Faculty Publications
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Item Evaluation of suitability of garnetiferous biotite gneiss for M-sand production - A case study(CAFET INNOVA Technical Society cafetinnova@gmail.com 1-2-18/103, Mohini Mansion, Gagan Mahal Road, Domalguda, Hyderabad 500029, 2014) Anand, R.S.; Venkat Reddy, D.V.Natural sand are weathered and worn out particles of rocks and are of various grades or size depending on the accounting of wearing. The main natural and cheapest resource of sand is river. River sands are mined from river beds and sand mining has disastrous environmental consequences. Rivers in the southwest coast of India are under immense pressure due to various kinds of human activities among which indiscriminate extraction of construction grade sand is the most disastrous one. The situation is rather alarming in the rivers of Kerala. Indiscriminate of sand has depleted the natural resource and ravaged the rivers of the State. Since sand mining from river caused a lot of environmental issues, the Government has banned mining of the same. Thus, river sand is becoming a scarce commodity and hence exploring alternatives to it has become imminent. The artificial sand (M-sand) produced by proper machines can be a better substitute to river sand. Rock crushed to the required grain size distribution is termed as Manufactured sand (M-Sand). The most common rock in the quarries of Trivandrum is Garnetiferous Biotite gneiss (GBG), followed by Charnockite, Leptynite etc. In present investigation, suitability of the available GBG in Trivandrum area, to be used for m-sand production is verified. For this, a case study was done at Cheriyakonni quarry. The rocks were collected from ‘Metarock Pvt. Ltd.’ m-sand manufacturing plant which collects rock Cheriyakonni quarry, which is rich in GBG. The result of the study gives the best size and best proportion of GBG for m-sand production. © 2014 CAFET-INNOVA TECHNICAL SOCIETY.Item A geological and geotechnical investigation of some rocks in Trivandrum area, Kerala, India(CAFET INNOVA Technical Society 1-2-18/103, Mohini Mansion, Gagan Mahal Road, Domalguda, Hyderabad 500029, 2015) Anand, R.S.; Venkat Reddy, D.In the Indian stone industry, possessing huge reserves of about 1,690 million cum dimensional stone deposits, geological and geotechnical parameters of commercial rock deposit plays a significant role in the economic exploitation of quality commercial rock deposits. The success of the commercial stone industry solely depends upon the availability of large reserves of defect free raw materials. An attempt is made here to study the geological and geotechnical properties of different rocks from Trivandrum area, Kerala. A variety of rock samples from different parts of Trivandrum are subjected to study petrographic, physical and strength parameters. Geological studies reveals that inherent geological discontinuities in rock deposits like multiple joints, weathering, foliations and variations in color of the rocks etc. lead to huge wastage of resources, because such rocks are not suitable for any engineering purpose. The geotechnical studies conducted gives an idea about the physical and strength properties of the rocks. The result of the study gives the rock which has maximum favorable properties for use in construction engineering applications, out of the available ones in the study area. © 2015 CAFET-INNOVA TECHNICAL SOCIETY. All rights reserved.Item 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 A study on electroactive PVDF/mica nanosheet composites with an enhanced ?-phase for capacitive and piezoelectric force sensing(Royal Society of Chemistry, 2021) Khalifa, M.; Schoeffmann, E.; Lammer, H.; Mahendran, A.R.; Wuzella, G.; Anandhan, S.Herein, a multifunctional poly(vinylidene fluoride) (PVDF)/mica nanosheet composite (PMNC) thin film was developed for preparing a capacitive and piezoelectric force sensor. A high electroactive ?-phase content (89%) of PVDF was achieved through a facile rapid cooling process of PMNC films. The crystallinity of PVDF decreased upon the addition of mica nanosheets, while the dielectric constant increased significantly (?300%). The capacitance-based PMNC pressure sensor was found to be sensitive to the applied pressure. On the other hand, piezoelectric voltages of 18 V (single layer) and 32 V (multi-layer) were generated for PMNCs loaded with 1% mica nanosheets. Furthermore, a PMNC based nanogenerator generated a power density of 8.8 ?W cm?2and showed excellent durability (>60?000 cycles). High flexibility, lightweight and skin-friendly PMNCs could be a potential material in applications such as energy harvesting, energy storage, actuators, and self-powered and smart wearable electronic devices. © The Royal Society of Chemistry 2021.Item A Fully-Automated Framework for Mineral Identification on Martian Surface Using Supervised Learning Models(Institute of Electrical and Electronics Engineers Inc., 2023) Kumari, P.; Soor, S.; Shetty, A.; Koolagudi, S.G.The availability of various spectral libraries for CRISM (Compact Reconnaissance Imaging Spectrometer for Mars) data on NASA PDS (Planetary Data System) hugely facilitated the research on the surface mineralogy of Mars, however, building supervised learning models for mineral mapping appears to be challenging due to the lack of ground-truth/training data. In this paper, an automated framework is presented that classifies the spectra in a CRISM hyperspectral image using supervised learning models, where the required training data is produced by augmenting the mineral spectra available in the MICA (Minerals Identified in CRISM Analysis) spectral library, that keeps the key absorption signatures in the mineral spectra intact while providing adequate variability. The framework contains a pre-processing pipeline that in addition to some conventional pre-processing steps includes a new feature extraction method to capture the information of the most distinguishable absorption patterns in the spectra. The proposed framework is validated on a set of CRISM images captured from different locations on the Martian surface by using different types of supervised learning models, like random forests, support vector machines, and neural networks. An uncertainty analysis of the different steps involved in the pre-processing pipeline is provided, as well as a comparison of performances with some of the previously used methods for this purpose, which shows this framework works comparably well with a mean accuracy of around 0.8. Interactive mineral maps are also provided for the detected dominant minerals. © 2013 IEEE.Item MICAnet: A Deep Convolutional Neural Network for mineral identification on Martian surface(Elsevier B.V., 2024) Kumari, P.; Soor, S.; Shetty, A.; Koolagudi, S.G.Mineral identification plays a vital role in understanding the diversity and past habitability of the Martian surface. Mineral mapping by the traditional manual method is time-consuming and the unavailability of ground truth data limited the research on building supervised learning models. To address this issue an augmentation process is already proposed in the literature that generates training data replicating the spectra in the MICA (Minerals Identified in CRISM Analysis) spectral library while preserving absorption signatures and introducing variability. This study introduces MICAnet, a specialized Deep Convolutional Neural Network (DCNN) architecture for mineral identification using the CRISM (Compact Reconnaissance Imaging Spectrometer for Mars) hyperspectral data. MICAnet is inspired by the Inception-v3 and InceptionResNet-v1 architectures, but it is tailored with 1-dimensional convolutions for processing the spectra at the pixel level of a hyperspectral image. To the best of the authors’ knowledge, this is the first DCNN architecture solely dedicated to mineral identification on the Martian surface. The model is evaluated by its matching with a TRDR (Targeted Reduced Data Record) dataset obtained using a hierarchical Bayesian model. The results demonstrate an impressive f-score of at least .77 among different mineral groups in the MICA library, which is on par with or better than the unsupervised models previously applied to this objective. © 2024