【摘要】 利用三层BP神经网络预测海洋环境因素对材料的腐蚀速率的影响。结合实测的pH值、温度、溶解氧、盐度、生物附着等影响因素,分析了上述环境因素对平台钢腐蚀的影响,建立环境因素与腐蚀速率之间的映射关系,预测了平台钢在海洋环境中的腐蚀速率。结果表明,全浸区腐蚀速率预测误差为6.95%,潮差带腐蚀速率预测误差为4.2%,预测精度较高。说明利用三层BP神经网络预测钢在海水中腐蚀速率技术可行,具有较高的预测精度和应用价值。更多还原

【Abstract】 We introduced the methodology to study relationship between steel corrosion and marine environmental fac-tors and to predict of steel corrosion rates with three-layer BP neural network.With the in situ measurements of pHs,wa-ter temperatures,dissolved oxygen,salinities and bio-fouling,the effects of marine environmental factors on steel corro-sion were analyzed and sorted in a descending sequence.With a three-layer BP neural network,the corrosion rates of steel in seawater were predicted with an error of 6.95% in submerged zones and 4.2% in tidal zones.The results show that prediction with the neural network was feasible,producing good prediction accuracy and value.更多还原