Chained machine learning model for predicting load capacity and ductility of steel fiber–reinforced concrete beams
Institute of Fluid Flow-Machinery · Polish Academy of Sciences · +4 more institutions
Abstract
One of the main issues associated with steel fiber–reinforced concrete (SFRC) beams is the ability to anticipate their flexural response. With a comprehensive grid search, several stacked models (i.e., chained, parallel) consisting of various machine learning (ML) algorithms and artificial neural networks (ANNs) were developed to predict the flexural response of SFRC beams. The flexural performance of SFRC beams under bending was assessed based on 193 experimental specimens from real-life beam models. The ML techniques were applied to predict SFRC beam responses to bending load as functions of the steel fiber properties, concrete elastic modulus, beam dimensions, and reinforcement details. The accuracy of the…
Citation impact
- FWCI
- 29.29
- Percentile
- 100%
- References
- 83
Authors
5- TSTorkan Shafighfard
Institute of Fluid Flow-Machinery, Polish Academy of Sciences
- FKFarzin Kazemi
Gdańsk University of Technology, University College London
- FBFaramarz BagherzadehCorresponding
University of Bremen
- MMMagdalena Mieloszyk
Institute of Fluid Flow-Machinery, Polish Academy of Sciences
- DYDoo‐Yeol YooCorresponding
Yonsei University
Topics & keywords
- Structural engineering
- Flexural strength
- Beam (structure)
- Mean squared error
- Artificial neural network
- Bending
- Deflection (physics)
- Reinforcement