation covers less than 4% of the Pilbara region, whereas almost 10.5% of this area is composed of groundwater dependent vegetation (GDV). GDV is often associated with open water (river pools), providing refugia for a variety of species. GDV has an extremely high ecological value and are often important Indigenous sites. This paper demonstrates how Landsat data calibrated to Top of Atmosphere reflectance can be used to delineate riparian vegetation across 16 Landsat scenes and two Universal Transverse Mercator spatial zones. The proposed method is able to delineate riparian vegetation and GDV, without the need for Bidirectional Reflectance Distribution Function correction. Results were validated using ground truth data from local and regional scale vegetation surveys.     

Analysis of Spatial Autocorrelation Patterns of Heavy and Super-Heavy Rainfall in Iran

Rainfall is a highly variable climatic element, and rainfall-related changes occur in spatial and temporal dimensions within a regional climate. The purpose of this study is to investigate the spatial autocorrelation changes of Iran's heavy and super-heavy rainfall over the past 40 years. For this purpose, the daily rainfall data of 664 meteorological stations between 1971 and 2011 are used. To analyze the changes in rainfall within a decade, geostatistical techniques like spatial autocorrelation analysis of hot spots, based on the Getis-Ord Gistatistic, are employed. Furthermore, programming features in MATLAB, Surfer, and GIS are used. The results indicate that the Caspian coast, the northwest and west of the western foothills of the Zagros Mountains of Iran, the inner regions of Iran, and southern parts of Southeast and Northeast Iran,have the highest likelihood of heavy and super-heavy rainfall. The spatial pattern of heavy rainfall shows that, despite its oscillation in different periods, the maximum positive spatial autocorrelation pattern of heavy rainfall includes areas of the west, northwest and west coast of the Caspian Sea. On the other hand, a negative spatial autocorrelation pattern of heavy rainfall is observed in central Iran and parts of the east, particularly in Zabul. Finally, it is found that patterns of super-heavy rainfall are similar to those of heavy rainfall.     

Optimal fusion of optical and SAR high-resolution images for semiautomatic building detection

Building detection from different high-resolution aerial and satellite images has been a notable research topic in recent decades. The primary challenges are occlusions, shadows, different roof types, and similar spectral behavior of urban covers. Integration of different data sources is a solution to supplement the input feature space and improve the existing algorithms. Regarding the different nature and unique characteristics of optical and radar images, there are motivations for their fusion. This paper is aimed to identify an optimal fusion of radar and optical images to overcome their individual shortcomings and weaknesses. For this reason, panchromatic, multispectral, and radar images were first classified individually, and their strengths and weaknesses were evaluated. Different feature-level fusions of these data sets were then assessed followed by a decision-level fusion of their results. In both the feature and decision levels of integration, artificial neural networks were applied as the classifiers. Several post-processing methods using normalized different vegetation index, majority filter, and area filter were finally applied to the results. Overall accuracy of 92.8% and building detection accuracy of 89.1% confirmed the ability of the proposed fusion strategy of optical and radar images for building detection purposes.