The dynamic strain monitoring system is an important means to reflect the deformation and evolution of the structure in the field of traffic and transportation visually. The exact measurement of dynamic strain measurement system is inseparable from the traceability system of high-precision strain gauge. In this paper, the resistance strain monitoring system installed on the web surface of the main girder box of Jiujiang bridge is calibrated online by using the parallel method at a similar location. By means of dynamic strain measurement system calibration sample signal feature extraction, put forward online calibration method based on neural network algorithm for dynamic strain signaler, move the noise of passive nonlinear signal under the circumstance of incentives, set up and designed a calibration method of HOABC-NN for dynamic signal analysis of on-line monitoring system. After verification, it is found that the algorithm is superior to the traditional neural network training method, the calibration precision is improved obviously, and can support the dynamic strain of bridge monitoring system online calibration

动态应变监测系统是在交通运输领域可视化反映结构变形和演化的重要手段。动态应变测量系统的精确测量离不开高精度应变仪的可追溯系统。本文通过在相似位置使用并行方法，在线校准安装在九江大桥主梁箱腹表面上的电阻应变监测系统。通过动态应变测量系统标定样本信号特征提取，提出了基于神经网络算法的动态应变信号在线标定方法，在激励情况下移动了无源非线性信号的噪声，建立并设计了一种用于在线监测系统动态信号分析的HOABC-NN标定方法。经过验证，发现该算法优于传统的神经网络训练方法，标定精度明显提高，可以支持桥梁监控系统在线应变的动态标定。