Faculty Publications
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Item Key Variables Influencing the Performance of 3D Printed Concrete: A Comprehensive Analysis(Springer Science+Business Media, 2025) Barbhuiya, S.; Das, B.B.; Adak, D.This chapter examines key variables influencing 3D printed concrete performance, focusing on material, process, environmental, and geometric factors essential for achieving optimal strength and durability. It begins with an overview of 3D printed concrete, performance metrics, and the scope of the study. The chapter then delves into material composition, discussing how cement type, aggregate characteristics, additives, and water-cement ratios affect mix consistency, workability, and structural integrity. Process parameters, such as layer height, print speed, and extrusion rate, are analysed for their impact on layer adhesion and structural stability. Environmental factors—including temperature, humidity, and curing—are examined, highlighting their influence on setting time and strength. Geometric and structural considerations, like wall thickness and layer bonding, reveal the effects of design complexity on stability. The chapter concludes by synthesizing these findings, offering insights into optimizing 3D printed concrete performance through coordinated control of materials, process, and environmental conditions. © 2025 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.Item Experimental studies on the effects of corrosion on the flexural strength of RC beams(CAFET INNOVA Technical Society cafetinnova@gmail.com 1-2-18/103, Mohini Mansion, Gagan Mahal Road, Domalguda, Hyderabad 500029, 2014) Pandit, P.; Venkataramana, K.; BabuNarayan, K.S.; Parla, B.; Kimura, Y.RC structures are generally very durable and are capable of withstanding a variety of adverse environmental conditions. However, failures of these structures still occur and reinforcement corrosion is one of the major causes. In the present research, corroded Ordinary Portland Cement (OPC) beams were tested in the laboratory to evaluate their flexural behavior. Accelerated corrosion technique was adopted to corrode the beams. The corrosion was measured using Applied Corrosion Monitoring (ACM) instrument. From the results, it is seen that, as the rate of corrosion increases, the load carrying capacity decreases. The deflection increases initially and then decreases. It is observed that the stiffness of the beams is reduced when rate of corrosion is increased due to changes in the modulus of elasticity of corroded steel. © 2014 CAFET-INNOVA TECHNICAL SOCIETY.Item Bird classification based on their sound patterns(Springer New York LLC barbara.b.bertram@gsk.com, 2016) Raghuram, M.A.; Chavan, N.R.; Belur, R.; Koolagudi, S.G.In this paper we focus on automatic bird classification based on their sound patterns. This is useful in the field of ornithology for studying bird species and their behavior based on their sound. The proposed methodology may be used to conduct survey of birds. The proposed methods may be used to automatically classify birds using different audio processing and machine learning techniques on the basis of their chirping patterns. An effort has been made in this work to map characteristics of birds such as size, habitat, species and types of call, on to their sounds. This study is also part of a broader project that includes development of software and hardware systems to monitor the bird species that appear in different geographical locations which helps ornithologists to monitor environmental conditions with respect to specific bird species. © 2016, Springer Science+Business Media New York.Item Spectrogram Enhancement Using Multiple Window Savitzky-Golay (MWSG) Filter for Robust Bird Sound Detection(Institute of Electrical and Electronics Engineers Inc., 2017) Koluguri, N.R.; Nisha Meenakshi, G.N.; Ghosh, P.K.Bird sound detection from real-field recordings is essential for identifying bird species in bioacoustic monitoring. Variations in the recording devices, environmental conditions, and the presence of vocalizations from other animals make the bird sound detection very challenging. In order to overcome these challenges, we propose an unsupervised algorithm comprising two main stages. In the first stage, a spectrogram enhancement technique is proposed using a multiple window Savitzky-Golay (MWSG) filter. We show that the spectrogram estimate using MWSG filter is unbiased and has lower variance compared with its single window counterpart. It is known that bird sounds are highly structured in the time-frequency (T-F) plane. We exploit these cues of prominence of T-F activity in specific directions from the enhanced spectrogram, in the second stage of the proposed method, for bird sound detection. In this regard, we use a set of four moving average filters that when applied to the enhanced spectrogram, yield directional spectrograms that capture the direction specific information. We propose a thresholding scheme on the time varying energy profile computed from each of these directional spectrograms to obtain frame-level binary decisions of bird sound activity. These individual decisions are then combined to obtain the final decision. Experiments are performed with three different datasets, with varying recording and noise conditions. Frame level F-score is used as the evaluation metric for bird sound