Journal Articles
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/19884
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Item Geotechnical characteristics of lithomargic clay blended with marine clay as landfill liner material(CAFET INNOVA Technical Society cafetinnova@gmail.com 1-2-18/103, Mohini Mansion, Gagan Mahal Road, Domalguda, Hyderabad 500029, 2012) Allamaprabhu, K.; Sunil, B.M.; Nayak, S.; Fernandes, S.; Zafar, M.This paper reports a series of laboratory tests conducted on lithomargic clay (shedi soil), which is widespread over part of southwest coast of India, to assess whether it could be used as compacted clay liner for hydraulic barriers in engineered landfill. In order to assess the suitability of lithomarge as a barrier material, following tests such as index properties, compaction characteristics, hydraulic conductivity and unconfined compressive strength of the soil were conducted in the laboratory. From the studies, it is found that lithomargic soil is near to the recommended specifications for soils to be used as liner material. Suitable materials for soil liners are then obtained by blending different types of locally available soils to achieve the required low hydraulic conductivity and good strength. To achieve specifications for the liner material lithomargic clay is blended with 15% and 20% marine clay by weight of lithomargic clay. From standard compaction control, the blended soil shows hydraulic conductivity lower than the 1x10-7 cm/s. Acceptable zones (AZ) are constructed on the compaction plane to meet design objectives for hydraulic conductivity. It's strength properties show that the soil possesses higher strength than the recommended minimum strength of 200kPa, to support the overburden pressure imposed by the waste body. From the laboratory test results, it can be concluded that lithomargic clay blended with marine clay satisfies the requirements for a good soil liner material. © 2012 CAFET-INNOVA TECHNICAL SOCIETY.Item Artificial neural network based modeling to evaluate methane yield from biogas in a laboratory-scale anaerobic bioreactor(Elsevier Ltd, 2016) Nair, V.V.; Dhar, H.; Kumar, S.; Thalla, A.K.; Mukherjee, S.; Wong, J.W.C.The performance of a laboratory-scale anaerobic bioreactor was investigated in the present study to determine methane (CH4) content in biogas yield from digestion of organic fraction of municipal solid waste (OFMSW). OFMSW consists of food waste, vegetable waste and yard trimming. An organic loading between 40 and 120 kg VS/m3 was applied in different runs of the bioreactor. The study was aimed to focus on the effects of various factors, such as pH, moisture content (MC), total volatile solids (TVS), volatile fatty acids (VFAs), and CH4 fraction on biogas production. OFMSW witnessed high CH4 yield as 346.65 L CH4/kg VS added. A target of 60–70% of CH4 fraction in biogas was set as an optimized condition. The experimental results were statistically optimized by application of ANN model using free forward back propagation in MATLAB environment. © 2016 Elsevier LtdItem Identification and characterization of hydrothermally altered minerals using surface and space-based reflectance spectroscopy, in parts of south-eastern Rajasthan, India(Springer Nature, 2020) Chattoraj, S.L.; Sharma, R.U.; Kumar, C.; Champati Ray, P.K.; Sengar, V.Imaging spectroscopy has evolved as one of the most significant advancements due to contiguous spectral coverage and higher spectral resolution which enable mineral identification and mineral exploration. Many phyllosilicate and carbonate minerals show specific spectral absorption feature in the wavelength range of visible-to-near-infra-red region of electromagnetic spectrum. These spectral features enable delineation of different mineral assemblages which in turn help in mineral prospecting using hyperspectral imaging spectra. The present study is focussed on evaluation and application of EO-1 Hyperion (hyperspectral) data as an Earth Observation tool for mineral detection and mapping in parts of Udaipur district in south-eastern Rajasthan. Hyperion reflectance imagery of this area was analysed using spectral angle mapper after pre-processing, atmospheric correction and geometric correction. Five endmembers, viz. dolomite, montmorillonite, chlorite, phlogopite and serpentine, were derived from both atmospherically corrected image and from rock samples in the laboratory using ASD field spectroradiometer covering spectral range of 0.4–2.5 µm. The reflectance spectra of endmembers derived from satellite image were initially compared with USGS mineral spectral library, and then after comparing with laboratory-based spectra with respect to absorption features, target minerals were identified which shows more than 70% match with the USGS and laboratory spectra. These minerals were also cross-checked with the reported litho-sequence of the area. Minerals derived from laboratory and image spectra are indicative of hydrothermally altered outer thermal aureole which is also corroborated by litho-structural association of the area. © 2020, Springer Nature Switzerland AG.Item Multimodal behavior analysis in computer-enabled laboratories using nonverbal cues(Springer Science and Business Media Deutschland GmbH info@springer-sbm.com, 2020) Banerjee, S.; Ashwin, T.S.; Guddeti, R.M.R.In the modern era, there is a growing need for surveillance to ensure the safety and security of the people. Real-time object detection is crucial for many applications such as traffic monitoring, security, search and rescue, vehicle counting, and classroom monitoring. Computer-enabled laboratories are generally equipped with video surveillance cameras in the smart campus. But, from the existing literature, it is observed that the use of video surveillance data obtained from smart campus for any unobtrusive behavioral analysis is seldom performed. Though there are several works on the students’ and teachers’ behavior recognition from devices such as Kinect and handy cameras, there exists no such work which extracts the video surveillance data and predicts the behavioral patterns of both the students and the teachers in