Browsing by Author "Prasanth, S."

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Effect of recuring on compressive strength of thermally deteriorated concrete cubes
(2011) Prasanth, S.; Yaragal, S.C.; Babu Narayan, K.S.
Concrete is found to undergo degradation when subjected to elevated temperatures during an event such as fire and lose substantial amount of its strength. The loss of strength in concrete is mainly attributed to decomposition of C-S-H and release of chemically bound water, which begins when the exposure temperature exceeds 500°C. When thermally deteriorated concrete is supplied with water there is a substantive gain in strength as a consequence of rehydration of cement that is initiated. This paper presents results of an experimental program carried out to investigate the effect of recuring on strength gain of normal strength concrete specimens subjected to elevated temperatures from 500°C to 800°C, which were subjected to retention time of two hours at the designated temperatures. © 2011 CAFET-INNOVA TECHNICAL SOCIETY. All rights reserved.
Neural network based technique for preliminary design of reinforced concrete columns
(2007) Raju, K.R.; Raghu, M.; Prasanth, S.
An Artificial Neural Network (ANN) based preliminary design methodology for reinforced concrete columns was developed by linking two neural networks with logical rules as per IS:456-2000. The first neural network is simulated for capturing interaction diagram for column subjected to axial load and uniaxial bending moments. The second neural network is developed to capture the interaction equation for column subjected to axial and biaxial moments. The sensitivity of the reinforcement in columns corresponding to different parameters such as depth to cover ratio, arrangement of reinforcement bars (number of bars in a row in horizontal and vertical direction), and grade of steel are captured in ANN. The trained ANNs automatically interpolate and extrapolate the results for given design parameters. In this study, the neural networks are used to replace the iterative processing steps involved in actual design computation.
Neural network based technique for preliminary design of reinforced concrete columns
(2007) Rama Raju, K.R.; Raghu, M.; Prasanth, S.
An Artificial Neural Network (ANN) based preliminary design methodology for reinforced concrete columns was developed by linking two neural networks with logical rules as per IS:456-2000. The first neural network is simulated for capturing interaction diagram for column subjected to axial load and uniaxial bending moments. The second neural network is developed to capture the interaction equation for column subjected to axial and biaxial moments. The sensitivity of the reinforcement in columns corresponding to different parameters such as depth to cover ratio, arrangement of reinforcement bars (number of bars in a row in horizontal and vertical direction), and grade of steel are captured in ANN. The trained ANNs automatically interpolate and extrapolate the results for given design parameters. In this study, the neural networks are used to replace the iterative processing steps involved in actual design computation.