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Strengthening of brick masonry with welded wire mesh
(Science Publishing Corporation Inc, 2018-07-20) Umamaheswari, V; Kanchidurai, S; Krishnan, P.A.; Baskar, K
Unreinforced brickwork (URM) is the most established development method. URM being weak can't withstand the parallel burdens amid a seismic region. Consequently, it is important to locate an appropriate low-cost technique to fortify existing brickwork structures. In this paper flexural bond strength test was conducted on the rectangular brick masonry prisms with two types of welded wire meshes (epoxy coated mesh with the spacing of 12mm and galvanized iron wire mesh with the spacing of 15mm). Masonry prisms were cast and tested as per the guidelines are given in ASTM E518 /E518-15 standards. The results of the flexural bond strength embedded masonry prism show greater when comparing the prisms with no mesh embedment
An assessment of brick masonry strengthening practice by special methods
(Blue Eyes Intelligence Engineering and Sciences Publication, 2019-09-03) Kanchidurai, S; Krishnan, P.A.; Baskar, K
This study aims to identify the best suitable method to enhance strength and the structural performance of masonry. There are different techniques available to strengthen the existing and new masonry structures. This paper deals the metal/mesh embedment in the masonry wall, strengthening by added different polymers and textile strips, masonry grout, engineered cementitious materials (ECC) and interlocking masonry method. The comparison of different unique masonry strengthening methods helps us to provide a better suggestion for construction issues. In contrast to the conventional method, welded wire mesh gives better results than all other ways. Also, embedment of TRM, ECC, FRP, GFRP, CFRP, and interlocking holds an excellent performance in some other aspects. © BEIESP.
GIS-based multi-criteria analysis for identification of potential groundwater recharge zones - a case study from Ponnaniyaru watershed, Tamil Nadu, India
(KeAi Communications Co., 2020-02-21) Abijith, Devanantham; Saravanan, Subbarayan; Singh, Leelambar; Jennifer, Jesudasan Jacinth; Saranya, Parthasarathy K.S.S.
Groundwater is one of the most vital natural resources; spatially varying in quality and quantity. Increased urbanisation and population creates tremendous pressure on the quality and quantity of the groundwater resources. In this study, Ponnaniyaru watershed of Cauvery basin was considered for this research. Geographical information system (GIS) and remote sensing (RS) plays a vital role in preparing various thematic layers for targeting the groundwater potential zones (GWPZ). This study adopts the Analytical Hierarchy Process (AHP) and Multi influence factor (MIF), multi-criteria decision-making approaches to determine the weights for the influencing factors. Weighted linear overlay analysis was carried out to determine the GWPZ. Further, the resultant GWPZ map has been reclassified into five different classes, namely Very good, Good, Moderate, Poor and Very poor. The results were validated with observed well-yield data, and the predictive precision for AHP and MIF was found to be 75%, and 71% respectively. © 2020 The Authors
Kinetics and equilibrium studies for the removal of heavy metals in both single and binary systems using hydroxyapatite
(Springer Verlag, 2012-03-21) Ramesh S.T.; Rameshbabu, N; Gandhimathi, R; Nidheesh, P.V.; Srikanth Kumar, M
Removal of heavy metals is very important with respect to environmental considerations. This study investigated the sorption of copper (Cu) and zinc (Zn) in single and binary aqueous systems onto laboratory prepared hydroxyapatite (HA) surfaces. Batch experiments were carried out using synthetic HA at 30 °C. Parameters that influence the adsorption such as contact time, adsorbent dosage and pH of solution were investigated. The maximum adsorption was found at contact time of 12 and 9 h, HA dosage of 0. 4 and 0. 7 g/l and pH of 6 and 8 for Cu and Zn, respectively, in single system. Adsorption kinetics data were analyzed using the pseudofirst-, pseudosecond-order and intraparticle diffusion models. The results indicated that the adsorption kinetic data were best described by pseudosecond-order model. Langmuir and Freundlich isotherm models were applied to analyze adsorption data, and Langmuir isotherm was found to be applicable to this adsorption system, in terms of relatively high regression values. The removal capacity of HA was found to be 125 mg of Cu/g, 30. 3 mg of Zn/g in single system and 50 mg of Cu/g, 15. 16 mg of Zn/g in binary system. The results indicated that the HA used in this work proved to be effective material for removing Cu and Zn from aqueous solutions. © 2012 The Author(s).
Adsorptive removal of Pb(II) from aqueous solution using nano-sized hydroxyapatite
(Springer Verlag, 2012-10-11) Ramesh, S.T.; Rameshbabu, N; Gandhimathi, R; Srikanth Kumar, M; Nidheesh P.V.
