吉林大学学报(工学版) ›› 2025, Vol. 55 ›› Issue (7): 2276-2285.doi: 10.13229/j.cnki.jdxbgxb.20231096

• 交通运输工程·土木工程 • 上一篇    下一篇

基于鲸鱼优化算法-反向传播神经网络的钢筋混凝土耐久性预测

冯琼1,2(),谢晓扬1,王鹏辉3,乔宏霞1,2,马云霞1   

  1. 1.兰州理工大学 土木工程学院，兰州 730050
    2.兰州理工大学 西部土木工程防灾减灾教育部工程研究中心，兰州 730050
    3.深圳大学 广东省滨海土木工程耐久性重点实验室，广东 深圳 518060
  • 收稿日期:2023-09-23 出版日期:2025-07-01 发布日期:2025-09-12
  • 作者简介:冯琼（1985-），女，副教授，博士. 研究方向：钢筋混凝土耐久性.E-mail： fengqiong.1985@163.com
  • 基金资助:
    国家自然科学基金项目(52008196);国家自然科学基金项目(U21A20150);国家自然科学基金项目(52178216);甘肃省科技计划项目(23JRRA799)

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

PDF (PC)

36

摘要：

本文研究通过配合比设计提高钢筋混凝土的耐久性，设计拓扑结构为6-14-2的鲸鱼优化算法-反向传播神经网络模型。模型数据集共100*2组数据，其中60*2组数据用于建立模型，40*2组数据用于验证模型。通过对比反向传播神经网络模型与鲸鱼优化算法-反向传播神经网络模型的预测性能，表明鲸鱼优化算法可以显著提高反向传播神经网络模型的预测性能。鲸鱼优化算法-反向传播神经网络模型预测T1性能指标的均值分别为R2=0.90、RMSE=33.92、MAPE=0.06、MAE=27.31；T2性能指标的均值分别为R2=0.90、RMSE=29.75、MAPE=0.04、MAE=23.81。可知，鲸鱼优化算法-反向传播神经网络模型可以有效预测钢筋混凝土的耐久性。

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.

中图分类号: 

