摘要
当前污水管网周期性检测机制存在成本高且无法及时发现影响管网正常运行的重大缺陷等问题,亟需开发重大缺陷快速识别方法。通过整合各类先进技术提出了一种污水管网重大缺陷快速识别方法。首先,以区县级片区内的污水管网为研究对象,依托多源大数据分析技术,构建管网缺陷预测模型,筛选高风险区域。然后,综合采用水质水量分析检测、无人机航拍遥感、逻辑回归预测模型、车载探地雷达等先进技术对高风险区域的管网进行全方位、快速的检测,以精确识别出存在重大缺陷的管道。最后,采用多种先进物探技术相结合的方式开展管道内部快速检测,精准获取缺陷类型和缺陷等级,从而为后期的修复提供科学依据。
The current periodic detection mechanism for pipelines has issues,including high costs and an inability to detect major defects that could impact the safety of the pipelines in a timely manner.Therefore,it is imperative to develop a rapid identification method for major defects in sewage pipelines.In this paper,a rapid identification method for major defects in sewerage pipelines is proposed by integrating various advanced technologies.Firstly,taking the sewage pipelines in the district and county area as the research object,relying on the multi-source big data analysis technology,the pipe network defect prediction model is built to screen the high-risk areas.Then,advanced technologies such as water quality and quantity analysis and detection,UAV aerial remote sensing,logistic regression prediction model,and vehicle-mounted ground penetrating radar are used to comprehensively and quickly detect the pipelines in high-risk areas to accurately identify pipelines with major defects.Finally,a variety of advanced physical exploration techniques are used in combination to carry out rapid pipeline internal inspection,in order to accurately obtain the type and level of pipeline defects,so as to provide a scientific basis for the later repair.
作者
王福芝
尹炜
王殿常
彭寿海
王万琼
陈晓龙
WANG Fuzhi;YIN Wei;WANG Dianchang;PENG Shouhai;WANG Wanqiong;CHEN Xiaolong(China Three Gorges Corporation,Wuhan 430010,China;Ecological Environment of Yangtze River Economic Zone,Wuhan 430010,China;Yangtze Eco-Environment Engineering Research Center,China Three Gorges,Wuhan 430010,China;Yangtze Ecology and Environment Co.,Ltd.,Wuhan 430010,China)
出处
《给水排水》
CSCD
北大核心
2024年第8期112-119,共8页
Water & Wastewater Engineering
基金
国家重点研发计划(2022YFC3203205)。
关键词
重大缺陷
检测技术
快速识别
Major defects
Detection technologies
Rapid identification
