Prediction of Reinforced Concrete Beam-Column Joint Capacity Considering Shear Stress-Strain Using Feedback Propagation in MATLAB/
Bea Marie L. Andrade, Carla Jane C. Evangelista, Caitlyn A. Merelos, Kyla G. Remetio and Vincent A. Salvador.--
- Technological University of the Philippines, Manila. July 2024
- xiv, 218 pages. 29 cm
Bachelor's thesis
College of Engineering.--
Includes bibliographic references and index.
Artificial Neural Networks (ANNs) are widely applied in construction engineering, particularly structural analysis and design. This study investigates the use of ANNs for predicting beam-column joint capacity based on shear stress-strain, utilizing MATLAB as the implementation platform. An ANN model was developed and trained to enhance predictive accuracy and optimize structural design parameters. A dataset of 517 samples was compiled from 137 research studies, with 153 outliers identified and removed using Cook's Distance Method in MATLAB, leaving 364 datasets for training. The network was trained using the Levenberg-Marquardt (LM) algorithm, a widely used iterative technique for nonlinear least-squares problems, combined with the TANSIG
activation function. The model achieved a mean squared error (MSE) of 0.480 and an R- value of 0.94, with the optimal configuration being 36 layers. Validation was conducted
through simulations, weight, and bias computations using the Sigmoid function, and graphical analyses of stress-strain and moment-rotation curves. The study developed a spreadsheet-based tool that enables efficient shear stress-strain prediction by inputting key structural parameters. This tool offers significant benefits for structural analysis, design, and the maintenance of buildings, particularly in developing regions.