detection. We find that the proposed method, on average, achieves higher F-score (10.24% relative) compared to the best of the six baseline schemes considered in this work. © 2017 IEEE.Item A Hybrid Trust Management Scheme for Wireless Sensor Networks(Springer New York LLC barbara.b.bertram@gsk.com, 2017) Karthik, N.; Ananthanarayana, V.S.Wireless sensor network (WSN) consists of wireless small sensor nodes deployed in the terrain for continuous observation of physical or environmental conditions. The data collected from the WSN is used for making decisions. The condition for making critical decision is to assure the trustworthiness of the data generated from sensor nodes. However, the approaches for scoring the sensed data alone is not enough in WSN since there is an interdependency between node and data item. If the overall trust score of the network is based on one trust component, then the network might be misguided. In this work, we propose the hybrid approach to address the issue by assigning the trust score to data items and sensor nodes based on data quality and communication trust respectively. The proposed hybrid trust management scheme (HTMS) detects the data fault with the help of temporal and spatial correlations. The correlation metric and provenance data are used to score the sensed data. The data trust score is utilized for making decision. The communication trust and provenance data are used to evaluate the trust score of intermediate nodes and source node. If the data item is reliable enough to make critical decisions, a reward is given by means of adding trust score to the intermediate nodes and source node. A punishment is given by reducing the trust score of the source and intermediate nodes, if the data item is not reliable enough to make critical decisions. Result shows that the proposed HTMS detects the malicious, faulty, selfish node and untrustworthy data. © 2017, Springer Science+Business Media, LLC.Item Accurate parametrization and methodology for selection of pertinent single diode photovoltaic model with improved simulation efficiency(Elsevier Ltd, 2018) Gudimindla, H.; Sharma K, M.An accurate model of photovoltaic (PV) panel is indispensable for simulations studies. In general, the PV circuit parameters for simulation studies are extracted from the manufacturer's data sheet under different environmental conditions. The PV characterizing equations are nonlinear and requires a more involved computation. This paper presents a fast convergent third order Newton-type method to solve such nonlinear equations and thereby, to accurately parameterize any of the possible PV circuit models. The applicability and suitability of the proposed method are demonstrated through modeling of multi and mono-crystalline PV cells. Further an algorithm to evaluate the efficacy of the available methods and the proposed method is presented. PV characteristics of the suitable circuit model at various levels of temperature and irradiation are also examined. Finally, the effectiveness of the developed method is comprehensively assessed through comparison with the most recent and available effective techniques by considering various performance indices based on current-voltage, power-voltage curves and experimental data is carried out. © 2018 Elsevier LtdItem Evaluation of Superpave mixtures for perpetual asphalt pavements(Taylor and Francis Ltd. michael.wagreich@univie.ac.at, 2019) Priyanka, B.A.; Goutham, G.; Ravi Shankar, A.U.Early deterioration of flexible pavements, due to increased traffic volume, environmental conditions, poor maintenance and construction quality, causes difficulties to road users, all around the world. The structural failures such as fatigue and rutting demand the reconstruction of the pavements which further leads to significant construction cost. One potentially sustainable solution to this problem is to adopt perpetual pavement technology. The fatigue and rutting distresses in the pavements can be minimised to some extent by utilising Superpave mixtures with perpetual pavement concept. This paper proposes two polymer-modified Superpave mixtures, one with optimum amount of binder and the other with rich binder content, for the asphalt intermediate and base layers of perpetual pavement, respectively. The optimum mixtures were prepared with two aggregate gradations having nominal maximum aggregate sizes 25 mm and 19 mm for the intermediate layers to enhance the rutting resistance. Rich mixtures were prepared with the same gradations for the asphalt base layer to improve the fatigue resistance. Laboratory tests were conducted on these mixtures to determine moisture susceptibility, rutting resistance, fatigue behaviour and resilient modulus. The fatigue and rutting criteria of perpetual pavement sections were evaluated using KENPAVE software and the critical strains were found to be within the limits. The experimental results and analysis on perpetual pavement sections with proposed mixtures for the intermediate and base layers show that they can be considered as a better alternative for conventional pavements. © 2018, © 2018 Informa UK Limited, trading as Taylor & Francis Group.Item Acetaminophen micropollutant: Historical and current occurrences, toxicity, removal strategies and transformation pathways in different environments(Elsevier Ltd, 2019) Vo, H.N.; Le, G.K.; Nguyen, T.M.; Bui, X.