基于BP神经网络的改进型新奇检测技术诊断大跨度拱桥异常状态

Abnormality Identification of Large-Span Arch Bridge Based on BP Neural Improved Novelty Detection Technique

为了诊断大跨度拱桥的异常状态,使用了基于BP神经网络的改进型新奇检测技术的方法,该方法通过BP神经网络对大量实测数据进行训练,得到桥梁状态正常时的新奇指标,并确定阈值,通过阈值判定是否发生异常.经实际分析以及实测数据的验证,该方法可以较准确地识别大型桥梁异常情况,并可以定位异常区域,避免了模型误差的影响,大大提高了方法的实用价值,同时降低了漏警、虚报警,使识别结果更为准确,更符合实际要求.

In order to identify abnormal state of large-span arch bridge,an improved novelty detection technique based on BP neural algorithm was used.The method used BP neural to train a large number of measured data and got the novelty index when bridge state was normal.Then the threshold and whether the bridge abnormal or not was determined.Combined with concrete situation and data analysis,the method can accurately identify the abnormal region.The method,which avoids the effect of the model error,can greatly improve the practical value,decrease false alarm,make the identification result more accurate and more practical.