Faculty Publications
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Publications by NITK Faculty
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Item Evaluation of properties of porous friction course mixes for different gyration levels(2009) Suresha, S.N.; George, V.; Shankar, A.U.R.Porous frictions courses (PFCs) are characterized by high percent air voids content, and are widely used as pavement surface drainage layers. This paper presents details on the laboratory investigation performed on evaluation of properties of PFC mixes using the Superpave gyratory compactor. It also, provides a brief review of the latest specifications related to standard practices for mix design and the uses of these mixes adopted by various agencies. Major differences were observed in the design gyrations (Ndesign) and the design aggregate gradations. In this study, six gradations (G) were investigated with binder contents (BCs) ranging between 4.0 and 5.0% by mass of the total mix, for various gyration levels (N). The effect of N, G, and BC on the volumetric properties, unaged abrasion loss, permeability, and the permanent deformation characteristics of PFC mixes were investigated. The experimental results were statistically analyzed to identify the major influencing factors and their significance. © 2009 ASCE.Item Influence of planetary ball milling parameters on the mechano-chemical activation of fly ash(Elsevier, 2015) Patil, A.G.; Anandhan, S.This study illustrates the design of statistical analysis by Taguchi methodology to obtain nanostructured fly ash by planetary ball milling. An orthogonal array and analysis of variance were employed to analyze the effect of milling parameters. A class-F fly ash was subjected to planetary ball milling induced mechano-chemical activation aided by a surfactant. Ball milling parameters, such as ball-to-powder weight ratio, type and quantity of surfactant and type of medium were varied as guided by the Taguchi design. The nanostructured fly ash was characterized by dynamic light scattering, BET surface area analysis, X-ray diffraction, FTIR spectroscopy, scanning electron microscopy, field emission scanning electron microscopy and transmission electron microscopy. The ball-to-powder weight ratio and the surfactant type are the major influencing factors on lower crystallite size and average particle size and higher specific surface area. The surface modification of fly ash was confirmed by FTIR spectroscopy. The nano fly ash produced by this method has a wide application potential in polymer industries as reinforcement in composites. © 2015 Elsevier B.V.Item Prediction model for peninsular Indian summer monsoon rainfall using data mining and statistical approaches(Elsevier Ltd, 2017) Vathsala, H.; Koolagudi, S.G.In this paper we discuss a data mining application for predicting peninsular Indian summer monsoon rainfall, and propose an algorithm that combine data mining and statistical techniques. We select likely predictors based on association rules that have the highest confidence levels. We then cluster the selected predictors to reduce their dimensions and use cluster membership values for classification. We derive the predictors from local conditions in southern India, including mean sea level pressure, wind speed, and maximum and minimum temperatures. The global condition variables include southern oscillation and Indian Ocean dipole conditions. The algorithm predicts rainfall in five categories: Flood, Excess, Normal, Deficit and Drought. We use closed itemset mining, cluster membership calculations and a multilayer perceptron function in the algorithm to predict monsoon rainfall in peninsular India. Using Indian Institute of Tropical Meteorology data, we found the prediction accuracy of our proposed approach to be exceptionally good. © 2016 Elsevier LtdItem Metabolomic profiling of doxycycline treatment in chronic obstructive pulmonary disease(Elsevier B.V., 2017) Singh, B.; Jana, S.K.; Ghosh, N.; Das, S.K.; Joshi, M.; Bhattacharyya, P.; Chaudhury, K.Serum metabolic profiling can identify the metabolites responsible for discrimination between doxycycline treated and untreated chronic obstructive pulmonary disease (COPD) and explain the possible effect of doxycycline in improving the disease conditions. 