基于改进PSO-FNN算法的钢筋混凝土腐蚀检测研究  

Research on reinforced concrete corrosion detection based on improved PSO-FNN algorithm

作　　者：林旭梅 刘帅 石智梁 LIN Xumei;LIU Shuai;SHI Zhiliang(School of Information and Control Engineering,Qingdao University of Technology,Qingdao 266000,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266000

出　　处：《中国测试》2020年第12期149-155,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：针对传统粒子群算法(PSO)在处理复杂搜索问题中容易产生早熟收敛,局部寻优能力较差等问题,提出PSO算法中惯性因子的自适应调整方法,将改进的PSO算法优化模糊神经网络(FNN),并将改进的PSO-FNN算法应用于多传感器信息融合的钢筋混凝土腐蚀检测中,检测系统包括pH值传感器、氯离子传感器和湿度传感器。通过改进的PSO算法得到优化的神经网络连接权值,提高算法的搜索速度和训练效率,避免模糊神经网络易陷入局部极小值的问题。利用改进PSO-FNN算法对钢筋腐蚀的样本数据进行训练及测试,结果表明,改进的PSO-FNN腐蚀检测模型算法性能优于PSO-FNN算法,收敛速度快,可有效提高钢筋混凝土腐蚀检测的精度。Aiming at the problems that traditional particle swarm optimization(PSO)is prone to premature convergence and poor local optimization ability when dealing with complex search problems,an adaptive adjustment method of inertia factor in PSO algorithm is proposed.The improved PSO algorithm is used to optimize the fuzzy neural network(FNN),and the improved PSO-FNN algorithm is applied to the corrosion detection of reinforced concrete based on multi-sensor information fusion.The detection system includes pH sensor,chloride ion sensor and humidity sensor.The optimized neural network connection weights are obtained through the improved PSO algorithm,which improves the search speed and training efficiency of the algorithm,and avoids the problem of fuzzy neural networks easily falling into local minimums.The improved PSO-FNN algorithm is used to train and test the sample data of steel corrosion.The results show that the performance of the improved PSO-FNN corrosion detection model algorithm is better than the PSO-FNN algorithm,the convergence speed is faster,which improves the accuracy of reinforced concrete corrosion detection effectively.

关 键 词：钢筋混凝土腐蚀程度 改进PSO 模糊神经网络 惯性因子 检测精度 PH值传感器 

分 类 号：TU528.33[建筑科学—建筑技术科学]