Journal of Jilin University(Engineering and Technology Edition) ›› 2025, Vol. 55 ›› Issue (7): 2276-2285.doi: 10.13229/j.cnki.jdxbgxb.20231096

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Prediction of reinforced concrete durability based on whale optimization algorithm-back propagation neural network

Qiong FENG1,2(),Xiao-yang XIE1,Peng-hui WANG3,Hong-xia QIAO1,2,Yun-xia MA1   

  1. 1.School of Civil Engineering，Lanzhou University of Technology，Lanzhou 730050，China
    2.Western Ministry of Civil Engineering Disaster Prevention and Mitigation Engineering Research Center，Lanzhou University of Technology，Lanzhou 730050，China
    3.Guangdong Provincial Key Laboratory of Durability of Binhai Civil Engineering，Shenzhen University，Shenzhen 518060，China
  • Received:2023-09-23 Online:2025-07-01 Published:2025-09-12

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Abstract:

To enhance the durability of reinforced concrete through mix design， a whale optimization algorithm-back propagation neural network model with a topology structure of 6-14-2 is designed. The model dataset comprises 100 2sets of data， with60×2 sets used for model establishment and 40*2 sets for model validation. By comparing the predictive performance of the backpropagation neural network model with the whale optimization algorithm-back propagation neural network model， it is evident that the whale optimization algorithm significantly improves the predictive ability of the back propagation neural network model. The whale optimization algorithm-back propagation neural network model predicts the mean values of T1 performance indicators as follows： R2=0.90， RMSE=33.92， MAPE=0.06， MAE=27.31； the mean value of T2 performance indicators as follows： R2=0.90， RMSE=29.75， MAPE=0.04， MAE=23.81. Therefore， the whale optimization algorithm-back propagation neural network model can effectively predict the durability of reinforced concrete.

CLC Number: 

  • TU528
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