TY - GEN
T1 - Predicting Shear, Stiffness and Stirrup Strain Histories in Reinforced Concrete Beams Using Machine Learning
AU - Castillo, Rodrigo
AU - Okumus, Pinar
AU - Elhami-Khorasani, Negar
AU - Chandola, Varun
N1 - Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2023
Y1 - 2023
N2 - Shear failures in reinforced concrete structures should be evaluated to ensure safety. Traditional evaluation methods for members with shear cracks include detailed analyses or expert opinion. This study uses machine learning to investigate how crack widths correlate with shear load, stiffness, and stirrup strain histories. A shear test database is assembled with 260 and 480 crack width measurements for rectangular slender beams with shear reinforcement ratios smaller and larger than the minimum required by ACI 318-19, respectively. Measured load-displacement relationships, stirrup strains, and crack widths were documented. Gaussian Process Regression (GPR), a machine learning method, is used to correlate crack width to shear history, stiffness, and stirrup strains considering beam design details. The three indicators (shear, stiffness, and stirrup strain histories) predicted based on test data given a crack width can be a rapid in-service performance evaluation tool for reinforced concrete beams with signs of shear distress.
AB - Shear failures in reinforced concrete structures should be evaluated to ensure safety. Traditional evaluation methods for members with shear cracks include detailed analyses or expert opinion. This study uses machine learning to investigate how crack widths correlate with shear load, stiffness, and stirrup strain histories. A shear test database is assembled with 260 and 480 crack width measurements for rectangular slender beams with shear reinforcement ratios smaller and larger than the minimum required by ACI 318-19, respectively. Measured load-displacement relationships, stirrup strains, and crack widths were documented. Gaussian Process Regression (GPR), a machine learning method, is used to correlate crack width to shear history, stiffness, and stirrup strains considering beam design details. The three indicators (shear, stiffness, and stirrup strain histories) predicted based on test data given a crack width can be a rapid in-service performance evaluation tool for reinforced concrete beams with signs of shear distress.
KW - Crack Width
KW - Evaluation
KW - Gaussian Process
KW - Machine Learning
KW - Stiffness
KW - Stirrup Strain
UR - https://www.scopus.com/pages/publications/85164265047
U2 - 10.1007/978-3-031-32511-3_64
DO - 10.1007/978-3-031-32511-3_64
M3 - Conference contribution
AN - SCOPUS:85164265047
SN - 9783031325106
T3 - Lecture Notes in Civil Engineering
SP - 613
EP - 621
BT - Building for the Future
A2 - Ilki, Alper
A2 - Çavunt, Derya
A2 - Çavunt, Yavuz Selim
PB - Springer Science and Business Media Deutschland GmbH
T2 - International Symposium of the International Federation for Structural Concrete, fib Symposium 2023
Y2 - 5 June 2023 through 7 June 2023
ER -