| 城市地下空间安全监测与预警指标研究 |
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| 引用本文： | 李守雷.城市地下空间安全监测与预警指标研究[J].地质与勘探,2024,60(1):95-104. |
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| 作者姓名： | 李守雷 |
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| 作者单位： | 中国电建集团中南勘测设计研究院有限公司，湖南长沙； 中南大学土木工程学院，湖南长沙 |
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| 基金项目： | 中国电建集团中南勘测设计研究院有限公司科研课题“互联网+”城市地下工程安全监测快速采集发布预警系统（编号：YFHT-1832）和国家自然科学基金面上项目（编号：51978674）联合资助 |
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| 摘 要： | 为保障城市地下空间开发利用的安全性，促进城市可持续发展，通过文献调研、现场调查和专家咨询等方法，分析提出城市地下空间监测的五项原则，将监测对象划分为三类：工程结构本体、周围岩土体以及周边环境。将监测指标归纳为变形类、力学类、振动类和宏观状态类共四类，其中变形类指标执行双控要求，其他三种指标执行单控要求。监测趋势预测分析可采用公式法、回归分析法、时间序列分析法、灰色预测法、神经网络法和支持向量机法等。全国各地监测控制值基本一致，但预警分级标准存在地区差异，其中北京市和广州市分级预警具有较大参考价值。目前城市地下空间安全监测存在七项不足：预警分级标准不完善，人工监测效率低，监测参数单一，监测信息缺少共享协同，测量精度较低，重监测轻预测以及缺乏数据融合和机器学习应用。针对这些问题，可采取七项措施进行改进：建立合理预警分级标准，发展自动化与智能化监测，多参数综合监测，应用远程监测与云平台，开发高精度测量设备，监测和预测并重，以及数据融合与机器学习应用。
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| 关 键 词： | 地下工程 安全监测 监测指标 趋势预测 预警分级 |
| 收稿时间： | 2023/11/17 0:00:00 |
| 修稿时间： | 2024/1/19 0:00:00 |
Safety monitoring and early warning indexes of urban underground space |
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| Li Shoulei.Safety monitoring and early warning indexes of urban underground space[J].Geology and Prospecting,2024,60(1):95-104. |
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| Authors: | Li Shoulei |
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| Institution: | PowerChina Zhongnan Engineering Corporation Limited, Changsha,Hunan; School of Civil Engineering, Central South University, Changsha, Hunan |
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| Abstract: | In order to ensure the safety of the development and utilization of urban underground space and promote the sustainable development of cities, this work proposed five principles of urban underground space monitoring by means of literature investigation, field investigation and expert consultation. The monitoring objects were divided into three categories, i.e., engineering structure, surrounding rock and soil mass, and surrounding environment. The monitoring indexes were summarized into four categories of deformation, mechanics, vibration and macrostate, among which the deformation indexes implemented double control requirements, and the other three indexes implemented single control requirements. The prediction of monitoring trends adopted the formula method, regression analysis method, time series analysis method, gray prediction method, neural network method and support vector machine method. The monitoring control values were basically the same throughout the country, but the early warning classification standards varied between different cities. The graded early warning of Beijing and Guangzhou cities provides significant reference for other areas. Seven deficiencies have been proposed for the safety monitoring of urban underground space, i.e., imperfect standards of warning classification, low efficiency of manual monitoring, single monitoring parameters, lack of monitoring information sharing, poor measurement accuracy, emphasis on monitoring rather than prediction, and lack of data fusion and machine learning applications. In view of these problems, seven measures were recommended, i.e., the establishment of reasonable early warning classification standards, the development of automation and intelligent monitoring, multi-parameter comprehensive monitoring, the application of remote monitoring and cloud platform, the development of high-precision measurement equipment, equal emphasis on monitoring and prediction, and data fusion and machine learning. |
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| Keywords: | underground engineering safety monitoring monitoring indicators trend prediction early warning and classification |
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