-T.; Nguyen, K.H.; Rene, E.R.; Vo, T.D.H.; Cao Ngoc, N.-D.; Mohan, R.Acetaminophen (ACT) is commonly used as a counter painkiller and nowadays, it is increasingly present in the natural water environment. Although its concentrations are usually at the ppt to ppm levels, ACT can transform into various intermediates depending on the environmental conditions. Due to the complexity of the ACT degradation products and the intermediates, it poses a major challenge for monitoring, detection and to propose adequate treatment technologies. The main objectives of this review study were to assess (i) the occurrences and toxicities, (2) the removal technologies and (3) the transformation pathways and intermediates of ACT in four environmental compartments namely wastewater, surface water, ground water, and soil/sediments. Based on the review, it was observed that the ACT concentrations in wastewater can reach up to several hundreds of ppb. Amongst the different countries, China and the USA showed the highest ACT concentration in wastewater (?300 ?g/L), with a very high detection frequency (81–100%). Concerning surface water, the ACT concentrations were found to be at the ppt level. Some regions in France, Spain, Germany, Korea, USA, and UK comply with the recommended ACT concentration for drinking water (71 ng/L). Notably, ACT can transform and degrade into various metabolites such as aromatic derivatives or organic acids. Some of them (e.g., hydroquinone and benzoquinone) are toxic to human and other life forms. Thus, in water and wastewater treatment plants, tertiary treatment systems such as advanced oxidation, membrane separation, and hybrid processes should be used to remove the toxic metabolites of ACT. © 2019 Elsevier LtdItem Development of energy efficient organic bricks in construction using IOT and perlite(Taylor and Francis Ltd., 2021) Shubhananda Rao, P.; Ram Chandar, K.R.The study focuses on improvement of bricks in mechanical properties, reduction of energy consumption, making more economical and environmentally friendly by saving the depleting resources. The bricks were mixed in different proportions, by replacing sand with Iron Ore Tailings from 30 to 60 percent at 10 percent interval, cement from 10 to 20 percent at 5 percent interval and perlite at 2 and 5 percent to make bricks of 230 mm×112.5 mm×75 mm dimensions. The bricks were tested for compressive strength, water absorption and thermal conductivity. From these tests among different combinations, IOTs:Sand:Cement:Perlite 50:25:20:5 combinations have yielded better results by satisfying Indian Standard (IS) codes and this is taken as optimum dosage of raw materials. Model rooms are constructed using these bricks to access the effectiveness of thermal conductivity to compare with the ordinary conventional brick (fired brick) room, both rooms are of the same dimension and exposed to same environmental conditions. Thermal conductivity is assessed by measuring the temperature on walls of all sides of the room at different timings of the day. The results revealed that heat transferred from the outside to inside of the walls of the model room constructed with IOT-perlite bricks was at least 2°C less compared with that of ordinary bricks. Lower thermal conductivity leads to energy savings and results established 8 percent of energy savings with IOT-perlite bricks. The study proved the eco-friendly bricks by using the mine waste, lower thermal conductivity, good strength and light weight in structure. © 2020 Informa UK Limited, trading as Taylor & Francis Group.Item Advancing solar PV panel power prediction: A comparative machine learning approach in fluctuating environmental conditions(Elsevier Ltd, 2024) Tripathi, A.K.; Mangalpady, M.; Elumalai, P.V.; Karthik, K.; Khan, S.A.; Asif, M.; Koppula, K.S.Solar photovoltaic (PV) panels play a crucial role in sustainable energy generation, yet their power output often faces uncertainties due to dynamic weather conditions. In this study, a comparative machine learning approach is introduced, utilizing multivariate regression (MR), support vector machine regression (SVMR), and Gaussian regression (GR) techniques for precise solar PV panel power prediction. The investigation into the impact of environmental factors—solar radiation, ambient temperature, and relative humidity—on PV panel output reveals the superior predictive capabilities of SVMR models. With a mean squared error (MSE) of 0.038, a mean absolute error (MAE) of 0.17, and an R2 value of 0.99, SVMR outperforms GR and MR models. Conversely, Gaussian regression demonstrates comparatively weaker performance, yielding an R2 of 0.88, an MSE of 0.49, and an MAE of 0.63. This research underscores the reliability and enhanced accuracy of the proposed SVMR model in forecasting solar PV panel output. The outcomes presented herein carry significant implications for promoting the widespread adoption of PV panels in electricity generation, particularly in challenging environmental conditions. The findings offer valuable insights into optimizing solar PV deployment, ultimately contributing to the expansion of solar power generation in the national energy landscape. Moreover, the comparative analysis provides insights into how anticipated PV power generation can adapt to varying weather conditions, encompassing factors such as temperature, humidity, and solar radiation. © 2024 The Authors