摘要
目的研究锚杆支护边坡失稳情况下的监测预警阈值,开发智能监测系统,实现边坡安全预警。方法采用PLAXIS2D建立边坡有限元模型,运用强度折减法分析动态折减的边坡稳定性,研究锚杆应变与边坡失稳过程的关联性;利用LabVIEW软件平台开发具有智能预警功能的实时监测系统并在实际现场进行应用。结果获得了动态折减系数下对应的安全稳定系数;边坡安全系数越小,锚杆应变值越大,应变变化速率越快,边坡处于不稳定状态,易发生边坡失稳破坏;对边坡预警分级标准给出的对应锚杆监测应变阈值分别为195.12×10-6、1212.15×10-6、1236.92×10-6;在LabVIEW中实现了智能监测预警系统的开发,并对某挡土墙的预警监测验证其实际效果是可行的。结论笔者所开发锚杆支护边坡安全预警分级开发的智能监测系统监测效果较好,可为锚杆支护边坡结构的智能监测预警系统提供思路。
This paper had developed an intelligent monitoring system based on the research of monitoring and early warning thresholds in the case of slope instability of anchor support to realize slope safety early warning.PLAXIS2D was employed to establish the slope finite element model.The strength reduction method was applied to analyze the stability of the dynamically reduced slope and to study the correlation between anchor strain and slope instability process.The LabVIEW software platform was used to develop a real-time monitoring system with intelligent warning function and apply it in the actual site.The coefficient of safety and stability corresponding to the dynamic discount factor were obtained.The smaller the slope safety factor is,the larger the anchor strain value is and the faster the strain change rate is.The slope is in an unstable state and prone to slope instability damage at this point.The corresponding anchor monitoring strain thresholds given for the slope warning classification criteria were 195.12×10-6,1212.15×10-6 and 1236.92×10-6 respectively.The development of intelligent monitoring and warning system had been realized in LabVIEW.Its effect was verified by monitoring a retaining wall.The intelligent monitoring system developed based on the safety warning grading of anchor support slope has a good monitoring effect.It can provide ideas for the intelligent monitoring and early warning system of anchor support slope structure.
作者
王桂萱
黄平
赵杰
WANG Guixuan;HUANG Ping;ZHAO Jie(Research and Development Center of the Civil Engineering Technology,Dalian University,Dalian,China,116622)
出处
《沈阳建筑大学学报(自然科学版)》
CAS
CSCD
北大核心
2021年第4期668-675,共8页
Journal of Shenyang Jianzhu University:Natural Science
基金
国家自然科学基金项目(51738010)。