scholarly journals Intelligent Early Warning System for Construction Safety of Excavations Adjacent to Existing Metro Tunnels

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Wei Tian ◽  
Jiang Meng ◽  
Xing-Ju Zhong ◽  
Xiao Tan
Keyword(s):  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Assessment Model ◽  
Monitoring Data ◽  
Effective Control ◽  
Construction Process ◽  
Safety Risk ◽  
Time Deformation ◽  
Metro Tunnel

With the increasing exploitation and utilization of underground spaces, the excavation of deep foundation pits adjacent to existing metro tunnels is becoming increasingly common. These excavations have the potential to cause safety problems for the operation of the nearby metro. Therefore, to prevent metro tunnel accidents from occurring during the construction process and to ensure the safety of lives and property, it is necessary to establish a risk-based early warning system. During the excavation process, the main methods for preventing accidents in excavations adjacent to existing metro tunnels are manual analyses based on on-site monitoring data. However, these methods make it difficult to enact effective control measures in a timely manner owing to the lag of information processing. However, the trial application of artificial neural networks (ANNs) and building information modelling (BIM) for engineering projects provides a new method for solving such problems. This study uses a backpropagation neural network to predict the real-time deformation of the tunnel based on monitoring data from the adjacent construction site. A safety risk assessment model is then established based on the relevant specifications. Through the establishment of an intelligent warning system, the safety risk to the metro tunnel during the construction process can be displayed in a three-dimensional (3D) form using the BIM. The operation results of the ANN–BIM system show that it can effectively present the safety risk to existing metro tunnels in a 3D manner, which can provide managers with rapid and convenient visual information to inform their decision-making.

Research on the architecture of food safety risk intelligence early warning system

2021 ◽  
pp. 1-11
Author(s):  
Yu Zhang ◽  
Yarui Zhang ◽  
Xiaocui Li
Keyword(s):  
Decision Support ◽  
Food Safety ◽  
Early Warning ◽  
Early Warning System ◽  
System Development ◽  
Warning System ◽  
Risk Identification ◽  
Functional Modules ◽  
Safety Risk ◽  
Food Safety Risk

Food safety supervision involves all aspects of production, processing and sales. True, reliable and complete intelligence can realize the traceability of the entire process of food safety production, thereby ensuring that food safety incidents are controllable from the source. However, most studies only analyze the food safety risk identification and early warning from the perspective of information flow from the theoretical level, and lack specific applications at the practical level. Therefore, this study analyzes the system requirements and the overall business process of the system, expounds the goals and principles of system design, designs the overall framework of the system, and finally elaborates on the realization of its functions of the different functional modules of the system, so as to provide the early warning system development provides decision support and reference. Finally elaborates the realization of its functions according to the different functional modules of the system, so as to provide decision support and reference for the development of early warning system.

scholarly journals Application of Data Mining in an Intelligent Early Warning System for Rock Bursts

Processes ◽  
2019 ◽  
Vol 7 (2) ◽  
pp. 55 ◽  
Author(s):  
Xuejun Zhu ◽  
Xiaona Jin ◽  
Dongdong Jia ◽  
Naiwei Sun ◽  
Pu Wang
Keyword(s):  
Data Mining ◽  
Data Warehouse ◽  
Rock Burst ◽  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Monitoring Data ◽  
Efficient Production ◽  
Mining Technology ◽  
Rock Bursts

In view of rock burst accidents frequently occurring, a basic framework for an intelligent early warning system for rock bursts (IEWSRB) is constructed based on several big data technologies in the computer industry, including data mining, databases and data warehouses. Then, a data warehouse is modeled with regard to monitoring the data of rock bursts, and the effective application of data mining technology in this system is discussed in detail. Furthermore, we focus on the K-means clustering algorithm, and a data visualization interface based on the Browser/Server (B/S) mode is developed, which is mainly based on the Java language, supplemented by Cascading Style Sheets (CSS), JavaScript and HyperText Markup Language (HTML), with Tomcat, as the server and Mysql as the JavaWeb project of the rock burst monitoring data warehouse. The application of data mining technology in IEWSRB can improve the existing rock burst monitoring system and enhance the prediction. It can also realize real-time queries and the analysis of monitoring data through browsers, which is very convenient. Hence, it can make important contributions to the safe and efficient production of coal mines and the sustainable development of the coal economy.

scholarly journals A Study about the 3S-based Great Ruins Monitoring and Early-warning System

