Gobinath R
@sru.edu.in
Professor and Head-Department of Civil Engineering
SR University, Telangana, India
is presently serving as Professor and Head of the Department, Civil Engineering of SR University, Telangana, India and also a member of Center for Construction Materials and Methods division. He completed his PhD in the field of Environmental Geotechnology and Disaster Management from Center for Disaster Management and Mitigation, VIT, Vellore, M.E in Environmental Management from College of Engineering, Guindy, Anna University and BE in Civil engineering from Bharathiar University. He had handled several under graduate course in Civil and Environmental engineering including Project management, smart materials and structures, Geosynthetics, Engineering Mechanics etc and also presently handling elective courses related to Disaster Management and Intellectual Property rights in various Universities.
His research interests includes Sustainable materials, Construction Materials, Landslide mitigation and Management, Soil Bioengineering, Engineering Education, Geotechnology, Environmental Geotechnology, Sustainable Development, Sustainable construction technologies, he had handled several projects related to this domains in various levels. He also had executed good number of consultancy projects related to Geotechnical and environmental engineering for various organisations.
EDUCATION
B.E- Civil Engineering (2001), Bharathiar University
M.E- Environmental Management (2007), College of Engineering, Guindy, Anna University
PhD ( Environmental Geotechnology), VIT University, Vellore, India
RESEARCH INTERESTS
Sustainable materials, Construction Materials, Landslide mitigation and Management, Soil Bioengineering, Engineering Education, Geotechnology, Environmental Geotechnology, Sustainable Development, Sustainable construction technologies, Image processing, Machine Learning applications in Civil Enginee
FUTURE PROJECTS
Sustainable Construction material development
This project work involves obtaining sustainable construction materials without depleting the natural resources and by using non convetional material development techniques. It involves lot of analysis, simulation based works and the work is initiated under the aegies of Center for Construction Methdos and Materials of SR University
Applications Invited
Research Scholars and collaborators
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Bh Revathi, R. Gobinath, G Sri Bala, T Vamsi Nagaraju, and Sridevi Bonthu
Elsevier BV
T. Vamsi Nagaraju, B.M. Sunil, Babloo Chaudhary, R. Gobinath, and G. Sri Bala
Elsevier BV
Preethi Vijayarengan, Sri Chandana Panchangam, Ananth Stephen, Gokulanandhan Bernatsha, Gokul Krishnan Murali, Subramanyam Sarma Loka, Sathish Kumar Manoharan, Venkatramu Vemula, Rama Rao Karri, and Gobinath Ravindran
Springer Science and Business Media LLC
Pingili Vydehi, Gobinath Ravindran, G. Shyamala, Sri Bala G, Vamsi Nagaraju T, Mallaiah Mekala, and Rama Rao Karri
Elsevier BV
R Gobinath, G.P. Ganapathy, E. Gayathiri, Mehmet Serkan Kırgız, Nihan Naiboğlu, André Gustavo de Sousa Galdino, and Jamal Khatib
CRC Press
E. Gayathiri, R Gobinath, J. Jayanthij, Paniswamy Prakash, and M.G. Ragunathan
CRC Press
R Gobinath, Gayathiri Ekambaram, Paniswamy Prakash, Kumaravel Priya, and Venkata SSR Marella
CRC Press
Thotakura Vamsi Nagaraju, Sunil B. Malegole, Babloo Chaudhary, Gobinath Ravindran, Phanindra Chitturi, and Durga Prasad Chinta
Springer Science and Business Media LLC
S. Padmakala and Gobinath Ravindran
Springer Nature Singapore
Kumar Shubham, Subhadeep Metya, Abdhesh Kumar Sinha, and Ravindran Gobinath
Wiley
This paper presents a thorough reliability assessment of cavity foundation systems involving the generation of 272 datasets using Plaxis 2D automation. The parameters were systematically varied across feasible ranges, and Sobol‐based sensitivity analysis identified the negligible influence of the soil modulus of elasticity (E) on subsequent reliability analyses. A robust 1D‐CNN surrogate model was developed to predict the critical foundation responses by integrating Gaussian white noise to simulate real‐world uncertainties. A log transformation with 1,000 bootstrap samples was chosen for resampling non‐normally distributed data. This study employed a novel approach utilising 1D‐CNN regressor models for bearing capacity (BC) prediction, achieving promising results with R2 values of 0.953 and 0.945 for BC in the training and testing phases, respectively. Bootstrapping resampling facilitates reliability analysis preparation and ensures robustness in handling complex data. Simulated noise varied with specific variance (p) from 0.01 to 0.5, allowing the examination of model efficacy under varying noise levels. Both the Monte Carlo Simulation (MCS) and first‐order reliability method (FORM) were employed, revealing a reliability index (β) of 2.046 for FORM and 2.066 for MCS. This indicates a 0.976% increase in β and a 75% increase in the probability of failure transitioning from FORM to MCS, underscoring the model’s sensitivity to analytical methods.
