An Elasto-Plastic Constitutive Model for Rock Joints Under Cyclic Loading and Constant Normal Stiffness Conditions   总被引：1，自引：1，他引：0  

Jan Nemcik  Ali Mirzaghorbanali  Naj Aziz 《Geotechnical and Geological Engineering》2014,32(2):321-335

An elasto-plastic constitutive model is introduced for rock joints under cyclic loading, considering the additional shear resistance generated by the asperity damage in the first forward shear cycle and sliding mechanism for further shearing. A series of cyclic loading direct shear tests was conducted on artificial joints with triangular asperities and replicas of a real rock asperity surface under constant normal stiffness (CNS) conditions. The model was calibrated and then validated using selected data sets from the experimental results. Model simulations were found to be in good agreement with the rock joints behaviour under cyclic loading and CNS conditions both in stress prediction and dilation behaviour. In addition, dynamic stability analysis of an underground structure was carried out, using Universal Distinct Element Code and the proposed constitutive model.  相似文献   

Retrieval and Validation of Chlorophyll-a Concentrations in the Coastal Waters Off Yanam and Kakinada (Godavari) Basin Along East Coast of India     

T. Preethi Latha  P. V. Nagamani  K. H. Rao  S. K. Dash  S. B. Choudhury  Aziz Rehman  V. V. S. S. Sarma  M. Naveen Babu  P. Amarendra  B. Srinivasa Rao  T. D. V. Prasad 《Journal of the Indian Society of Remote Sensing》2014,42(1):129-138

In this study chlorophyll measurements were made during March 2012 in the estuarine waters of Off Kakinada and Yanam coast, Bay of Bengal onboard a coastal vessel. In-situ water samples and optical data was collected at 21 stations (surface to 150 m depth) using Underwater radiometer (Hyperpro-II). In-vivo chlorophyll profiles were collected using wet labs fluorometer integrated with underwater Hyperspectral radiometer. Chlorophyll-a concentrations were estimated using HPLC by collecting the water samples at each sampling location. And also chlorophyll-a concentrations were retrieved from the OCM-2 data of OCEANSAT-2 satellite, processed using SeaDAS v.6.2 with the available global ocean colour algorithms namely, OC2 and OC4V4. A total of 33 samples used covering all the stations for chlorophyll-a estimation, and surface water samples of all the stations only being used for direct comparison among chlorophyll concentrations of HPLC, in-situ (fluorometrically integrated to Hyperpro-II) and retrieved from OCM-2. A good correlation found between the Fluorometer derived and HPLC measured chlorophyll-a concentration with an R² value of 0.78. The relation between Chlorophyll-a concentration measured from HPLC and retrieved from OCM-2 (OC2 and OC4V4 algorithms) using SeaDASv.6.2 for 10 samples has been compared for validation and obtained an R² value of 0.6. Also comparisons done with the in-situ measured (fluorometer) Chlorophyll-a concentration with OCM-2 chlorophyll data (OC4-V4 and OC2 algorithms) and validation with 10 concurrent in-situ surface measurements showed a significant overestimation by OCM-2 at low chlorophyll-a concentrations and underestimation at high chlorophyll-a concentrations.  相似文献   

Non-linear electrodynamics and the gravitational redshift of highly magnetized neutron stars     

Herman J. Mosquera Cuesta  José M. Salim 《Monthly notices of the Royal Astronomical Society》2004,354(4):L55-L59

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Yield Forecasting of Spring Maize Using Remote Sensing and Crop Modeling in Faisalabad-Punjab Pakistan     

Ishfaq Ahmad  Umer Saeed  Muhammad Fahad  Asmat Ullah  M. Habib ur Rahman  Ashfaq Ahmad  Jasmeet Judge 《Journal of the Indian Society of Remote Sensing》2018,46(10):1701-1711

Real time, accurate and reliable estimation of maize yield is valuable to policy makers in decision making. The current study was planned for yield estimation of spring maize using remote sensing and crop modeling. In crop modeling, the CERES-Maize model was calibrated and evaluated with the field experiment data and after calibration and evaluation, this model was used to forecast maize yield. A Field survey of 64 farm was also conducted in Faisalabad to collect data on initial field conditions and crop management data. These data were used to forecast maize yield using crop model at farmers’ field. While in remote sensing, peak season Landsat 8 images were classified for landcover classification using machine learning algorithm. After classification, time series normalized difference vegetation index (NDVI) and land surface temperature (LST) of the surveyed 64 farms were calculated. Principle component analysis were run to correlate the indicators with maize yield. The selected LSTs and NDVIs were used to develop yield forecasting equations using least absolute shrinkage and selection operator (LASSO) regression. Calibrated and evaluated results of CERES-Maize showed the mean absolute % error (MAPE) of 0.35–6.71% for all recorded variables. In remote sensing all machine learning algorithms showed the accuracy greater the 90%, however support vector machine (SVM-radial basis) showed the higher accuracy of 97%, that was used for classification of maize area. The accuracy of area estimated through SVM-radial basis was 91%, when validated with crop reporting service. Yield forecasting results of crop model were precise with RMSE of 255 kg ha^?1, while remote sensing showed the RMSE of 397 kg ha^?1. Overall strength of relationship between estimated and actual grain yields were good with R² of 0.94 in both techniques. For regional yield forecasting remote sensing could be used due greater advantages of less input dataset and if focus is to assess specific stress, and interaction of plant genetics to soil and environmental conditions than crop model is very useful tool.  相似文献   

