基于融合RBF-PSO-AE算法的混凝土力学性能预测

doi:10.3969/j.issn.1000-1158.2022.11.12

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摘要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。

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	黄晨亮
	郭力群
	吕阳阳
	刘畅

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

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.

Key words： metrology concrete materials mechanical prediction model radial basis function particle swarm optimization autoencoder

收稿日期: 2022-03-01 发布日期: 2022-11-14

PACS:

TB93

基金资助:国家自然科学基金(51778249)

作者简介: 黄晨亮(1984-), 男, 河南郑州人, 高级工程师, 主要从事岩土工程技术方面的研究。Email:clhuang2021@163.com

引用本文:

黄晨亮,郭力群,吕阳阳,刘畅. 基于融合RBF-PSO-AE算法的混凝土力学性能预测[J]. 计量学报, 2022, 43(11): 1464-1469.
HUANG Chen-liang,GUO Li-qun,Lü Yang-yang,LIU Chang. Prediction of Concrete Mechanical Properties Based on Fusion RBF-PSO-AE Algorithm. Acta Metrologica Sinica, 2022, 43(11): 1464-1469.

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