Prediction of the Postfire Flexural Capacity of RC Beam Using GA-BPNN Machine Learning
Publication: Journal of Performance of Constructed Facilities
Volume 34, Issue 6
Abstract
To accurately predict the flexural capacity of postfire RC beams is imperative for fire safety design. In this paper, the residual flexural capacity of postfire RC beams is predicted based on a back-propagation (BP) neural network (NN) optimized by a genetic algorithm (GA). First, the temperature distribution of the beams was determined using the finite-element analysis software ABAQUS version 6.14-4, and the strength reduction factor of materials was determined. The flexural capacity of the RC beams after fire was calculated by the flexural strength reduction calculation model. The model was used to generate the training data for the NN. To enable machine learning, 480 data sets were produced, of which 360 were used to train the network; the remaining 120 were used to test the network. The predictive models were constructed using BPNN and GA-BPNN. The prediction accuracy was evaluated by comparing the predicted and target values. The comparison showed that the GA-BPNN has a faster convergence speed and higher stability and can reach the goal more times, reducing the possibility of BPNN falling into the local optimum and achieving the global optimum. The proposed GA-BPNN model for predicting the flexural capacity of postfire RC beams provides a new approach for design practice.
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Data Availability Statement
All data and code for the machine learning that support the findings of this study are available from the corresponding author upon reasonable request:
Training data for machine learning
Prediction result data for machine learning
Code for machine learning.
Acknowledgments
This research was financially supported by the Foundation of China Scholarship Council (No. 201805975002) National Natural Science Foundation of China (Grant No. 51678274), Science and Technological Planning Project of Ministry of Housing and Urban–Rural Development of the People’s Republic of China (No. 2017-K9-047). The authors wish to acknowledge the sponsors. However, any opinions, findings, conclusions and recommendations presented in this paper are those of the authors and do not necessarily reflect the views of the sponsors.
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Published In
Journal of Performance of Constructed Facilities
Volume 34 • Issue 6 • December 2020
Copyright
© 2020 American Society of Civil Engineers.
History
Received: Dec 24, 2019
Accepted: Jun 2, 2020
Published online: Aug 21, 2020
Published in print: Dec 1, 2020
Discussion open until: Jan 21, 2021
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