Sriansh Raj Pradhan, Sushruta Mishra, Hrudaya Kumar Tripathy, Biswajit Brahma, R. Gobinath, and Rajeev Sobti
Springer Nature Singapore
Mohammad Hadi Dehghani, Parnia Bashardoust, Fatemeh Zirrahi, Benyamin Ajami, Mohammad Rezvani Ghalhari, Elahe Noruzzade, Samira Sheikhi, Nabisab Mujawar Mubarak, Rama Rao Karri, and Gobinath Ravindran
Elsevier
Rama Rao Karri, Nabisab Mujawar Mubarak, Salwa Kamal Mohamed Hassan, Mamdouh I. Khoder, Mohammad Hadi Dehghani, Teresa Vera, and Gobinath Ravindran
Elsevier
Rama Rao Karri, Teresa Vera, Salwa Kamal Mohamed Hassan, Mamdouh I. Khoder, Mohammad Hadi Dehghani, Nabisab Mujawar Mubarak, and Gobinath Ravindran
Elsevier
Rama Rao Karri, Gobinath Ravindran, Nabisab Mujawar Mubarak, Balram Ambade, Mohammad Hadi Dehghani, Salwa Kamal Mohamed Hassan, and Teresa Vera
Elsevier
Sushant Agarwal, Sanjay Saxena, Alessandro Carriero, Gian Luca Chabert, Gobinath Ravindran, Sudip Paul, John R. Laird, Deepak Garg, Mostafa Fatemi, Lopamudra Mohanty,et al.
Frontiers Media SA
Background and noveltyWhen RT-PCR is ineffective in early diagnosis and understanding of COVID-19 severity, Computed Tomography (CT) scans are needed for COVID diagnosis, especially in patients having high ground-glass opacities, consolidations, and crazy paving. Radiologists find the manual method for lesion detection in CT very challenging and tedious. Previously solo deep learning (SDL) was tried but they had low to moderate-level performance. This study presents two new cloud-based quantized deep learning UNet3+ hybrid (HDL) models, which incorporated full-scale skip connections to enhance and improve the detections.MethodologyAnnotations from expert radiologists were used to train one SDL (UNet3+), and two HDL models, namely, VGG-UNet3+ and ResNet-UNet3+. For accuracy, 5-fold cross-validation protocols, training on 3,500 CT scans, and testing on unseen 500 CT scans were adopted in the cloud framework. Two kinds of loss functions were used: Dice Similarity (DS) and binary cross-entropy (BCE). Performance was evaluated using (i) Area error, (ii) DS, (iii) Jaccard Index, (iii) Bland–Altman, and (iv) Correlation plots.ResultsAmong the two HDL models, ResNet-UNet3+ was superior to UNet3+ by 17 and 10% for Dice and BCE loss. The models were further compressed using quantization showing a percentage size reduction of 66.76, 36.64, and 46.23%, respectively, for UNet3+, VGG-UNet3+, and ResNet-UNet3+. Its stability and reliability were proved by statistical tests such as the Mann–Whitney, Paired t-Test, Wilcoxon test, and Friedman test all of which had a p < 0.001.ConclusionFull-scale skip connections of UNet3+ with VGG and ResNet in HDL framework proved the hypothesis showing powerful results improving the detection accuracy of COVID-19.