  • TU528
[1] Ahmed A A, Shakouri M, Trejo D, et al. Effect of curing temperature and water-to-cement ratio on corrosion of steel in calcium aluminate cement concrete[J]. Construction and Building Materials, 2022, 350: No.128875.
[2] Wang P, Ke L Y W, Wu H L, et al. Effects of water-to-cement ratio on the performance of concrete and embedded GFRP reinforcement[J]. Construction and Building Materials, 2022, 351: No.128833.
[3] Liang Y, Wang L. Effect of water‐to‐cement ratio on service life of reinforced concrete structures in chloride environment[J]. Structural Concrete, 2021, 22(5): 2748-2760.
[4] Kurda R, Silvestre J D, De Brito J, et al. Optimizing recycled concrete containing high volume of fly ash in terms of the embodied energy and chloride ion resistance[J]. Journal of Cleaner Production, 2018, 194: 735-750.
[5] Qin Y, Guan K, Kou J, et al. Durability evaluation and life prediction of fiber concrete with fly ash based on entropy weight method and grey theory[J]. Construction and Building Materials, 2022, 327: No.126918.
[6] Yang R, Yu R, Shui Z, et al. Environmental and economical friendly ultra-high performance-concrete incorporating appropriate quarry-stone powders[J]. Journal of Cleaner Production, 2020, 260: No. 121112.
[7] 王甲春, 阎培渝. 海洋环境下钢筋混凝土中钢筋锈蚀的概率[J].吉林大学学报:工学版, 2014, 44(2):352-357.
[7] Wang Jia-chun, Yan Pei-yu. Probability of corrosion of steel reinforcement in reinforced concrete in marine environment[J]. Journal of Jilin University(Engineering and Technology Edition), 2014,44(2): 352-357.
[8] 王鹏辉, 乔宏霞, 冯琼, 等. 氯氧镁涂层钢筋混凝土两重因素耦合作用下的耐久性模型[J]. 吉林大学学报: 工学版, 2020, 50(1): 191-201.
[8] Wang Peng-hui, Qiao Hong-xia, Feng Qiong, et al. Durability modeling of magnesium chloride coated reinforced concrete under two-factor coupling[J]. Journal of Jilin University(Engineering and Technology Edition), 2020, 50(1): 191-201.
[9] 冯琼, 田浩正, 乔宏霞, 等. 自然暴露与盐雾加速环境下钢筋混凝土劣化规律及等效关系[J].吉林大学学报: 工学版,2023,53(12): 1-12.
[9] Feng Qiong, Tian Hao-zheng, Qiao Hong-xia,et al. Deterioration law and equivalent relationship of reinforced concrete under natural exposure and salt spray accelerated environment[J]. Journal of Jilin University(Engineering and Technology Edition), 2023,53(12): 1-12.
[10] Tien Bui D, Abdullahi M M, Ghareh S, et al. Fine-tuning of neural computing using whale optimization algorithm for predicting compressive strength of concrete[J]. Engineering with Computers, 2021, 37(1): 701-712.
[11] Shi B, Wang X, Dong Q, et al. Voids prediction beneath cement concrete slabs using a FEM-ANN method[J]. International Journal of Pavement Engineering, 2023, 24(1): No.2191198.
[12] 慕儒. 冻融循环与外部弯曲应力、盐溶液复合作用下混凝土的耐久性与寿命预测[D].南京: 东南大学材料科学与工程系, 2000.
[12] Mu Ru. Durability and life prediction of concrete under the composite effect of freezing and thawing cycles with external bending stress and salt solution[D]. Nanjing:Department of Materials Science and Engineering, Southeast University, 2000.
[13] Nian T, Li J, Li P, et al. Method to predict the interlayer shear strength of asphalt pavement based on improved back propagation neural network[J]. Construction and Building Materials, 2022, 351: No.128969.
[14] Giergiczny Z. Fly ash and slag[J]. Cement and Concrete Research, 2019, 124: No.105826.
[15] Scrivener K L, Juilland P, Monteiro P J M. Advances in understanding hydration of Portland cement[J]. Cement and Concrete Research, 2015, 78: 38-56.
[16] Farhan N A, Sheikh M N, Hadi M N S. Investigation of engineering properties of normal and high strength fly ash based geopolymer and alkali-activated slag concrete compared to ordinary Portland cement concrete[J]. Construction and Building Materials, 2019, 196: 26-42.
