Faculty Publications
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Item Correlation between Petrographical and engineering properties of Ilkal granites, Karnataka(CAFET INNOVA Technical Society 1-2-18/103, Mohini Mansion, Gagan Mahal Road, Domalguda, Hyderabad 500029, 2011) Venkat Reddy, D.; Santosh, H.G.; Priyanka, K.Granitic rocks show a variety of engineering properties that may affect quarrying operations, slope stability, mining and the use of rock as a structural as well as architectural material. In present investigation, correlation analysis is carried out for experimental results to study the influence of mineralogical and textural characteristics on physical and strength properties using SPSS software. A variety of granitic rock samples from different parts of Ilkal were subjected to study petrographical and then same samples were tested to determine the specific gravity, bulk and dry density, water absorption, porosity, P-wave velocity, rebound hardness, point load strength index, uniaxial compressive strength and tensile strength. The study revealed that a petrographical characteristic like grain size, mineral composition and the bonding between each mineral of granitic rocks plays a major role in contributing the strength parameters. © 2011 Cafet-Innova Technical Society. All rights reserved.Item Analysis of observed soil moisture patterns under different land covers in Western Ghats, India(2011) Venkatesh, B.; Lakshman, N.; Purandara, B.K.; Reddy, V.B.An understanding of the soil moisture variability is necessary to characterize the linkages between a region's hydrology, ecology and physiography. In the changing land use scenario of Western Ghats, India, where deforestation along with extensive afforestation with exotic species is being undertaken, there is an urgent need to evaluate the impacts of these changes on regional hydrology. The objectives of the present study were: (a) to understand spatio-temporal variability of soil water potential and soil moisture content under different land covers in the humid tropical Western Ghats region and (b) to evaluate differences if any in spatial and temporal patterns of soil moisture content as influenced by nature of land cover. To this end, experimental watersheds located in the Western Ghats of Uttara Kannada District, Karnataka State, India, were established for monitoring of soil moisture. These watersheds possessed homogenous land covers of acacia plantation, natural forest and degraded forest. In addition to the measurements of hydro-meteorological parameters, soil matric potential measurements were made at four locations in each watershed at 50 cm, 100 cm and 150 cm depths at weekly time intervals during the period October 2004-December 2008.Soil moisture contents derived from potential measurements collected were analyzed to characterize the spatial and temporal variations across the three land covers. The results of ANOVA (p<0.01, LSD) test indicated that there was no significant change in the mean soil moisture across land covers. However, significant differences in soil moisture with depth were observed under forested watershed, whereas no such changes with depth were noticed under acacia and degraded land covers. Also, relationships between soil moisture at different depths were evaluated using correlation analysis and multiple linear regression models for prediction of soil moisture from climatic variables and antecedent moisture condition were developed and tested. A regression model relating near-surface soil moisture (50 cm) with profile soil moisture content was developed which may prove useful when surface soil moisture contents derived from satellite remote sensing are available. Overall results of this study indicate that while the nature of land cover has an influence on the spatio-temporal variability of soil moisture, other variables related to topography may have a more dominant effect. © 2010 Elsevier B.V.Item Nonlinear system identification using memetic differential evolution trained neural networks(2011) Subudhi, B.; Jena, D.Several gradient-based approaches such as back propagation (BP) and Levenberg Marquardt (LM) methods have been developed for training the neural network (NN) based systems. But, for multimodal cost functions these procedures may lead to local minima, therefore, the evolutionary algorithms (EAs) based procedures are considered as promising alternatives. In this paper we focus on a memetic algorithm based approach for training the multilayer perceptron NN applied to nonlinear system identification. The proposed memetic algorithm is an alternative to gradient search methods, such as back-propagation and back-propagation with momentum which has inherent limitations of many local optima. Here we have proposed the identification of a nonlinear system using memetic differential evolution (DE) algorithm and compared the results with other six algorithms such as Back-propagation (BP), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Differential Evolution (DE), Genetic Algorithm Back-propagation (GABP), Particle Swarm Optimization combined with Back-propagation (PSOBP). In the proposed system identification scheme, we have exploited DE to be hybridized with the back propagation algorithm, i.e. differential evolution back-propagation (DEBP) where the local search BP algorithm is used as an operator to DE. These algorithms have been tested on a standard benchmark problem for nonlinear system identification to prove their efficacy. First examples shows the comparison of different algorithms which proves that the proposed DEBP is having better identification capability in comparison to other. In example 2 good behavior of the identification method is tested on an one degree of freedom (1DOF) experimental aerodynamic test rig, a twin rotor multi-input-multi-output system (TRMS), finally it is applied to Box and Jenkins Gas furnace benchmark identification problem and its efficacy has been tested through correlation analysis. © 2011 Elsevier B.V.Item A non-invasive approach to investigation of ventricular blood pressure using cardiac sound features(IOP Publishing Ltd, 2017) Tang, H.; Zhang, J.; Chen, H.; Mondal, A.; Park, Y.Heart sounds (HSs) are produced by the interaction of the heart valves, great vessels, and heart wall with blood flow. Previous researchers have demonstrated that blood pressure can be predicted by exploring the features of cardiac sounds. These features include the amplitude of the HSs, the ratio of the amplitude, the systolic time interval, and the spectrum of the HSs. A single feature or combinations of several features have been used for prediction of blood pressure with moderate accuracy. Experiments were conducted with three beagles under various levels of blood pressure induced by different doses of epinephrine. The HSs, blood pressure in the left ventricle and electrocardiograph signals were simultaneously recorded. A total of 31 records (18 262 cardiac beats) were collected. In this paper, 91 features in various domains are extracted and their linear correlations with the measured blood pressures are examined. These features are divided into four groups and applied individually at the input of a neural network to predict the left ventricular blood pressure (LVBP). The analysis shows that non-spectral features can track changes of the LVBP with lower standard deviation. Consequently, the non-spectral feature set gives the best prediction accuracy. The average correlation coefficient between the measured and the predicted blood pressure is 0.92 and the mean absolute error is 6.86 mmHg, even when the systolic blood pressure varies in the large range from 90 mmHg to 282 mmHg. Hence, systolic blood pressure can be accurately predicted even when using fewer HS features. This technique can be used as an alternative to real-time blood pressure monitoring and it has promising applications in home health care environments. © 2017 Institute of Physics and Engineering in Medicine.Item Knowledge management influence on safety management practices evidence from construction industry(IGI Global cust@igi-global.com, 2019) Deepak, M.D.; Mahesh, G.; Medi, N.K.Many studies have been conducted in relation with knowledge management (KM), indicating the benefit associated with KM; among which safety management (SM) improvement is one of them. So, the aim of this article is to assess the influence of KM on SM practices in construction industry. In this regard, various factors that affect KM and SM are identified through literature review. Then, a questionnaire survey was facilitated to collect data based on the identified factors. These factors are ranked using a relative importance index (RII) to ascertain the level of importance among its group. Further, correlation analysis and multiple linear regression analysis are carried out to test and measure the strength of the relationship between KM and SM factors. Results indicate that there exists a definite and significant relationship between the factors of KM and SM in construction industry. Overall, the results obtained from the study will assist practitioners and professionals to develop and upgrade KM and SM practices in construction industry. © 2019, IGI Global.