Lai Wah Sim, Herda Yati Binti Katman, Intan Nor Zuliana Binti Baharuddin, Gobinath Ravindran, Mohd Rasdan Ibrahim, and Adham Mohammed Alnadish
Institute of Electrical and Electronics Engineers (IEEE)
Soft soils present significant challenges to infrastructure development worldwide due to high compressibility, low shear strength and low permeability. Bibliometric analysis remains scarce in the soft soil management field, making it difficult to grasp global trends and contributions. This research addresses this gap by providing a novel and comprehensive bibliometric analysis of soft soil management literature to investigate the trend, opportunities and challenges. The analysis predominantly examines ground improvement methods based on 2,260 journal articles and proceeding papers from WOS core collection between 2013 and 2023 using CiteSpace. The analysis encompasses country distribution, authorship and co-cited authors, cited journals, reference co-citations and identification of research hotspots and frontiers. Findings show a growing interest and increased research focus in soft soil management, with China emerging as a prominent contributor. Reference co-citation clustering analysis reveals two dominant research themes: ground improvement methods (geosynthetic-reinforced embankments and stone columns) and sustainable practices (reuse of waste materials). The emerging word analysis reveals an evolutionary trend from investigating innovative and sustainable techniques to shear strength and failure mechanisms. Recent studies focus on the use of vertical drains, deep mixing columns, chemical stabilization, and sustainable ground improvement strategies. Opportunities lie in sustainable ground improvement methods and novel sensing technologies. Key challenges include bearing capacity, settlement, and slope stability. The study highlights the need for more research into two other key areas such as geotechnical characterization and foundation design. Overall, this bibliometric review contributes to a more thorough understanding of soft soil management studies for researchers and practitioners.
Muntather Almusawi, Gobinath Ravindran, Parameshachari B D, B. Bhasker, and Lavanya N L
IEEE
Wireless Sensor Networks (WSNs), the imperative challenge of energy efficiency takes center stage. This paper delves into inventive methodologies to combat energy depletion in sensor nodes, introducing the Chaotic Grey Wolf Optimization Algorithm (CGWOA) for optimal Cluster Head (CH) selection in WSNs. An evolution strategy and a mutation operation operator, CGWOA aims to amplify energy efficiency, scalability, and minimize network overhead through streamlined clustering. The outcomes underscore CGWOA's potential as a solution for advancing energy-efficient clustering and routing in WSNs, particularly in optimizing CH selection. The ensuing numerical validation underscores CGWOA's revealing an energy consumption of 1100 J, a throughput ratio of 69,785 kbps, 4900 active nodes, and a network lifetime extension to 8021. This comparative analysis shows the emphasis on problems to overcome existing methods like Particle Swarm Optimization for Energy-Efficient Clustering (PSO-EEC), Adaptive Hybrid Clustering Scheme with Grey Wolf Optimization (AHCS-GWO), Hybrid Optimization for Cluster Head Selection (HOCK).