real time. Hence, in this study, we unobtrusively analyze the students’ and teachers’ behavioral patterns inside a teaching laboratory (which is considered as an indoor scenario of a smart campus). Here, we propose a deep convolution network architecture to classify and recognize an object in the indoor scenario, i.e., the teaching laboratory environment of the smart campus with modified Single-Shot MultiBox Detector approach. We used six different class labels for predicting the behavioral patterns of both the students and the teachers. We created our dataset with six different class labels for training deep learning architecture. The performance evaluation demonstrates that the proposed method performs better with an accuracy of 0.765 for classification and localization. © 2020, Springer-Verlag London Ltd., part of Springer Nature.Item Rock strength characterization using measurement while drilling technique(Springer, 2020) Lakshminarayana, C.R.; Tripathi, A.K.; Pal, S.K.The approximation of strength properties of rocks most often requires during the preliminary phase of any engineering projects related to rock mechanics. The main disadvantage of evaluating the rock properties in a testing laboratory is the prerequisite for high-quality rock core with many numbers. In this empirical method, the essential strength properties of rocks would measure during the rock drilling process using some identified machine variables along with the acoustic parameter. The machine operating variables such as thrust and torque and acoustic vibration parameter collecting at the machine head were used to develop rock strength models. A drill-type dynamometer was employed to gauge the machine variables and the NI-9234 data acquisition system for gauging the vibration parameter. The evaluation of the mathematical models for their efficiency shows that the applied empirical approach could determine the strength properties with fewer errors and can use as an alternative method for measuring the compressive and tensile strength of sedimentary rocks in the laboratory without using core samples. © 2020, Indian Geotechnical Society.Item Surveillance video analysis for student action recognition and localization inside computer laboratories of a smart campus(Springer, 2021) Rashmi, M.; Ashwin, T.S.; Guddeti, G.R.M.In the era of smart campus, unobtrusive methods for students’ monitoring is a challenging task. The monitoring system must have the ability to recognize and detect the actions performed by the students. Recently many deep neural network based approaches have been proposed to automate Human Action Recognition (HAR) in different domains, but these are not explored in learning environments. HAR can be used in classrooms, laboratories, and libraries to make the teaching-learning process more effective. To make the learning process more effective in computer laboratories, in this study, we proposed a system for recognition and localization of student actions from still images extracted from (Closed Circuit Television) CCTV videos. The proposed method uses (You Only Look Once) YOLOv3, state-of-the-art real-time object detection technology, for localization, recognition of students’ actions. Further, the image template matching method is used to decrease the number of image frames and thus processing the video quickly. As actions performed by the humans are domain specific and since no standard dataset is available for students’ action recognition in smart computer laboratories, thus we created the STUDENT ACTION dataset using the image frames obtained from the CCTV cameras placed in the computer laboratory of a university campus. The proposed method recognizes various actions performed by students in different locations within an image frame. It shows excellent performance in identifying the actions with more samples compared to actions with fewer samples. © 2020, Springer Science+Business Media, LLC, part of Springer Nature.Item Functions and performance of sensors for slope monitoring in opencast coal mines–laboratory experimentation(Taylor and Francis Ltd., 2024) Mittapally, S.K.; Ram Chandar, R.C.Slope monitoring has become mandatory in opencast mines. Despite various slope monitoring systems, Wireless Sensor Network is faster and more efficient. It is essential to test the sensors before installation in the field to assess their effectiveness and suitability for a particular slope condition. The significant factors affecting the slope’s stability are soil moisture and vibration intensity. This study aims to analyze the functions and performance of sensors used in the advanced real-time wireless slope monitoring system. The sensors were validated by comparing the results from the lab experiment with existing methodologies like the Gravimetric method and Geophone method, then installed in a slope model of clay built on a laboratory scale to analyze their performance. The results are drawn and discussed in conclusion. © 2023 Taylor & Francis Group, LLC.Item Prediction of the specific energy requirement of hydraulic rock breaker based on laboratory impact hammer – a case study(Inderscience Publishers, 2024) Pal, S.K.; Akhil, A.; Vyas, A.; Tripathi, A.K.The present study was conducted in a limestone mine at central Rajasthan, India for correlating the impact energy of hydraulic rock breaker with laboratory size gravitational impact rock breaker. Data generation and results were obtained mainly to correlate the specific impact energy required by the hydraulic rock breaker field (IEF) in kJ/m3 for breaking boulders in the field versus the specific impact energy required by the gravitational impact rock breaker laboratory (IEL) in kJ/m3. Laboratory investigation values for Schmidt rebound number (RN) is between 27 to 44; UCS (σc) between 96.27 to 132.25 MPa; tensile strength (σt) between 9.14 to 14.66 MPa; and point load strength (Is50) between 8.95 to 12.65 MPa. In the present research, an attempt is made to study impact energy utilised by hydraulic rock breaker in the field and comparison of specific energy requirements based on a laboratory-size impact hammer. © 2024 Inderscience Enterprises Ltd.