| 干湿循环-硫酸盐作用的混凝土中硫酸根离子分布预测 |
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| 引用本文： | 陈记豪,钱晓军,赵顺波.干湿循环-硫酸盐作用的混凝土中硫酸根离子分布预测[J].河南科学,2008,26(7). |
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| 作者姓名： | 陈记豪 钱晓军 赵顺波 |
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| 作者单位： | 华北水利水电学院,土木与交通学院,郑州,450011 |
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| 基金项目： | 河南省杰出青年科学基金
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河南省高校创新人才培养工程培养对象基金 |
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| 摘 要： | 以影响混凝土中硫酸根离子分布的混凝土水灰比、环境中硫酸根离子浓度、侵蚀龄期和侵蚀深度4种影响因素为参数,以试验数据为训练样本,基于BP神经网络算法,建立了干湿循环-硫酸盐侵蚀作用下混凝土中硫酸根离子分布拟合及预测模型.经验证,该模型对硫酸盐侵蚀作用下混凝土中硫酸根离子分布具有良好的拟合及预测效果.
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| 关 键 词： | 混凝土 硫酸根离子分布 干湿循环 BP神经网络 预测 |
Neural Network Prediction of Sulfate-Ion Distribution in Concrete Suffered Dry-Wet Cycle |
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| Chen Jihao,Qian Xiaojun,Zhao Shunbo.Neural Network Prediction of Sulfate-Ion Distribution in Concrete Suffered Dry-Wet Cycle[J].Henan Science,2008,26(7). |
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| Authors: | Chen Jihao Qian Xiaojun Zhao Shunbo |
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| Institution: | Chen Jihao,Qian Xiaojun,Zhao Shunbo(School of Civil Engineering , Communication,North China University of Water Conservancy , Electric Power,Zhengzhou 450011,China) |
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| Abstract: | Take the water-cement ratio,the environmental sulfate concentration,the erosion age and the erosion depth as parameters,and take the test data as training samples,the goodness-in-fit and forecasting model of sulfateion distribution in concrete suffered dry-wet cycle and sulfate corrosion is proposed by BP neural network algorithm.It is proved that the model of sulfate-ion distribution in concrete has the good fitting and the forecasting effects. |
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| Keywords: | concrete sulfate-ion distribution dry-wet cycle BP network forecast |
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