一种改进ADAM-CNN模型的钢筋混凝土腐蚀检测方法  

An improved ADAM-CNN model for corrosion detection of reinforced concrete

作　　者：林旭梅 胡川 朱广辉 陈一戈 苗芳荣 LIN Xumei;HU Chuan;ZHU Guanghui;CHEN Yige;MIAO Fangrong(College of Information and Control Engineering,Qingdao University of Technology,Qingdao 266520,China)

机构地区：[1]青岛理工大学信息与控制工程学院,山东青岛266520

出　　处：《中国测试》2023年第2期8-14,共7页China Measurement & Test

基　　金：国家重点基础研究发展计划“973”项目(2015CB655100)。

摘　　要：针对钢筋混凝土腐蚀检测中单一传感器或检测方式获取锈蚀特征信息不足及准确率不高等问题,提出一种改进神经网络模型结构下对集成阳极梯、应变力、温度传感器的多传感器数据融合检测方式。首先将一维多传感器数据二维化,采用卷积核对特征信息滤波提取,提取后的信息平展后连接BP残差神经网络层,增强浅层低非线性度特征信息向深层网络的直接传递和重复利用,提高网络模型的拟合及泛化能力。针对ADAM优化算法在模型训练后期学习率可能震荡不收敛问题,引入分段学习率衰减策略抑制后期震荡,同时对二阶矩估计梯度变化进行调整,提高迭代收敛效率。仿真结果表明,改进后的ADAM-CNN算法模型具有更好的分类性能,在钢筋腐蚀样本测试集上的平均准确率为96.2%。In order to solve the problems of insufficient information and low accuracy of single sensor or detection method in corrosion detection of reinforced concrete,a multi-sensor data fusion detection method based on improved neural network model structure is proposed,which integrates anode ladder,strain force and temperature sensors.Firstly,the one-dimensional multi-sensor data is transformed into two dimensions,and the feature information is extracted by convolution check filter,the extracted information is flattened and connected with BP residual neural network layer to enhance the direct transmission and reuse of shallow low nonlinear feature information to deep network,and improve the fitting and generalization ability of network model.Aiming at the problem that the learning rate of ADAM algorithm may fluctuate and not converge in the later stage of training,the piecewise learning rate attenuation strategy is introduced to suppress the later stage of oscillation,and the gradient change of the second moment estimation is adjusted to improve the iterative convergence efficiency.The simulation results show that the improved ADAM-CNN algorithm model has better classification performance,with an average accuracy of 96.2%on the test set of reinforcement corrosion samples.

关 键 词：钢筋混凝土 腐蚀检测 多传感器检测 残差神经网络 ADAM算法 学习率衰减 

分 类 号：TU528.33[建筑科学—建筑技术科学] TB9[一般工业技术—计量学]