1H nuclear magnetic resonance (NMR)-based metabolomics was used to obtain serum metabolic profiles of 60 add-on doxycycline treated COPD patients and 40 patients receiving standard therapy. The acquired data were analyzed using multivariate principal component analysis (PCA), partial least-squares-discriminant analysis (PLS-DA), and orthogonal projection to latent structure with discriminant analysis (OPLS-DA). A clear metabolic differentiation was apparent between the pre and post doxycycline treated group. The distinguishing metabolites lactate and fatty acids were significantly down-regulated and formate, citrate, imidazole and L-arginine upregulated. Lactate and folate are further validated biochemically. Metabolic changes, such as decreased lactate level, inhibited arginase activity and lowered fatty acid level observed in COPD patients in response to add-on doxycycline treatment, reflect the anti-inflammatory action of the drug. Doxycycline as a possible therapeutic option for COPD seems promising. © 2016 Elsevier B.V.Item Can coffee certification schemes increase incomes of smallholder farmers? Evidence from Jinotega, Nicaragua(Springer Netherlands, 2017) Jena, P.R.; Stellmacher, T.; Grote, U.This paper investigates the impact of Fairtrade and organic certification on household income of smallholder coffee farmers in the Jinotega Municipality of Nicaragua. Using a sample of 233 coffee farming households and employing endogenous switching regression model and propensity score matching method, the results found that Fairtrade and organic certification standards have different effects on the certified farmers; while Fairtrade farmers had experienced yield gains, organic farmers had the price advantage. However, the overall impact of these certification standards on the total household income is found to be statistically not significant. While some of the Fairtrade-certified cooperatives have used the social premium in creating community-level infrastructure, there is a need for more investment. The major constraint the organic-certified farmers face is lack of availability of adequate organic inputs such as manures and organic herbicides. © 2015, Springer Science+Business Media Dordrecht.Item Long-range prediction of Indian summer monsoon rainfall using data mining and statistical approaches(Springer-Verlag Wien michaela.bolli@springer.at, 2017) Vathsala, H.; Koolagudi, S.G.This paper presents a hybrid model to better predict Indian summer monsoon rainfall. The algorithm considers suitable techniques for processing dense datasets. The proposed three-step algorithm comprises closed itemset generation-based association rule mining for feature selection, cluster membership for dimensionality reduction, and simple logistic function for prediction. The application of predicting rainfall into flood, excess, normal, deficit, and drought based on 36 predictors consisting of land and ocean variables is presented. Results show good accuracy in the considered study period of 37years (1969–2005). © 2016, Springer-Verlag Wien.Item An over-limit risk assessment of PV integrated power system using probabilistic load flow based on multi-time instant uncertainty modeling(Elsevier Ltd, 2018) Prusty, B.R.; Jena, D.In this paper, the risk assessment of a PV integrated power system is accomplished by computing the over-limit probabilities and the severities of events such as under-voltage, over-voltage, over-load, and thermal over-load. These aspects are computed by performing temperature-augmented probabilistic load flow (TPLF) using Monte Carlo simulation. For TPLF, the historical data for PV generation, ambient temperature, and load power, each collected at twelve specific time instants of a day for the past five years are pre-processed by using three linear regression models for accurate uncertainty modeling. For PV generation data, the developed model is capable of filtering out the annual predictable periodic variation (owing to positioning of the Sun) and decreasing production trend due to ageing effect whereas, for ambient temperature and load power, the corresponding models accurately remove the annual cyclic variations in the data and their growth. The simulations pertaining to the aforesaid risk assessment are performed on a PV integrated New England 39-bus test system. The system over-limit risk indices are calculated for different PV penetrations and input correlations. In addition, the changes in the values of TPLF model parameters on the statistics of the result variables are analyzed. The risk indices so obtained help in executing necessary steps to reduce system risks for reliable operation. © 2017 Elsevier LtdItem Multispectral satellite image denoising via adaptive cuckoo search-based wiener filter(Institute of Electrical and Electronics Engineers Inc., 2018) Suresh, S.; Lal, S.; Chen, C.; Çelik, T.Satellite image denoising is essential for enhancing the visual quality of images and for facilitating further image processing and analysis tasks. Designing of