2015 ◽  
Vol II-5/W3 ◽  
pp. 357-361
Author(s):  
W. Xuefeng ◽  
H. Zhongyuan ◽  
L. Gongli ◽  
Z. Li
Keyword(s):  
Early Warning ◽  
Early Warning System ◽  
Large Scale ◽  
Processing System ◽  
Warning System ◽  
Monitoring Data ◽  
Software Systems ◽  
New Countryside ◽  
Monitoring Data Processing ◽  
Monitoring And Early Warning

Large-scale urbanization construction and new countryside construction, frequent natural disasters, and natural corrosion pose severe threat to the great ruins. It is not uncommon that the cultural relics are damaged and great ruins are occupied. Now the ruins monitoring mainly adopt general monitoring data processing system which can not effectively exert management, display, excavation analysis and data sharing of the relics monitoring data. Meanwhile those general software systems require layout of large number of devices or apparatuses, but they are applied to small-scope relics monitoring only. Therefore, this paper proposes a method to make use of the stereoscopic cartographic satellite technology to improve and supplement the great ruins monitoring index system and combine GIS and GPS to establish a highly automatic, real-time and intelligent great ruins monitoring and early-warning system in order to realize collection, processing, updating, spatial visualization, analysis, distribution and sharing of the monitoring data, and provide scientific and effective data for the relics protection, scientific planning, reasonable development and sustainable utilization.

scholarly journals A GIS-based monitoring and early warning system for cover-collapse sinkholes in karst terrane in Wuhan, China

2017 ◽  
Author(s):  
Li Xueping ◽  
Xiao Shangde ◽  
Tang Huiming ◽  
Peng Jinsheng
Keyword(s):  
Plastic Zone ◽  
Water Level ◽  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Underground Water ◽  
Water Levels ◽  
Monitoring Data ◽  
Karst Collapse ◽  
Warning Model

Abstract. To reduce disastrous losses caused by karst collapse especially in urban areas, it is important to establish an early warning system utilizing monitoring data. Three major aspects have been monitored based upon engineering geological conditions and characteristics of karst collapse processes in Wuhan, China: changes in surface soil, soil deformation, and groundwater levels. Measurements have been recorded of: (1) soil pressure, (2) ground-penetrating radar images, (3) underground water levels, (4) ground water levels, (5) rainfall, (6) cracking, (7) ground deformation, and (8) water level in monitored wells. This paper has selected geological radar cross-sectional data and underground water level monitoring data to obtain criteria for hydraulic gradient warning, geological radar warning and plastic zone warning based upon these monitoring data and wider knowledge of karst collapse in Wuhan. A comprehensive warning system has been developed on a MAPGIS platform, employing monitoring data in Microsoft Excel format and Microsoft Visual C++ development tools. Three warning levels are adopted by the system: safe, becoming dangerous, and dangerous; indicated in green, yellow and red respectively on hazard maps. The system automatically undertakes processes of data management and model calculation leading to geo-hazard warning map generation. Using monitoring data collected in the first six months of 2011 at Wuhan, the system has established a hydraulic gradient model, plastic zone warning model, geological radar warning model, and a comprehensive early warning model; and has been shown to be an effective method of providing karst collapse warning.

Design of Mountain Road Tunnel Safety Monitoring System

2011 ◽  
Vol 378-379 ◽  
pp. 241-246
Author(s):  
Wei Dong Pan ◽  
Ren Guo Gu ◽  
Yong Gang Lv ◽  
Xiao Long Sheng
Keyword(s):  
Monitoring System ◽  
Early Warning ◽  
Early Warning System ◽  
Measurement Data ◽  
Monitoring Program ◽  
Warning System ◽  
Safety Monitoring ◽  
Construction Process ◽  
Accuracy Measurement ◽  
Safety Monitoring System

The tunnel, the construction safety of which is the most important part of highway to put into operation in time, is an important part of the mountain region road. Based on the characteristics of New Austrian Tunneling Method, the importance of safety monitoring system for grasping the rock deformation in the construction process goes without saying. This paper presents a safety monitoring program through the construction of the sage Mei Shan Highway, Lung Hang Tunnel, providing theoretical and operational support for the sage Mei Shan Highway through analyzing the construction process, the principle of safety monitoring, frequency of monitoring items, monitoring and early warning system, measures to ensure measurement accuracy, measurement data analysis, feedback of early warning system.

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