This study investigated the sorption of Pb(II) in aqueous solution onto hydroxyapatite (HA) surfaces. Batch experiments were carried out using synthetic HA. The effect of contact time, HA dosage, and initial pH on removal efficiency were also investigated. The adsorption equilibrium and kinetics of Pb(II) on this adsorbent were then examined at 25 °C. Kinetic data were analyzed by pseudo first, second, and intra-particle diffusion models. The sorption data were then correlated with the Langmuir, Freundlich, Halsey, and Harkins-Jura adsorption isotherm models. The optimum dose of HA for Pb(II) removal is found to be 0. 12 g/l with the removal efficiency of 97. 3 % at an equilibrium contact time of 1 h. It is found that the adsorption kinetics of the Pb(II) on HA follow the pseudo second-order reaction. All the isotherms fitted well for experimental data. Capacity of HA is found as 357. 14 mg Pb(II)/g of HA. The Pb(II) immobilization mechanism was studied. The results indicated that HA can be used as an effective adsorbent for removal of Pb(II) from aqueous solution. © 2012 The Author(s).
Effects of Climate Change on Streamflow in the Godavari Basin Simulated Using a Conceptual Model including CMIP6 Dataset
(MDPI, 2023-04-27) Reddy, Nagireddy Masthan; Saravanan, Subbarayan; Almohamad, Hussein; Al Dughairi, Ahmed Abdullah; Abdo, Hazem Ghassan
Hydrological reaction to climate change anticipates water cycle alterations. To ensure long-term water availability and accessibility, it is essential to develop sustainable water management strategies and better hydrological models that can simulate peak flow. These efforts will aid in water resource planning, management, and climate change mitigation. This study develops and compares Sacramento, Australian Water Balance Model (AWBM), TANK, and SIMHYD conceptual models to simulate daily streamflow at Rajegaon station of the Pranhita subbasin in the Godavari basin of India. The study uses daily Indian Meteorological Department (IMD) gridded rainfall and temperature datasets. For 1987–2019, 70% of the models were calibrated and 30% validated. Pearson correlation (CC), Nash Sutcliffe efficiency (NSE), Root mean square error (RMSE), and coefficient of determination (CD) between the observed and simulated streamflow to evaluate model efficacy. The best conceptual (Sacramento) model selected to forecast future streamflow for the SSP126, SSP245, SSP370, and SSP585 scenarios for the near (2021–2040), middle (2041–2070), and far future (2071–2100) using EC-Earth3 data was resampled and bias-corrected using distribution mapping. In the far future, the SSP585 scenario had the most significant relative rainfall change (55.02%) and absolute rise in the annual mean temperature (3.29 °C). In the middle and far future, the 95th percentile of monthly streamflow in the wettest July is anticipated to rise 40.09% to 127.06% and 73.90% to 215.13%. SSP370 and SSP585 scenarios predicted the largest streamflow increases in all three time periods. In the near, middle, and far future, the SSP585 scenario projects yearly relative streamflow changes of 72.49%, 93.80%, and 150.76%. Overall, the findings emphasize the importance of considering the potential impacts of future scenarios on water resources to develop effective and sustainable water management practices.
Streamflow simulation using conceptual and neural network models in the Hemavathi sub-watershed, India
(Elsevier B.V., 2022-11-21) Reddy, Nagireddy Masthan; Saravanan, Subbarayan; Abijith, Devanantham
Water is one of the most valuable natural resources and a major element of a state's and country's socioeconomic growth. The world's water resources and India are under huge pressure because of rising demand and a limited supply. Proper water management is the only solution for ensuring a close gap between demand and supply. Hydrological modeling offers an answer to this issue by establishing relationships between different hydrological processes. Several models have been developed in the past decade to simulate the rainfall and runoff relations. Some models are simple conceptual models based on spatially distributed event-based or continuous and artificial intelligence (AI) models. This study aims to compare two conceptual daily-based models and one AI model developed for the Hemavathi sub-watershed in the Cauvery Basin (India). Two daily runoff models are implemented using conceptual models, i.e., Sacramento and the Australian water balance model (AWBM) using Rainfall-Runoff Library (RRL) tool and Feed forward Backpropagation neural network (FFBPNN) model. The models were calibrated for daily streamflow values from 1990 to 2006 and then validated from 2007 to 2015. The effectiveness of model runoff predictions is evaluated using statistical parameters such as Nash-Sutcliffe efficiency (NSE) and Correlation coefficient (CC) values. The NSE values for the FFBPNN is 0.88 (calibration) and 0.74 (validation), Sacramento model is 0.66 (calibration) and 0.48 (validation), and 0.63 (calibration) and 0.44 (validation) for the AWBM model. From the obtained results, the FFBPNN model performs well in terms of NSE and CC compared to Sacramento and AWBM models. © 2022
Multi-Criterion Analysis of Cyclone Risk along the Coast of Tamil Nadu, India—A Geospatial Approach
(Multidisciplinary Digital Publishing Institute (MDPI), 2023-08-16) Saravanan, Subbarayan; Abijith, Devanantham; Kulithalai Shiyam Sundar, Parthasarathy; Reddy, Nagireddy Masthan; Almohamad, Hussein; Al Dughairi, Ahmed Abdullah; Al-Mutiry, Motrih; Abdo, Hazem Ghassan
A tropical cyclone is a significant natural phenomenon that results in substantial socio-economic and environmental damage. These catastrophes impact millions of people every year, with those who live close to coastal areas being particularly affected. With a few coastal cities with large population densities, Tamil Nadu’s coast is the third-most cyclone-prone state in India. This study involves the generation of a cyclone risk map by utilizing four distinct components: hazards, exposure, vulnerability, and mitigation. The study employed a Geographical Information System (GIS) and an Analytical Hierarchical Process (AHP) technique to compute an integrated risk index considering 16 spatial variables. The study was validated by the devastating cyclone GAJA in 2018. The resulting risk assessment shows the cyclone risk is higher in zones 1 and 2 in the study area and emphasizes the variations in mitigation impact on cyclone risk in zones 4 and 5. The risk maps demonstrate that low-lying areas near the coast, comprising about 3%, are perceived as having the adaptive capacity for disaster mitigation and are at heightened risk from cyclones regarding population and assets. The present study can offer valuable guidance for enhancing natural hazard preparedness and mitigation measures in the coastal region of Tamil Nadu.
