基于神经网络的管道沿线土壤腐蚀态势评价

利用神经网络极强的非线性逼近特点,采用神经网络评价管道土壤腐蚀态势。介绍了基于BP神经网络的管道土壤腐蚀态势评价模型建立的方法和过程,在实例计算中考虑土壤pH值、土壤电阻率、土壤氧化还原电位和电解失重四个主要因素,选取9个学习样本对BP神经网络进行训练,分别用训练好的BP神经网络和模糊综合评价对现场管道6个实测点进行腐蚀态势评价,结果表明,两种方法得到的评价结果完全一致,该方法可以用于对管道沿线土壤腐蚀态势的评价。

Artificial Neural Network-based Evaluation on Soil Corrosion Situation along Pipelines

The remarkable nonlinear proximity is one of the characteristics of artificial neural network, which is utilized to evaluate soil corrosion situation of pipelines. This paper introduces the methodology and process in establishing the artificial neural network-based evaluation model on soil corrosion situation of pipelines. In a case, 4 factors, that is pH value of soil, soil's resistivity, oxidation-reduction potential of soil and electrolytic weightlessness, are taken into account in calculation. 9 studying samples selected are used to train the BP artificial neural network,and then,to evaluate the corrosion situation on 6 field test points of pipeline with trained BP artificial neural network and fuzzy comprehensive assessment method. Evaluation results show that the evaluation results obtained from the above two methods are entirely fitted each other, and suitable for the evaluation of soil corrosion situation of pipelines.