Modeling of shear strength of RC beams using artificial neural network
Date of Publication
2009
Document Type
Bachelor's Thesis
Degree Name
Bachelor of Science in Civil Engineering with Specialization in Structural Engineering
Subject Categories
Civil Engineering
College
Gokongwei College of Engineering
Department/Unit
Civil Engineering
Thesis Adviser
Andres Winston C. Oreta
Defense Panel Member
Jonathan R. Dungca
Ronaldo S. Gallardo
Abstract/Summary
The increase in data available in literature on the shear strength of reinforced concrete (RC) beams prompted the creation of model that would be able to predict the shear strength of RC beams with a wider range of parameters. Two artificial neural network models were developed from the data currently available. The models were classified according to whether the beams had shear or without shear reinforcement. The models were then compared to existing design codes to verify the models accuracy. It was found out that the models developed by ANN were able to provide better predictions of the shear strength of RC beams. The model was also used to conduct parametric analysis. It was found out that there were other parameters that greatly affect the shear strength of RC beams other than the concrete compressive strength.
Abstract Format
html
Language
English
Format
Accession Number
TU19779
Shelf Location
Archives, The Learning Commons, 12F, Henry Sy Sr. Hall
Physical Description
xvii, 119 leaves : illustrations (some colored) ; 28 cm. + ; 1 cd supplement.
Keywords
Concrete beams; Reinforced concrete; Polymer-impregnated concrete
Recommended Citation
Aquino, D. A., Que, P. B., & Urena, A. B. (2009). Modeling of shear strength of RC beams using artificial neural network. Retrieved from https://animorepository.dlsu.edu.ph/etd_bachelors/11513