Lakshmana Rao Kalabarige, Jayaprakash Sridhar, Sivaramakrishnan Subbaram, Palaniappan Prasath, and Ravindran Gobinath
Hindawi Limited
This study aimed to develop accurate models for estimating the compressive strength (CS) of concrete using a combination of experimental testing and different machine learning (ML) approaches: baseline regression models, boosting model, bagging model, tree-based ensemble models, and average voting regression (VR). The research utilized an extensive experimental dataset with 14 input variables, including cement, limestone powder, fly ash, granulated glass blast furnace slag, silica fume, rice husk ash, marble powder, brick powder, coarse aggregate, fine aggregate, recycled coarse aggregate, water, superplasticizer, and voids in mineral aggregate. To evaluate the performance of each ML model, five metrics were used: mean absolute error (MAE), mean squared error (MSE), root mean squared error (RMSE), coefficient of determination (R2-score), and relative root mean squared error (RRMSE). The comparative analysis revealed that the VR model exhibited the highest effectiveness, displaying a strong correlation between actual and estimated outcomes. The boosting, bagging, and VR models achieved impressive R2-scores in the range of 86.69%–92.43%, with MAE ranging from 3.87 to 4.87, MSE from 21.74 to 38.37, RMSE from 4.66 to 4.87, and RRMSE between 8% and 11%. Particularly, the VR model outperformed all other models with the highest R2-score (92.43%) and the lowest error rate. The developed models demonstrated excellent generalization and prediction capabilities, providing valuable tools for practitioners, researchers, and designers to efficiently evaluate the CS of concrete. By mitigating environmental vulnerabilities and associated impacts, this research can significantly contribute to enhancing the quality and sustainability of concrete construction practices.
S. Karthik, S. Anandaraj, M. Senthil Rajan, R. Gobinath, S. Rajasekar, and R. Navinkumar
AIP Publishing
Ashwini Salunke, Pradnya Bidbag, R. Gobinath, S. Bhore, A. Patil, and Y. Mane
AIP Publishing
G. Shyamala, R. Gobinath, and K. Rajesh Kumar
EDP Sciences
This paper analyzes the problem of septage management related to opportunities and solution. in accordance with present treatment technologies. The scenario in India is about 41 % of septage sludge is disposed in local area without treatment Still some of the individual housing is not connected to suitable public sewage system. The current scenario of FSM in Warangal city is 77 % of the households have proper access to toilet and 23% insanitary toilet and open defecation were found. The samples were collected from the Warangal city and were tested for the characteristics. BOD and COD were very high above 9800 mg/l. As per CPHEEO norms TSS should be less than 15000 mg/l, but it is observed in all the samples it is in the range of 24,800 mg/l to 82,460 mg/l. Currently the treatment such as sludge drying beds, lime treatment, anaerobic baffled reactor, stabilization pond, constructed wetland, composting with solid waste, Mechanical dewatering,. Neglected septage waste ash was tried for several trial run in the mix design and found 20 % to be optimum replacement of septage ash. Strength enhancement is achieved by adding glass chopped strands and workability is improved by Super plasticizer Polycarboxlate ether. Utilization of residue in septage treatment plant in cost effective and ecofriendly way by replacing cement in concrete was tried in the pilot scale study near Warangal and proven to be effective.
Rabindra Kumar, Purushottam Kumar Singh, Showmen Saha, Santosh Kr. Mishra, Pankaj Kumar, and Ravindran Gobinath
Elsevier BV
Gokulan Ravindiran, Sivarethinamohan Rajamanickam, Sujatha Sivarethinamohan, Balamurugan Karupaiya Sathaiah, Gobinath Ravindran, Senthil Kumar Muniasamy, and Gasim Hayder
MDPI AG
Most water systems that support ecosystems and feed humans are depleted or stressed. Aquifer characteristics, topography, subsurface activities, climate, and geochemical processes regulate groundwater availability, a reliable source of fresh water. Globally, agriculture, industries, and the domestic sector are the three major sectors that consume vast quantities of freshwater resources. Further anthropogenic activities, such as soil leaching, acid rain, fertilizer, pesticides, mining, and other industrial activities, resulted in the release of organic and inorganic pollutants that affected global water resources. In India, groundwater is used in huge quantities, resulting in groundwater depletion of 1 to 2 m a year. Low-income countries face many issues related to water pollution, and the availability of safe water is minimal. In 2019, deaths due to unsafe sanitation accounted for 2.2% of the total global deaths, amounting to 1.2 million people’s deaths. India recorded 6.6% of deaths due to unsafe sanitation in 2019. India and China accounted for around 90.41% and 60.4% of the groundwater utilization for agricultural purposes, respectively. In 2020, China and India utilized vast quantities of nutrients (nitrate and phosphate) for crop growth to enhance crop yield, resulting in the highest nitrate and phosphate concentrations in groundwater. Remediating contaminants from different sources requires knowledge of their concentration, behavior, cycling, and degradation pathways. According to safety guidelines, limiting and optimizing crop organic and inorganic fertilizer, pesticide waste disposal, and empty container disposal can reduce groundwater contamination. The present study summarized groundwater utilization in various sectors, potential sources of groundwater contamination impacts on human health and the environment, preventive measures, and mitigation methods to overcome groundwater pollution.