[17] Moropoulou A, Polikreti K, Bakolas A, et al. Correlation of physicochemical and mechanical properties of historical mortars and classification by multivariate statistics[J]. Cement and Concrete Research, 2003, 33(6): 891-898.
[18] Shamsabadi E A, Roshan N, Hadigheh S A, et al. Machine learning-based compressive strength modelling of concrete incorporating waste marble powder[J]. Construction and Building Materials, 2022, 324: No.126592.
[19] Rahmani E, Kazem Sharbatdar M, Beygi M H A. A comprehensive investigation into the effect of water to cement ratios and cement contents on the physical and mechanical properties of roller compacted concrete pavement(RCCP)[J]. Construction and Building Materials, 2020, 253: No.119177.
[20] ?ahin R, Demirbo?a R, Uysal H, et al. The effects of different cement dosages, slumps and pumice aggregate ratios on the compressive strength and densities of concrete[J]. Cement and Concrete Research, 2003, 33(8): 1245-1249.
[21] Meddah M S, Zitouni S, Belaabes S. Effect of content and particle size distribution of coarse aggregate on the compressive strength of concrete[J]. Construction and Building Materials, 2010, 24(4): 505-512.
[22] Cetin A, Carrasquillo R L. High-performance concrete: Influence of coarse aggregates on mechanical properties[J]. Materials Journal, 1998, 95(3): 252-261.
[23] Hefni Y, El Zaher Y A, Wahab M A. Influence of activation of fly ash on the mechanical properties of concrete[J]. Construction and Building Materials, 2018, 172: 728-734.
[24] Barnett S J, Soutsos M N, Millard S G, et al. Strength development of mortars containing ground granulated blast-furnace slag: Effect of curing temperature and determination of apparent activation energies[J]. Cement and Concrete Research, 2006, 36(3): 434-440.
[25] 曲广雷, 闫宗伟, 郑木莲, 等.基于神经网络与回归分析的多孔混凝土性能预测[J].吉林大学学报:工学版, 2023, 53(9): 1-13.
[25] Qu Guang-lei, Yan Zong-wei, Zheng Mu-lian, et al. Prediction of porous concrete properties based on neural network and regression analysis[J]. Journal of Jilin University(Engineering and Technology Edition), 2023, 53(9): 1-13.
[1] 田耀刚,蒋静,赵成,杨小敏,张军,贾侃. 水性环氧树脂改性高早强砂浆的耐温机制[J]. 吉林大学学报(工学版), 2025, 55(7): 2203-2211.
[2] 袁杰,王军博,陈歆,黄馨,张傲翔,崔安琪. 人工智能在超高性能混凝土中的应用研究进展[J]. 吉林大学学报(工学版), 2025, 55(3): 771-789.
[3] 何子明,申爱琴,王路生,郭寅川,何江飞. 再生骨料强化技术及对再生混凝土性能影响研究综述[J]. 吉林大学学报(工学版), 2025, 55(3): 790-810.
[4] 张亮亮,程桦,王晓健. 常规三轴压缩下高强混凝土能量演化和破坏准则[J]. 吉林大学学报(工学版), 2025, 55(3): 974-985.
[5] 于本田,李彦宵,张占旭,苏俊辉,谢超,张凯. 不同石粉及掺量对高延性工程水泥基复合材料的性能影响[J]. 吉林大学学报(工学版), 2024, 54(10): 2908-2921.
[6] 许颖,樊悦,王青原,张振宇. 聚丙烯纤维混凝土断裂韧度试验与数值分析[J]. 吉林大学学报(工学版), 2024, 54(10): 2884-2896.
[7] 刘海峰,陶仁光,车佳玲,杨维武,朱立晨. 荷载和高温对沙漠砂混凝土单轴抗压性能的影响[J]. 吉林大学学报(工学版), 2024, 54(9): 2609-2619.
[8] 冯琼,田浩正,乔宏霞,念腾飞,韩文文. 自然暴露与盐雾加速环境下钢筋混凝土劣化规律及等效关系[J]. 吉林大学学报(工学版), 2024, 54(2): 494-505.
[9] 魏丽丽,胡明玉. 砂浆碱集料反应细观数值模拟[J]. 吉林大学学报(工学版), 2023, 53(12): 3501-3507.
[10] 秦拥军,陈楠,蔺鹏杰,于江. 掺锂渣再生混凝土三点弯曲梁双K断裂特性[J]. 吉林大学学报(工学版), 2021, 51(6): 2121-2127.
[11] 李艺,苏悦琦. 基于不同孔径范围的碳化作用下纤维混凝土的气体渗透性能和细观结构[J]. 吉林大学学报(工学版), 2021, 51(4): 1287-1295.
[12] 张广泰,张路杨,邢国华,曹银龙,易宝. 钢-聚丙烯混杂纤维混凝土剪力墙抗震性能[J]. 吉林大学学报(工学版), 2021, 51(3): 946-955.
[13] 刘寒冰,高鑫,宫亚峰,刘诗琪,李文俊. 表面处理对玄武岩纤维活性粉末混凝土力学性能的影响及断裂特性[J]. 吉林大学学报(工学版), 2021, 51(3): 936-945.
[14] 何娟,程从密,杨毅男,张亚芳,钟明峰. 湿热养护时掺合料对玻纤增强水泥性能的影响[J]. 吉林大学学报(工学版), 2020, 50(2): 648-653.
[15] 王鹏辉,乔宏霞,冯琼,曹辉,温少勇. 氯氧镁涂层钢筋混凝土两重因素耦合作用下的耐久性模型[J]. 吉林大学学报(工学版), 2020, 50(1): 191-201.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
版权所有 © 《吉林大学学报(工学版)》编辑部
地址：长春市人民大街5988号　电话：+86-431-85095297　传真：+86-431-85094074　E-mail: xbgxb@jlu.edu.cn
本系统由北京玛格泰克科技发展有限公司设计开发