Seismic reliability assessment of internal stability of reinforced soil walls using the pseudo-dynamic method

The study focuses on probabilistic assessment of the internal seismic stability of reinforced soil structures (RSS) subjected to earthquake loading in the framework of the pseudo-dynamic method. In the literature, the pseudo-static approach has been used to compute reliability indices against the tension and pullout failure modes, and the real dynamic nature of earthquake accelerations cannot be considered. The work presented in this paper makes use of the horizontal and vertical sinusoidal accelerations, amplification of vibrations, shear wave and primary wave velocities and time period. This approach is applied to quantify the influence of the backfill properties, geosynthetic reinforcement and characteristics of earthquake ground motions on reliability indices in relation to the tension and pullout failure modes. Seismic reliability indices at different levels of geosynthetic layers are determined for different magnitudes of seismic acceleration, soil amplification, shear wave and primary wave velocities. The results are compared with the pseudo-static method, and the significance of the present methodology for designing reinforced soil structures is discussed.

Genetic Algorithm for Damage Assessment

Genetic Algorithms (GAs) are recognized as an alternative class of computational model, which mimic natural evolution to solve problems in a wide domain including machine learning, music generation, genetic synthesis etc. In the present study Genetic Algorithm has been employed to obtain damage assessment of composite structural elements. It is considered that a state of damage can be modeled as reduction in stiffness. The task is to determine the magnitude and location of damage. In a composite plate that is discretized into a set of finite elements, if a jth element is damaged, the GA based technique will predict the reduction in Ex and Ey and the location j. The fact that the natural frequency decreases with decrease in stiffness is made use of in the method. The natural frequency of any two modes of the damaged plates for the assumed damage parameters is facilitated by the use of Eigen sensitivity analysis. The Eigen value sensitivities are the derivatives of the Eigen values with respect to certain design parameters. If ωiu is the natural frequency of the ith mode of the undamaged plate and ωid is that of the damaged plate, with δωi as the difference between the two, while δωk is a similar difference in the kth mode, R is defined as the ratio of the two. For a random selection of Ex,Ey and j, a ratio Ri is obtained. A proper combination of Ex,Ey and j which makes Ri−R=0 is obtained by Genetic Algorithm.

Generalized method for assessment of compressibility of rockfill material

Examination of experimental data of the modelled rockfill materials using parallel gradation technique has revealed that the plots of logarithm of strain at failure against logarithm of confining pressure are linear. Also, a trend of increase in failure strain with increase in confining pressure and maximum size of the particle have been observed. The approach presented in this paper highlights the prediction of volume change properties of rockfill materials over a range of confining pressures and particle sizes based on the results of only two tests carried out at two different confining pressures for a maximum particle size of modelled material with the use of parallel gradation technique. Two test approach and its application in modelling of rockfill materials to estimate its volume change behaviour is illustrated by means of a selected experimental data available in the literature.

An ANN based approach for voltage stability assessment

Artificial Neural Networks (ANNs) have recently been proposed as an alterative method for salving certain traditional problems in power systems where conventional techniques have not achieved the desired speed, accuracy or efficiency. This paper presents application of ANN where the aim is to achieve fast voltage stability margin assessment of power network in an energy control centre (ECC), with reduced number of appropriate inputs. L-index has been used for assessing voltage stability margin. Investigations are carried out on the influence of information encompassed in input vector and target out put vector, on the learning time and test performance of multi layer perceptron (MLP) based ANN model. LP based algorithm for voltage stability improvement, is used for generating meaningful training patterns in the normal operating range of the system. From the generated set of training patterns, appropriate training patterns are selected based on statistical correlation process, sensitivity matrix approach, contingency ranking approach and concentric relaxation method. Simulation results on a 24 bus EHV system, 30 bus modified IEEE system, and a 82 bus Indian power network are presented for illustration purposes.

Novel algorithm for online voltage stability assessment based on feed forward neural network

This paper presents an artificial feed forward neural network (FFNN) approach for the assessment of power system voltage stability. A novel approach based on the input-output relation between real and reactive power, as well as voltage vectors for generators and load buses is used to train the neural net (NN). The input properties of the feed forward network are generated from offline training data with various simulated loading conditions using a conventional voltage stability algorithm based on the L-index. The neural network is trained for the L-index output as the target vector for each of the system loads. Two separate trained NN, corresponding to normal loading and contingency, are investigated on the 367 node practical power system network. The performance of the trained artificial neural network (ANN) is also investigated on the system under various voltage stability assessment conditions. As compared to the computationally intensive benchmark conventional software, near accurate results in the value of L-index and thus the voltage profile were obtained. Proposed algorithm is fast, robust and accurate and can be used online for predicting the L-indices of all the power system buses. The proposed ANN approach is also shown to be effective and computationally feasible in voltage stability assessment as well as potential enhancements within an overall energy management system in order to determining local and global stability indices