Gobinath Ravindran, Alireza Bahrami, Vutukuru Mahesh, Herda Yati Binti Katman, Katakam Srihitha, Alamadri Sushmashree, and Alugoju Nikhil Kumar
MDPI AG
Soil, a naturally occurring resource, is increasingly used as a construction material. Stabilisation strengthens soil, which is weak as an engineering material. Stabilising soil changes its physical qualities, enhancing its strength. Soil stabilisation increases the shear strength and load-bearing capacity. Soil stabilisation refers to any endeavour to change natural soil for engineering purposes using physical, chemical, mechanical, or biological methods, or a mix of these. Strengthening road pavements includes improving the load-bearing capacity, tensile strength, and performance of unstable subsoils, sands, and waste materials. Due to market demands and scientific advances, the number of soil-stabilising additives has increased. These innovative stabilisers include reinforcing fibres, calcium chloride, sodium chloride, and cross-linking water-based styrene acrylic polymers, which are geopolymers that boost the load-bearing capacity and tensile strength of soil. Many materials are being explored for soil stabilisation. In this article, the authors investigated the direction of soil stabilisation research. Scientometric analysis identifies stabilisation challenges and research trends in the field. This study analysed research patterns by countries, authors, institutions, keywords, and journals from 1959 to 2023; in 2021, 150 articles were published, which was the highest number in a year. Citations peaked at 3084 in 2022. With 253 publications and 3084 citations, India was the most productive country. Iran and France published the fewest, 34 and 33, respectively. The Islamic Azad University and the National Institute of Technology had the fewest published articles with 17 articles. This work can help track soil stabilisation research and will serve as an information document for future research.
RECENT SCHOLAR PUBLICATIONS
MOST CITED SCHOLAR PUBLICATIONS
RESEARCH OUTPUTS (PATENTS, SOFTWARE, PUBLICATIONS, PRODUCTS)
A NOVEL METHOD OF ENHANCE FREEZE-THAW RESISTANCE OF SOIL - Patent application no: 202041005809
A NOVEL METHOD TO PREPARE SELF-COMPACTING CONCRETE USING SINGLE ALKALI ACTIVATED ASH BASED CONCRETE - Patent application no: 202041004257
A NOVEL STRENGTH ENHANCEMENT PROCEDURE FOR NATURAL CURED BINDERLESS CONCRETE- Patent application no:201941042302
A NOVEL METHOD OF WATER CONTENT IDENTIFICATION USING IMAGE PROCESSING FOR LAND SLIDE PRE CURSOR - Patent application no:201941042299
SILICA BASED BINDER COMPOSITION FOR SOIL STABILIZATION AND ENHANCING PAVEMENT LOAD BEARING CAPACITY OF ROADS - Patent application no:201941012760
Industry, Institute, or Organisation Collaboration
Covenant University, Otta, Nigeria
VIT University, Vellore
Anna University, Chennai
Windsor University, Canada