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Prediction of the post-fire flexural capacity of RC beam using GA-BPNN Machine Learning

Cai, B., Pan, G. & Fu, F. ORCID: 0000-0002-9176-8159 (2020). Prediction of the post-fire flexural capacity of RC beam using GA-BPNN Machine Learning. Journal of Performance of Constructed Facilities, 34(6), article number 04020105. doi: 10.1061/(ASCE)CF.1943-5509.0001514


To accurately predict the flexural capacity of post-fire RC beams is imperative for fire safety design. In this paper, the residual flexural capacity of post-fire 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, and the strength reduction factor of materials was determined. The flexural capacity of the RC beams after fire is calculated by the flexural strength reduction calculation model. The model is used to generate the training data for the NN. To enable machine learning, 480 datasets are produced, of which 360 datasets are used to train the network; the remaining 120 datasets are used to test the network. The predictive models are constructed using BPNN and GA-BPNN respectively. The prediction accuracy is evaluated by comparing the predicted values and the target values. The comparison shows that the GA-BPNN has a faster convergence speed, 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 post-fire RC beams provides a new approach for design practice.

Publication Type: Article
Additional Information: Copyright ASCE, 2020. This material may be downloaded for personal use only. Any other use requires prior permission of the American Society of Civil Engineers. This material may be found at
Publisher Keywords: reinforced concrete, fire, flexural capacity, BP neural network, GA-BP neural network, prediction
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
T Technology > TA Engineering (General). Civil engineering (General)
Departments: School of Science & Technology > Engineering
SWORD Depositor:
[thumbnail of GA_BP-Manuscript-final 2020-05-28-City deposit.pdf]
Text - Accepted Version
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