000			02916nam a22003137a 4500
003			OSt
005			20250522183740.0
008			250522b |||||||| |||| 00| 0 eng d
040			_aTUPM _bTUPM _cTUPM _dEnglish _erda
050			_aBTH TA 145 _bA53 2024
100			_aAndrade, Bea Marie L. _eauthor
245			_aPrediction of Reinforced Concrete Beam-Column Joint Capacity Considering Shear Stress-Strain Using Feedback Propagation in MATLAB/ _cBea Marie L. Andrade, Carla Jane C. Evangelista, Caitlyn A. Merelos, Kyla G. Remetio and Vincent A. Salvador.--
260			_bTechnological University of the Philippines, Manila. _cJuly 2024
300			_axiv, 218 pages. _c29 cm
336			_2rdacontent
337			_2rdamedia
338			_2rdacarrier
500			_aBachelor's thesis
502			_aCollege of Engineering.-- _bBachelor of Science in Civil Engineering: _cTechnological University of the Philippines, Manila. _d2024
504			_aIncludes bibliographic references and index.
520			_aArtificial 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. Keywords: Artificial Neural Networks (ANN), beam-column joint capacity, shear stress- strain, MATLAB, structural analysis, Levenberg-Marquardt algorithm, TANSIG activation function, Cook’s Distance Method, predictive modeling, spreadsheet tool.
650			_aCivil Engineering
650			_aArtificial Neural Networks (ANN)
700			_aEvangelista, Carla Jane C. _eauthor
700			_aMerelos, Caitlyn A. _eauthor
700			_aRemetio, Kyla G. _eauthor
700			_aSalvador, Vincent A. _eauthor
942			_2lcc _cBTH COE _n0
999			_c29881 _d29881