Numerical Simulation of Geosynthetic Reinforced Earth Structures Using Finite Element Method.

Geosynthetics are widely used as reinforcements in various earth structures. Current design methods are based on some simplified assumptions and are primarily modified versions of limit equilibrium methods used in design of the unreinforced earth structures. All of these design methods are conservative and result in uneconomical design. The best method for verifying the design assumptions and studying the stress-deformation behavior of geosynthetic reinforced earth structures is by constructing a series of large scale test walls with adequate instrumentation and collecting data from them. But the cost involved in such a scheme precludes undertaking this kind of study. Thus Numerical simulation provides an alternative and cost effective means for such a study. In the present research, a finite element model has been established for the numerical simulation of geosynthetic reinforced earth structures. The HiSS\ \delta\sb1 model has been implemented in the finite element method for modelling granular backfill. A new calibration technique has been developed based on genetic algorithms (GA). The new technique allows one to calibrate a constitutive model even when all the test data needed for calibration are not available. An elasto-plastic constitutive model based on the disturbed state concept (DSC) has been developed and implemented in the finite element method to model soil/geosynthetic interfaces. The interface model has been validated by simulating the large scale pull-out tests performed at the Louisiana Transportation Research Center (LTRC), and the finite element model of the geosynthetic reinforced wall has been validated with the observed behavior of two large scale test walls which were constructed and tested at the University of Colorado at Denver and the Royal Military College of Canada. The finite element simulation is able to predict the measured behavior of these walls well and the results of the simulation show some discrepancies and conservativeness in the assumptions made in the current design methods. The present finite element model can be used for parametric study and for formulating a realistic design method

A process capability index for zero-inflated processes

The proportion of zero defect (ZD) outputs is as an integral characteristic of a zero-inflated (ZI) process or high quality process. Different ZI processes can almost equally satisfy the same USL of number of defects but they can produce substantially different proportions of ZD products. The application of conventional method for process capability evaluation fails to discriminate these processes because in the conventional method, the process capability is evaluated taking into consideration the USL of number of defects only. In this paper, a new measure of process capability for ZI processes is proposed that can truly discriminate different ZI processes taking into account the USL of number of defects as well as the proportion of ZD units produced in these processes. In the proposed approach, at first a measure of process capability index (PCI) with respect to the USL is computed, and then the overall PCI is obtained by multiplying it with an appropriately defined multiplying factor. A real-life application is presented

Evaluating capability of a bivariate zero-inflated poisson process

A zero-inflated Poisson (ZIP) distribution is commonly used for modelling zero-inflated process data with single type of defect, and for developing appropriate tools for instituting statistical process control of manufacturing processes. However, in reality, such manufacturing scenarios are very common where more than one type of defect can occur. For example, occurrences of defects like solder short circuits (shorts) and absence of solder (skips) are very common on printed circuit boards. In literature, different forms of bivariate zero-inflated Poisson (BZIP) distributions are proposed, which can be used for modelling the manufacturing scenarios where two types of defects can occur. Control charts are designed for monitoring for such processes using BZIP models. Although evaluation of capability is an integral part of statistical process control of a manufacturing process, researchers have given very little effort on this aspect of zero-inflated processes. Only a few articles attempted to evaluate the capability of a univariate zero-inflated process and no work is reported on evbaluating capability of a bivariate zero-inflated process. In this paper, a methodology for measuring capability of a bivariate zero-inflated process is presented. The proposed methodology is illustrated using two case studies.&nbsp

Coherent bremsstrahlung and GDR width from 252Cf cold fission

The energy spectrum of the high energy gamma-rays in coincidence with the prompt gamma rays has been measured for the spontaneous fission of 252Cf. The nucleus-nucleus coherent bremsstrahlung of the accelerating fission fragments is observed and the result has been substantiated with a theoretical calculation based on the coulomb acceleration model. The width of the giant dipole resonance (GDR) decay from the excited fission fragments has been extracted for the first time and compared with the thermal shape fluctuation model (TSFM) in the liquid drop formalism. The extracted GDR width is significantly smaller than the predictions of TSFM.Comment: 12 pages, 3 figures, accepted for publication in Phys. Lett.

Estimation of the error matrix in a linear least square fit to the data from an experiment performed by smartphone photography

Determination of the Young modulus of a metal bar in the form of a cantilever is an old experimental concept. However, we have taken the advantage of modern advanced technology of smartphone camera to find the load depression graph of the cantilever by taking photographs with the smartphone camera. Smartphone photography allows us to find a precise transverse magnification of an object from the size of the real image formed on the sensor of the camera. Image size on the sensor can be obtained with micron level accuracy. From the load depression graph, we have determined the Young modulus of the bar. The sensitive measurements of the depression of the cantilever at its free end by its own weight, have allowed us to determine the density of aluminium. We have added an analysis of the chi squred minimisation technique for determining the parameters and their uncertainities in a linear fit. Starting from the curvature matrix we have made a comprehensive analysis of the error matrix relevant for a two parameter linear fit. Then we have shown how to form the error matrix for the fitted parameters which includes the covariance term between the two correlated parameters, in the context of our specific experiment. We have propagated the errors in the parameters to find the uncertainties in the Young modulus and the density of the bar. We have shown that a precise measurement is possible by smartphone photography