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V. Srinivas

Researcher at Structural Engineering Research Centre

Publications -  38
Citations -  530

V. Srinivas is an academic researcher from Structural Engineering Research Centre. The author has contributed to research in topics: Beam (structure) & Vibration. The author has an hindex of 12, co-authored 34 publications receiving 434 citations. Previous affiliations of V. Srinivas include Bangalore University & Council of Scientific and Industrial Research.

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Seismic retrofitting of nonductile beam-column sub-assemblage using FRP wrapping and steel plate jacketing

TL;DR: In this paper, the authors discussed the aspects of repair and retrofitting technique adopted for a damaged reinforced concrete beam-column joint specimen under cyclic loading and showed that a proper repair and adequate retrofitting can be used for strengthening and improvement of damaged regions in reinforced concrete structures.
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An integrated approach for optimum design of bridge decks using genetic algorithms and artificial neural networks

TL;DR: An integrated approach using artificial neural networks (ANN) and genetic algorithms (GA) for cost optimization of bridge deck configurations and guarantees bridge engineers to arrive at the near-optimal solution that could not be easily obtained using general modeling programs or by trial-and-error.
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Performance evaluation of dry and epoxy jointed segmental prestressed box girders under monotonic and cyclic loading

TL;DR: In this paper, the performance of segmental box-girders with both dry and epoxy joints was evaluated under static and cyclic loading, and it was observed that the flexural strength of dry jointed segmental girders is less than the epoxy joint due to high concentration of rotation and deflection at individual joints of the segmental girder.
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Multi-stage approach for structural damage identification using modal strain energy and evolutionary optimization techniques

TL;DR: In this paper, a multi-stage approach has been proposed for the identification and localization of damage based on optimization techniques and modal-based approaches, which reduces the number of parameters of the objective function in the optimization approach.