Machine Learning Approaches for Streamflow Modeling in the Godavari Basin with CMIP6 Dataset
(Multidisciplinary Digital Publishing Institute (MDPI), 2023-08-11) Saravanan, Subbarayan; Reddy, Nagireddy Masthan; Pham, Quoc Bao; Alodah, Abdullah; Abdo, Hazem Ghassan; Almohamad, Hussein; Al Dughairi, Ahmed Abdullah
Accurate streamflow modeling is crucial for effective water resource management. This study used five machine learning models (support vector regressor (SVR), random forest (RF), M5-pruned model (M5P), multilayer perceptron (MLP), and linear regression (LR)) to simulate one-day-ahead streamflow in the Pranhita subbasin (Godavari basin), India, from 1993 to 2014. Input parameters were selected using correlation and pairwise correlation attribution evaluation methods, incorporating a two-day lag of streamflow, maximum and minimum temperatures, and various precipitation datasets (including Indian Meteorological Department (IMD), EC-Earth3, EC-Earth3-Veg, MIROC6, MRI-ESM2-0, and GFDL-ESM4). Bias-corrected Coupled Model Intercomparison Project Phase 6 (CMIP6) datasets were utilized in the modeling process. Model performance was evaluated using Pearson correlation (R), Nash–Sutcliffe efficiency (NSE), root mean square error (RMSE), and coefficient of determination (R2). IMD outperformed all CMIP6 datasets in streamflow modeling, while RF demonstrated the best performance among the developed models for both CMIP6 and IMD datasets. During the training phase, RF exhibited NSE, R, R2, and RMSE values of 0.95, 0.979, 0.937, and 30.805 m3/s, respectively, using IMD gridded precipitation as input. In the testing phase, the corresponding values were 0.681, 0.91, 0.828, and 41.237 m3/s. The results highlight the significance of advanced machine learning models in streamflow modeling applications, providing valuable insights for water resource management and decision making.
Assessing the impact of climate and land use change on flood vulnerability: a machine learning approach in coastal region of Tamil Nadu, India
(Springer Science and Business Media Deutschland GmbH, 2025-01-27) Abijith, Devanantham; Saravanan, Subbarayan; Parthasarathy K.S.S.; Reddy, Nagireddy Masthan; Niraimathi, Janardhanam; Bindajam, Ahmed Ali; Mallick, Javed; Alharbi, Maged Muteb; Abdo, Hazem Ghassan
Flooding and other natural disasters threaten human life and property worldwide. They can cause significant damage to infrastructure and disrupt economies. Tamil Nadu coast is severely prone to flooding due to land use and climate changes. This research applies geospatial tools and machine learning to improve flood susceptibility mapping across the Tamil Nadu coast in India, using projections of Land Use and Land Cover (LULC) changes under current and future climate change scenarios. To identify flooded areas, the study utilised Google Earth Engine (GEE), Sentinel-1 data, and 12 geospatial datasets from multiple sources. A random forest algorithm was used for LULC change and flood susceptibility mapping. The LULC data are classified for the years 2000, 2010, and 2020, and from the classified data, the LULC for years 2030, 2040, and 2050 are projected for the study. Four future climate scenarios (SSP 126, 245, 370, and 585) were used for the average annual precipitation from the Coupled Model Intercomparison Project 6 (CMIP6). The results showed that the random forest model performed better in classifying LULC and identifying flood-prone areas. From the results, it has been depicted that the risk of flooding will increase across all scenarios over the period of 2000–2100, with some decadal fluctuations. A significant outcome indicates that the percentage of the area transitioning to moderate and very high flood risk consistently rises across all future projections. This study presents a viable method for flood susceptibility mapping based on different climate change scenarios and yields estimates of flood risk, which can provide valuable insights for managing flood risks. © The Author(s) 2025.