self-tunable 2-D finite-impulse response (FIR) filters attracted researchers to explore its usefulness in various domains. Furthermore, 2-D FIR Wiener filters which estimate the desired signal using its statistical parameters became a standard method employed for signal restoration applications. In this paper, we propose a 2-D FIR Wiener filter driven by the adaptive cuckoo search (ACS) algorithm for denoising multispectral satellite images contaminated with the Gaussian noise of different variance levels. The ACS algorithm is proposed to optimize the Wiener weights for obtaining the best possible estimate of the desired uncorrupted image. Quantitative and qualitative comparisons are conducted with 10 recent denoising algorithms prominently used in the remote-sensing domain to substantiate the performance and computational capability of the proposed ACSWF. The tested data set included satellite images procured from various sources, such as Satpalda Geospatial Services, Satellite Imaging Corporation, and National Aeronautics and Space Administration. The stability analysis and study of convergence characteristics are also performed, which revealed the possibility of extending the ACSWF for real-time applications as well. © 1980-2012 IEEE.Item Dew Point temperature estimation: Application of artificial intelligence model integrated with nature-inspired optimization algorithms(MDPI AG indexing@mdpi.com Postfach Basel CH-4005, 2019) Naganna, S.R.; Deka, P.C.; Ghorbani, M.A.; Biazar, S.M.; Al-Ansari, N.; Yaseen, Z.M.Dew point temperature (DPT) is known to fluctuate in space and time regardless of the climatic zone considered. The accurate estimation of the DPT is highly significant for various applications of hydro and agro-climatological researches. The current research investigated the hybridization of a multilayer perceptron (MLP) neural network with nature-inspired optimization algorithms (i.e., gravitational search (GSA) and firefly (FFA)) to model the DPT of two climatically contrasted (humid and semi-arid) regions in India. Daily time scale measured weather information, such as wet bulb temperature (WBT), vapor pressure (VP), relative humidity (RH), and dew point temperature, was used to build the proposed predictive models. The efficiencies of the proposed hybrid MLP networks (MLP-FFA and MLP-GSA) were authenticated against standard MLP tuned by a Levenberg-Marquardt back-propagation algorithm, extreme learning machine (ELM), and support vector machine (SVM) models. Statistical evaluation metrics such as Nash Sutcliffe efficiency (NSE), root mean square error (RMSE), and mean absolute error (MAE) were used to validate the model efficiency. The proposed hybrid MLP models exhibited excellent estimation accuracy. The hybridization of MLP with nature-inspired optimization algorithms boosted the estimation accuracy that is clearly owing to the tuning robustness. In general, the applied methodology showed very convincing results for both inspected climate zones. © 2019 by the authors.Item Experimental and Statistical Evaluations of Strength Properties of Concrete with Iron Ore Tailings as Fine Aggregate(American Society of Civil Engineers (ASCE) onlinejls@asce.org 1801 Alexander Bell DriveGEO Reston VA 20191 Alabama, 2020) Gayana, G.B.; Ram Chandar, R.C.Iron ore tailings (IOT) are the by-products of iron ore beneficiation, and these tailings are disposed of in several tons annually in quarries, landfills, and tailings dams, causing environmental issues. Various researchers have attempted to study the properties of IOT and the use of them in concrete as a building material. The present research aims to investigate the potential use of alccofine, a microfine particle of slag, as a cement replacement and IOT as fine aggregates in concrete. Experimental results indicated that the concrete workability decreased with an increase in the IOT-alccofine content and the maximum compressive strength (CS) obtained was 70.00, 68.67, and 65 MPa respectively at 40%, 30%, and 20% IOT-alccofine dosage for varying water-to-cement (w/c) ratios of 0.35, 0.40, and 0.45 respectively. Similarly, the flexural strength (FS) and splitting tensile strength (STS) increased with an increase in IOT-alccofine content. A statistically fitted multiple regression analysis was performed for all the mechanical properties to evaluate the significant level of concrete containing alccofine and IOT in concrete. These prediction models have high accuracy and low bias and the validation process represented that the equations can perform excellently in predicting the IOT-alccofine concrete properties. © 2019 American Society of Civil Engineers.