针对混凝土材料力学性能精准预测的问题, 提出了一种粒子群算法(PSO)优化的径向基函数(RBF)与自编码器(AE)融合预测模型(RBF-PSO-AE), 对混凝土断裂能、失稳韧度和起裂韧度等参数进行预测分析。首先运用RBF结合AE使用交叉熵损失函数对数据特征降维加速收敛, 其次利用PSO快速优化模型的网络最佳权值, 最后将该模型与多种单一预测模型进行实验比较。实验结果表明该算法模型预测精确度和泛化能力提升明显, 实现大于99.99%的预测精度, 均方根误差0.006%, 能有效减少混凝土力学性能预测的误差, 具有良好的鲁棒性。
Abstract
Aiming at the problem of accurate prediction of concrete material mechanical properties, a particle swarm optimization (PSO) optimization of radial basis function (RBF) and the autoencoder(AE) fusion predicting model (RBF-PSO-AE) is proposed to predict and analyz the fracture energy, instability toughness and crack initiation toughness of concrete. Firstly, RBF and AE are used to accelerate the convergence of data feature dimensionality reduction by using cross entropy loss function. Secondly, PSO is used to quickly optimize the network optimal weight of the model. Finally, the model is compared with a variety of single prediction models. The experimental results show that the prediction accuracy and generalization ability of the algorithm model are significantly improved, and the prediction accuracy is greater than 99.99%, with a root mean square error of 0.006%. It can effectively reduce the error of concrete mechanical property prediction, and has good robustness.
关键词
计量学 /
混凝土材料 /
力学性能预测模型 /
径向基函数 /
粒子群算法 /
自编码器
Key words
metrology /
concrete materials /
mechanical prediction model /
radial basis function /
particle swarm optimization /
autoencoder
{{custom_sec.title}}
{{custom_sec.title}}
{{custom_sec.content}}
参考文献
［1］郑淳. 基于断裂力学的公路钢桥疲劳寿命可靠度方法研究［D］. 广州:华南理工大学, 2013.
［2］杜鹏. 多因素耦合作用下混凝土的冻融损伤模型与寿命预测［D］. 北京:中国建筑材料科学研究总院, 2014.
［3］杨晓明, 吴天宇, 时丹. 基于人工神经网络的混凝土实时强度影响因素敏感性分析［J］. 混凝土, 2014(11):15-18.
Yang X M, Wu T Y, Shi D. Sensitivity Analysis on Influence Factors to Concrete Real-time Strength Based on Artificial Neural Network［J］. Concrete, 2014(11):15-18.
［4］张研, 吴哲康, 王鹏鹏, 等. 基于PCA-RVM的露天矿边坡稳定性预测模型［J］. 矿业研究与开发, 2021, 41(3) :13-18.
Zhang Y, Wu Z K, Wang P P, et al. Prediction Model of Slope Stability of Open Pit Mine Based on PCA-RVM［J］. Mining Research and Development, 2021, 41(3):13-18.
［5］Hochreiter S, Schmidhuber J . Long Short-Term Memory［J］. Neural Computation, 1997, 9(8):1735-1780.
［6］Vaswani A, Shazeer N, Parmar N, et al. Attention is all you need［C］//Advances in neural information processing systems. NewYork,United States, 2017:5998-6008.
［7］Li S Y, Jin X Y, Xuan Y, et al. Enhancing the locality and breaking the memory bottleneck of transformer on time series forecasting［C］// 33rd Conference on Neural Information Processing Systems (NeurIPS 2019). Vancouver, Canada, 2019:1-11.
［8］米正祥, 李庆斌, 胡昱. 预测混凝土断裂参数的最佳拟合成熟度法［J］. 水力发电学报, 2022, 41(2):92-101.
Mi Z X, Li Q B, Hu Y. The best-fit maturity method for predicting the fracture properties of Concrete［J］. Journal of Hydroelectric Engineering, 2022, 41(2):92-101.
［9］Cecati C, Kolbusz J, Róycki P, et al. A Novel RBF Training Algorithm for Short-Term Electric Load Forecasting and Comparative Studies［J］. IEEE Transactions on Industrial Electronics, 2015, 10(62):6519-6529, 2015.
［10］Xia L Q, Hu P H, Ma K L, et al. Research on Measurement Modeling of Spherical Joint Rotation Angle Based on RBF-ELM Network［J］. IEEE Sensors Journal, 2021, 21(20):23118-23124.
［11］金秀章, 李京. 基于互信息PSO-LSSVM的SO2浓度预测［J］.计量学报, 2021, 42(5):675-680.
Jin X Z, Li J. Prediction of SO2 Concentration Based on Mutual Information PSO-LSSVM［J］. Acta Metrologica Sinica, 2021, 42(5):675-680.
［12］滕峰成, 王珊珊, 杨雪璠, 等. 基于PSO算法的GMM改进J-A磁滞模型的参数辨识与验证［J］.计量学报, 2021, 42(9):1193-1199.
Teng F C, Wang S S, Yang X P, et al. Parameter Identification and Verification of Improved J-A Hysteresis Model of GMM Based on PSO Algorithm［J］. Acta Metrologica Sinica, 2021, 42(9):1193-1199.
［13］陈颖, 崔行宁, 肖春艳, 等. 复杂水质环境下基于PSO-ELM的BOD快速检测［J］.计量学报, 2021, 42(1):105-110.
Chen Y, Cui X N, Xiao C Y, et al. Rapid Detection of BOD Based on PSO-ELM in Complex Water Quality Environment［J］. Acta Metrologica Sinica, 2021, 42(1):105-110.
［14］朱建新, 吕宝林, 乔松, 等. 基于主成分分析及多维高斯贝叶斯的超声流量计故障智能诊断方法［J］. 计量学报, 2020, 41(12):1494-1499.
Zhu J X, Lv B L, Qiao S, et al. Application of Primary Component Analysis and Multivariate Gaussian Bayesian Method on Intelligent Failure Diagnosis of Ultrasonic Flowmeter［J］. Acta Metrologica Sinica, 2020, 41(12):1494-1499.
［15］魏立新, 王恒, 孙浩, 等. 基于改进深度信念网络训练的冷轧轧制力预报［J］.计量学报, 2021, 42(7):906-912.
Wei L X, Wang H, Sun H, et al. Research on Cold Rolling Force Prediction Model Based on Improved Deep Belief Network［J］. Acta Metrologica Sinica, 2021, 42(7):906-912.
［16］何群, 王红, 江国乾, 等.基于相关主成分分析和极限学习机的风电机组主轴承状态监测研究［J］. 计量学报, 2018, 39(1):89-93.
He Q, Wang H, Jiang G Q, et al. Research of Wind Turbine Main Bearing Condition Monitoring Based on Correlation PCA and ELM［J］. Acta Metrologica Sinica, 2018, 39(1):89-93.
PDF(3018 KB)
