基于深度学习和多目标优化的混凝土配合比设计及性能预测研究  

Concrete mix proportion design and performance prediction based on deep learning and multi-objective optimization

作　　者：李鹏远[1] 牛豪爽 刘毅豪 LI Pengyuan;NIU Haoshuang;LIU Yihao(Xuchang Vocational and Technical College,Xuchang 461000,Henan,China;School of Civil Engineering,Henan Polytechnic University,Zhengzhou 454003,Henan,China;Xuchang Key Laboratory of Digital Construction Technology and Equipment,Xuchang 461000,Henan,China)

机构地区：[1]许昌职业技术学院,河南许昌461000 [2]河南理工大学土木学院,河南郑州454003 [3]许昌市数字化建造技术与装备重点实验室,河南许昌461000

出　　处：《水利水电技术(中英文)》2025年第4期194-210,共17页Water Resources and Hydropower Engineering

基　　金：国家自然科学基金项目(42372331)。

摘　　要：【目的】混凝土作为国民经济建设的基石,其抗压强度的精确预测对于工程结构的设计和安全至关重要。通过深度神经网络(DNN)模型预测混凝土抗压强度,并提出RF-NSGA-II算法以优化混凝土配合比,实现抗压强度和成本的双重优化。【方法】构建了包含不同隐藏层和神经元数量的15种DNN模型架构,评估其性能并选取最佳模型,采用超参数优化策略和贝叶斯优化策略,提升DNN模型的预测性能,比较DNN模型与支持向量回归(SVR)和随机森林(RF)模型的性能。基于RF-NSGA-II算法,优化混凝土配合比,以满足强度要求和成本控制。【结果】研究结果显示,最优模型为3个隐藏层和64个隐藏单元(3L-64u)的DNN模型,经过优化DNN模型在MAE和MSE上分别降低了18%和27%,优化后的DNN模型相比SVR和RF模型在MAE和MSE上分别减少了4%和12%、11%和15%。【结论】通过案例验证,DNN3-L64u-BOP模型预测结果与试验值吻合良好,RF-NSGA-II算法优化的混凝土配合比方案有效降低成本,满足工程强度要求。基于贝叶斯优化的DNN模型能较好地预测混凝土抗压强度,RF-NSGA-II算法在多目标优化混凝土配合比方面展示出优异的性能,具有实际工程应用价值。[Objective]Concrete,as the cornerstone of national economic construction,necessitates the accurate prediction of its compressive strength for the design and safety of engineering structures.This study aims to predict concrete compressive strength using Deep Neural Network(DNN)models and proposes the RF-NSGA-II algorithm to optimize concrete mix proportions,achieving dual optimization of compressive strength and cost.[Methods]Fifteen DNN model architectures with different hidden layers and neuron numbers were constructed and evaluated for performance,selecting the best model.Hyperparameter optimization strategies and Bayesian optimization were employed to enhance the predictive performance of the DNN model.The performance of the DNN model was compared with Support Vector Regression(SVR)and Random Forest(RF)models.The RF-NSGA-II algorithm was used to optimize concrete mix proportions to meet strength requirements and cost control.[Results]The result showed that the optimal model had 3 hidden layers and 64 neurons(3L-64u).After optimization,the DNN model′s MAE and MSE decreased by 18%and 27%,respectively.Compared to the SVR and RF models,the optimized DNN model reduced MAE and MSE by 4%and 12%,and 11%and 15%,respectively.[Conclusion]Case validation demonstrated that the DNN3-L64u-BOP model′s predictions aligned well with experimental values,and the RF-NSGA-II algorithm effectively reduced costs while meeting engineering strength requirements.The Bayesian-optimized DNN model successfully predicted concrete compressive strength,and the RF-NSGA-II algorithm exhibited excellent performance in multi-objective optimization of concrete mix proportions,showing significant practical value in engineering applications.

关 键 词：混凝土 DNN 抗压强度 预测 优化 力学性能 影响因素 深度学习 

分 类 号：TU528[建筑科学—建筑技术科学] TP183[自动化与计算机技术—控制理论与控制工程]