Open AccessPrediction of Joint Shear Strength of RC Beam-Column Joint Subjected to Seismic loading using Artificial Neural Network
Author(s) -
Shreyas Alagundi,
T. Palanisamy
Publication year - 2021
Publication title -
safer
Language(s) - English
Resource type - Journals
ISSN - 0719-3726
DOI - 10.7770/safer-v10n1-art2490
Subject(s) - joint (building) , structural engineering , artificial neural network , shear (geology) , shear strength (soil) , failure mode and effects analysis , reinforced concrete , beam (structure) , seismic loading , engineering , computer science , geotechnical engineering , geology , materials science , artificial intelligence , composite material , soil science , soil water
BC (Beam-column) joints are critical locations in RC frames subjected to severe earthquake attack. Failure of Joint which is of shear type is not an appreciable structural behaviour. Present study proposes an artificial neural network model for joint shear strength of reinforced exterior BC connections. ANN is a component of artificial intelligence which mimics the human brain characteristics and learns from previous experiences and has recently gained popularity in the area of civil engineering. A dataset of specimen dimensions, material properties, Area of Reinforcement used in Beam and column and failure mode are established from past experimental investigations on BC joints subjected to seismic type loading and are used for ANN modelling. ANN model is developed with eleven input parameters to predict the Joint shear strength of Exterior BC Joints. The Proposed model is compared with the equation given in design codes and empirical formula. Proposed ANN model has predicted the shear strength more accurately. Thus the proposed ANN model can be used for Prediction of Joint shear strength of Reinforced concrete exterior BC joints subjected to seismic loading.
Keywords— Joint Shear strength, Beam-Column Joint, Artificial neural network