Dr. G. Sreelal
@krct.ac.in
Assistant Professor/Department of Civil Engineering
K Ramakrishnan College of Technology
EDUCATION
B.E Civil Engineering
M.E Environmental Engineering
Ph.D. Civil Engineering
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
<p>An approach was used in this study to relate the predicted and calculated water quality index (WQI) of the Brahmani River. The WQI was predicted using an artificial neural network (ANN) tool, and the weighted Arithmetic Index technique was used to calculate the WQI (WAI). The WQI is calculated using physicochemical parameters as input data. Pollution Control Board (India) data was utilised to train and evaluate the model, as well as to forecast WQI. The ANN model is trained using the feed-forward back-propogation approach. 70 percent of the data was used for training, whereas 30 percent was used for testing and validation (15 percent) (15 percent). The regression coefficients for all of the stations were greater than 0.9, indicating that ANN modelling produced successful results. For all stations, the average percent of variance between anticipated and computed WQI is 8.63 percent. According to the findings of this study, the ANN model may be useful for predicting the WQI of both surface and groundwater.</p>
A. Karthick, S. Ramkumar, R. Raja, and G. Sreelal
Elsevier BV
Jayaraju Raja Murugadoss, Nachimuthu Balasubramaniam, Ravindiran Gokulan, Kanta Naga Rajesh, Gurusamy Pandian Sreelal, Pupalwad Arti Sudam, Dhaleelur Rahman Zunaithur Rahman, and Razack Nasar Ali
Hindawi Limited
The main ingredients of concrete are derived from natural resources such as cement, sand, and coarse aggregate. Rapid urbanization leads to the high demand for concrete causing depletion of natural deposits of sand. In this study, the optimized quantities of sand with spent garnet sand are compared in Design Expert’s Response Surface Method and R Programming’s RStudio packages in terms of predicted and actual compressive and flexural strength at 28 days of curing. Optimization of sand with spent garnet sand at various percentages such as 20, 40, 60, and 80 is proposed. The findings revealed that the correlation coefficient ( R 2 ) of 28 days compressive strength is 0.976 and 28 days flexural strength is 0.969 in both software. It indicates that both software can effectively predict and optimize.