首页|基于灰色关联度-BP神经网络的混凝土抗腐蚀性能研究

基于灰色关联度-BP神经网络的混凝土抗腐蚀性能研究

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利用灰色关联度法对不同强度等级不同腐蚀环境下混凝土抗腐蚀性进行分析.利用神经网络对混凝土抗腐蚀性试验数据进行训练,并对未参与训练的数据进行预测验证.研究结果表明:砂子、水泥、碎石及防腐涂层等 4 种因素与抗腐蚀性的相关性最高,其余影响因素对抗腐蚀性影响则相对较小.BP神经网络预测的抗腐蚀性参数与试验所测数据误差基本在 10%以内,满足要求.
Study on Corrosion Resistance of Concrete Based on Grey Relational Degree-BP Neural Network
The corrosion resistance of concrete under different strength levels and different corrosion environments was analyzed by using grey relational degree method.The neural network is used to train the concrete corrosion resistance test data,and to predict and verify the data that is not involved in the training.The results show that sand,cement,gravel and anti-corrosion coating have the highest correlation with corrosion resistance,while the other factors have relatively little effect on corrosion resistance.The error between the corrosion resistance parameters predicted by BP neural network and those measured by experiment is basically less than 10%,which can basically meet the requirements.The test and prediction results can provide reference for the prediction of compressive strength of concrete under different strength grades and different corrosion environments in saline soil area.

corrosion resistancegrey correlation degreeBP neural networkcompressive strength

李渊、景涛、申铁军

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山西省太原市煤炭工业太原设计研究院集团有限公司,山西太原 030024

山西路桥建设集团有限公司,山西 太原 030006

抗腐蚀 灰色关联度 BP神经网络 抗压强度

2024

建材技术与应用
山西综合职业技术学院

建材技术与应用

影响因子:0.74
ISSN:1009-9441
年,卷(期):2024.(2)
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