Assessment of seismic hazard and liquefaction potential of Gujarat based on probabilistic approaches

Gujarat is one of the fastest-growing states of India with high industrial activities coming up in major cities of the state. It is indispensable to analyse seismic hazard as the region is considered to be most seismically active in stable continental region of India. The Bhuj earthquake of 2001 has caused extensive damage in terms of causality and economic loss. In the present study, the seismic hazard of Gujarat evaluated using a probabilistic approach with the use of logic tree framework that minimizes the uncertainties in hazard assessment. The peak horizontal acceleration (PHA) and spectral acceleration (Sa) values were evaluated for 10 and 2 % probability of exceedance in 50 years. Two important geotechnical effects of earthquakes, site amplification and liquefaction, are also evaluated, considering site characterization based on site classes. The liquefaction return period for the entire state of Gujarat is evaluated using a performance-based approach. The maps of PHA and PGA values prepared in this study are very useful for seismic hazard mitigation of the region in future.

Crowding, molecular volume and plasticity: An assessment involving crystallography, NMR and simulations

The discrepancy between the X-ray and NMR structures of Mycobacterium tuberculosis peptidyl-tRNA hydrolase in relation to the functionally important plasticity of the molecule led to molecular dynamics simulations. The X-ray and the NMR studies along with the simulations indicated an inverse correlation between crowding and molecular volume. A detailed comparison of proteins for which X-ray and the NMR structures appears to confirm this correlation. In consonance with the reported results of the investigations in cellular compartments and aqueous solution, the comparison indicates that the crowding results in compaction of the molecule as well as change in its shape, which could specifically involve regions of the molecule important in function. Crowding could thus influence the action of proteins through modulation of the functionally important plasticity of the molecule. Selvaraj M, Ahmad R, Varshney U and Vijayan M 2012 Crowding, molecular volume and plasticity: An assessment involving crystallography, NMR and simulations. J. Biosci. 37 953-963] DOI 10.1007/s12038-012-9276-5

Biocides - resistance, cross-resistance mechanisms and assessment

Importance of the field: Antibiotic resistance in bacterial pathogens has increased worldwide leading to treatment failures. Concerns have been raised about the use of biocides as a contributing factor to the risk of antimicrobial resistance (AMR) development. In vitro studies demonstrating increase in resistance have often been cited as evidence for increased risks. It is therefore important to understand the mechanisms of resistance employed by bacteria toward biocides used in consumer products and their potential to impart cross-resistance to therapeutic antibiotics. Areas covered: In this review, the mechanisms of resistance and cross-resistance reported in the literature toward biocides commonly used in consumer products are summarized. The physiological and molecular techniques used in describing and examining these mechanisms are reviewed and application of these techniques for systematic assessment of biocides for their potential to develop resistance and/or cross-resistance is discussed. Expert opinion: The guidelines in the usage of biocides in household or industrial purpose should be monitored and regulated to avoid the emergence of any MDR strains. The genetic and molecular methods to monitor the resistance development to biocides should be developed and included in preclinical and clinical studies.

NMR studies of preimplantation embryo metabolism in human assisted reproductive techniques: a new biomarker for assessment of embryo implantation potential

There has been growing interest in understanding energy metabolism in human embryos generated using assisted reproductive techniques (ART) for improving the overall success rate of the method. Using NMR spectroscopy as a noninvasive tool, we studied human embryo metabolism to identify specific biomarkers to assess the quality of embryos for their implantation potential. The study was based on estimation of pyruvate, lactate and alanine levels in the growth medium, ISM1, used in the culture of embryos. An NMR study involving 127 embryos from 48 couples revealed that embryos transferred on Day 3 (after 72 h in vitro culture) with successful implantation (pregnancy) exhibited significantly (p < 10(-5)) lower pyruvate/alanine ratios compared to those that failed to implant. Lactate levels in media were similar for all embryos. This implies that in addition to lactate production, successfully implanted embryos use pyruvate to produce alanine and other cellular functions. While pyruvate and alanine individually have been used as biomarkers, the present study highlights the potential of combining them to provide a single parameter that correlates strongly with implantation potential. Copyright (C) 2012 John Wiley & Sons, Ltd.