Design flood estimation in ungauged catchments using genetic algorithm-based artificial neural network (GAANN) technique for Australia   总被引：2，自引：0，他引：2  

K. Aziz  Sohail Rai  A. Rahman 《Natural Hazards》2015,77(2):805-821

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An old galaxy group: Chandra X-ray observations of the nearby fossil group NGC 6482     

Habib G. Khosroshahi  Laurence R. Jones  Trevor J. Ponman 《Monthly notices of the Royal Astronomical Society》2004,349(4):1240-1250

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A second order Jupiter-Saturn planetary theory     

Osman M. Kamel  Abdel Aziz Bakry 《Earth, Moon, and Planets》1982,27(4):417-430

We present a second order secular Jupiter-Saturn planetary theory through Poincaré canonical variables, von Zeipel's method and Jacobi-Radau referential. We neglect in our expansions terms of power higher than the fourth with respect to eccentricities and sines of inclinations. We assume that the disturbing function is composed of secular and critical terms only. We shall deriveF _2si and writeF _2s in terms of Poincaré canonical variables in Part II of this problem.  相似文献   

Third order uranus-neptune planetary theory     

Osman M. Kamel  Abdel Aziz Bakry 《Earth, Moon, and Planets》1988,40(3):227-293

We calculate in this paper the secular and critical terms arising from the principal part of the classical planetary Hamiltonian. This is the first step to establish a third order canonical planetary theory of Uranus-Neptune through the Hori-Lie technique. We truncate our expansions at the second degree of eccentricity-inclination. Our planetary theory is expressed in terms of the canonical variables of H. Poincaré.  相似文献   

Fitting optimum order of Markov chain models for daily rainfall occurrences in Peninsular Malaysia     

Sayang Mohd Deni  Abdul Aziz Jemain  Kamarulzaman Ibrahim 《Theoretical and Applied Climatology》2009,97(1-2):109-121

The analysis of the daily rainfall occurrence behavior is becoming more important, particularly in water-related sectors. Many studies have identified a more comprehensive pattern of the daily rainfall behavior based on the Markov chain models. One of the aims in fitting the Markov chain models of various orders to the daily rainfall occurrence is to determine the optimum order. In this study, the optimum order of the Markov chain models for a 5-day sequence will be examined in each of the 18 rainfall stations in Peninsular Malaysia, which have been selected based on the availability of the data, using the Akaike’s (AIC) and Bayesian information criteria (BIC). The identification of the most appropriate order in describing the distribution of the wet (dry) spells for each of the rainfall stations is obtained using the Kolmogorov-Smirnov goodness-of-fit test. It is found that the optimum order varies according to the levels of threshold used (e.g., either 0.1 or 10.0 mm), the locations of the region and the types of monsoon seasons. At most stations, the Markov chain models of a higher order are found to be optimum for rainfall occurrence during the northeast monsoon season for both levels of threshold. However, it is generally found that regardless of the monsoon seasons, the first-order model is optimum for the northwestern and eastern regions of the peninsula when the level of thresholds of 10.0 mm is considered. The analysis indicates that the first order of the Markov chain model is found to be most appropriate for describing the distribution of wet spells, whereas the higher-order models are found to be adequate for the dry spells in most of the rainfall stations for both threshold levels and monsoon seasons.  相似文献   

The best fitting distribution of annual maximum rainfall in Peninsular Malaysia based on methods of L-moment and LQ-moment   总被引：1，自引：1，他引：0  

Wan Zawiah Wan Zin  Abdul Aziz Jemain  Kamarulzaman Ibrahim 《Theoretical and Applied Climatology》2009,98(3-4):337-350

This paper discusses patterns of annual and monthly precipitation variability at seven weather stations in east central Europe (1851–2007). Precipitation patterns were compared to three simple regional indices of atmospheric circulation, i.e., western circulation, southern circulation and the cyclonicity (C) index and a relationship between precipitation and the North Atlantic Oscillation index was identified. Correlations of the monthly records and multiple regression, using a principal components’ analysis, helped determine the statistical significance of the dependence of precipitation on the circulation indices. The Mann–Kendall test revealed no trend to change in any of the precipitation series, but a certain spatial regularity could be discerned in the phase of the annual periodic component. A common feature of the variability in central European annual precipitation is the dry period identified in the 1980s and the first half of the 1990s. In the northern part of the region, above-average precipitation was noted from the 1960s through to the mid-1970s as a result of the frequent prevalence of depressions. South of the divide, the wettest period was recorded at the turn of 1930s/1940s. After a number of very wet years in the last decade of the twentieth century and the beginning of the twenty-first century, precipitation began to fall at all of the region’s weather stations. The C index is the strongest circulation-linked factor influencing precipitation in central Europe and it accounts for more than 40% of the variance in spatially averaged wintertime precipitation.  相似文献