The compressive strength is most reliable parameter to evaluate the ability of concrete in resisting compression. The paper presents a study on prediction of the compressive strength of roller compacted concrete using multiple regression analysis (MRA) and artificial neural networks (ANN). The compressive strength of roller compacted concrete was obtained experimentally at 3, 7 and 28 days of curing. The samples were prepared by varying the percentage of cement and superplasticizer. The data were organized in three different groups randomly using R statistical software. The models were executed with cement content, coarse and fine aggregate, superplasticizer content, water content and days of aging as input parameters that were used to predict compressive strength which is the output parameter. The analysis was performed using multiple regression and artificial neural networks methodology. Statistical measures like root-mean-square error (RMSE), mean absolute error (MAE) and coefficient of determination are used to assess the performance of models. The determination coefficient from multiple regression analysis is found to be 0.975 and 0.886 for testing and validating the data correspondingly, whereas the determination coefficient from artificial neural network analysis is found to be 0.9 for both testing and validating the data. The results obtained from ANN are highly accurate because of its own topology.

Water is essential for the survival of mankind on the surface of the Earth. The surface water bodies which act as a source of drinking water are prone to pollution in the current days. As a result people rely on groundwater sources for drinking, irrigation etc., It has become a necessity to evaluate the quality of groundwater as it is being polluted to a large extent because of rapid urbanization and industrialization. The present study aims to assess the quality of groundwater in Kurnool district. The samples are collected at various well locations and are studied for physico-chemical parameters H + ion concentration, bi-carbonate, carbonates, sulphates, and chloride concentration. Thematic maps for each physico-chemical parameter are prepared by Inverse distance weightage interpolation method in Geographical Information system. The groundwater quality is assessed using a water quality index, which expresses overall quality at a location. Based on the results potential zones are identified by query builder in ArcGIS. Thus it can be concluded that the potential zones act as sources of drinking water.

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