摘要
夯坑位置、夯次和夯沉量是强夯法施工质量监测的主要指标,其中夯沉量是传统监测的技术难点,监测的效率和精度往往难以兼顾。为此,本文研发了基于多元感知的强夯施工质量智能监测装备及配套的智能监测系统。构建了基于夯锤主动目标的夯沉量摄影测量监测体系;集成GNSS-RTK和磁方位角传感器实现了夯机和夯坑的协同定位;基于机器视觉和时序模式实现了夯次智能监测;研发了强夯施工质量智能监测云平台发布监测数据,集成上述系统实现了夯次、夯沉量、夯坑位置等施工参数的实时监测系统。现场实验验证了本智能装备的监测精度和效率均满足施工管理的要求。本文可为强夯施工等智能监测装备研发提供参考。
Tamping pit positioning,tamping count and tamping settlement are three basic indicators for monitoring the quality of dynamic compaction construction.How to measure tamping settlement is usually a challenge to traditional monitoring techniques,and the dilemma between its efficiency and accuracy are difficult to reconcile.This study developes a full set of multi-perception integrated intelligent monitoring instrumentation for monitoring dynamic compaction construction quality,a supporting intelligent monitoring software,and a monitoring system with an active measurement target based on photogrammetry.A real time kinematic Global Navigation Satellite System(GNSS-RTK)and a magnetic azimuth sensor are integrated to realize the coordinated positioning of the ramming machine and tamping pit,and tamping count is recorded using machine vision and temporal pattern recognition.We have also developed an information cloud platform for releasing monitoring data collected using the instrumentation.All the above systems and instruments working together can realize a real time measuring system for tamping pit positioning,tamping count monitoring,and tamping settlement calculation.Field experiments of dynamic compaction construction prove that the accuracy and efficiency of our monitoring instrumentation and system meets the engineering requirements.The results help develop new intelligent construction monitoring instruments and promote the monitoring level.
作者
刘全
张宏阳
邬志
金银龙
高乔裕
李飞羽
游川
赵越良
LIU Quan;ZHANG Hongyang;WU Zhi;JIN Yinlong;GAO Qiaoyu;LI Feiyu;YOU Chuan;ZHAO Yueliang(State Key Laboratory of Water Resources and Hydropower Engineering Science,Wuhan University,Wuhan 430072;POWERCHINA Huadong Engineering Corporation,Hangzhou 310010)
出处
《水力发电学报》
CSCD
北大核心
2021年第10期135-146,共12页
Journal of Hydroelectric Engineering
关键词
强夯施工监测
装备研发
摄影测量
卷积神经网络
模式识别
dynamic compaction construction monitoring
instrument development
photogrammetry
convolutional neural network(CNN)
pattern recognition