水闸混凝土碳化深度预测研究  

Prediction of carbonation depth of sluice concrete

作　　者：赵海娟 王军 Zhao Haijuan;Wang Jun(Daqiao Water Conservancy Management Service Station in Dafeng District of Yancheng City,Yancheng 224100,China;Xinfeng Water Conservancy Management Service Station in Dafeng District of Yancheng City,Yancheng 224100,China)

机构地区：[1]盐城市大丰区大桥水利管理服务站,江苏盐城224100 [2]盐城市大丰区新丰水利管理服务站,江苏盐城224100

出　　处：《山西建筑》2019年第22期151-152,共2页Shanxi Architecture

摘　　要：在水闸工程病害中,混凝土碳化最为典型,混凝土碳化是造成混凝土裂缝、钢筋锈蚀的最直接因素,因此,对混凝土碳化深度预测研究尤为重要。采用遗传算法优化神经网络,选取混凝土碳化深度的主要影响因素,建立混凝土碳化深度预测模型,并基于VS平台,开发水闸混凝土碳化深度预测系统。收集了盐城市25组水闸数据样本进行预测分析研究,结果表明,采用遗传算法优化BP神经网络模型进行水闸混凝土碳化深度预测是可行的,能够快速、准确识别混凝土碳化深度,为水闸除险加固提供技术支持。In the sluice engineering disease,concrete carbonization is typical. Concrete carbonization is the direct factor causing concrete cracks and steel corrosion. Therefore,it is important to predict concrete carbonation depth. In this paper,the genetic algorithm is used to optimize the neural network,the main influencing factors of concrete carbonation depth are selected,the concrete carbonation depth prediction model is established,and the sluice concrete carbonization depth prediction system is developed based on the VS platform. The data samples of 25 groups of sluices in Yancheng City were collected for predictive analysis. The results show that it is feasible to use BP neural network model for sluice concrete carbonation depth prediction. It can quickly and accurately identify concrete carbonation depth and provide technical support for sluice reinforcement.

关 键 词：混凝土碳化深度 遗传算法 BP神经网络 

分 类 号：TV698[水利工程—水利水电工程]