Simulation research on the deformation safety monitoring and evaluation algorithm of coastal soft foundation pit based on big data

2021 ◽  
Author(s):  
Zhenghong Huang ◽  
Chunguang Mao ◽  
Shiyu Guan ◽  
Hui Tang ◽  
Guanghua Chen ◽  
...  
Keyword(s):  
Big Data ◽  
Safety Monitoring ◽  
Monitoring And Evaluation ◽  
Foundation Pit ◽  
Simulation Research ◽  
Soft Foundation ◽  
Evaluation Algorithm

Simulation of Excavation Process for Deep Foundation Pit Base on Nonlinear Finite Element Theory

2015 ◽  
Vol 744-746 ◽  
pp. 579-583
Author(s):  
Hui Min Wang ◽  
Zhen Jian Ji ◽  
Liang Cao ◽  
Ji Yao ◽  
Shan Guang Qian
Keyword(s):  
Finite Element ◽  
Safety Evaluation ◽  
Nonlinear Finite Element ◽  
Distribution Law ◽  
Deep Foundation ◽  
Foundation Pit ◽  
Simulation Research ◽  
Deep Foundation Pit ◽  
Excavation Process ◽  
Element Theory

Deep Pit is the main content of modern urban geotechnical engineering. In this paper, based on a deep foundation pit engineering as an example, based on the nonlinear finite element theory, conduct a numerical simulation research for foundation pit excavation process. Obtained the distribution law of pit deformation, stress distribution and the supporting structure of the internal forces, under the various processes. These provide a theoretical basis for safety evaluation of foundation pit engineering.

Monitoring and evaluation algorithm of GNSS signal in space availability

2017 ◽  
Vol 59 (3) ◽  
pp. 786-793 ◽  
Author(s):  
Ershen Wang ◽  
Qing Zhang ◽  
Gang Tong ◽  
Pingping Qu ◽  
Tao Pang
Keyword(s):  
Monitoring And Evaluation ◽  
Evaluation Algorithm

scholarly journals Preliminary Discussion on Monitoring of Steel Support Axis Force in Metro Engineering

2019 ◽  
Vol 136 ◽  
pp. 04045
Author(s):  
Yiteng Xu ◽  
Feng Xu ◽  
Peirong Deng ◽  
Bin Li ◽  
Zhifa Yu ◽  
...  
Keyword(s):  
Axial Force ◽  
Warning System ◽  
Safety Monitoring ◽  
Control Measures ◽  
Foundation Pit ◽  
Depth Analysis ◽  
Force Monitoring ◽  
And Control ◽  
Monitoring Behavior ◽  
Irregular Behavior

Axial force monitoring of steel support is one of the important factors for foundation pit safety monitoring. In the monitoring of steel support, there are many problems, such as unreasonable installation of axonometer, irregular monitoring behavior of axonometer, incomplete analysis of axial force and imperfect early warning system. Collecting many engineering cases, and in-depth analysis and research on the problems and irregular behavior of steel support axial force monitoring in every link. The influencing factors and control measures of steel support axial force are discussed in detail, and some useful conclusions are obtained. It has been applied in the actual monitoring work and achieved good results. It is of great significance to guide subway safety construction and promote the development of axle force monitoring industry.

Safety Monitoring and Evaluation of Construction Projects Based on Multi-sensor Fusion

2020 ◽  
Vol 19 (6) ◽  
pp. 431-441
Author(s):  
Na Ni
Keyword(s):  
Missing Data ◽  
Monitoring System ◽  
Sensor Fusion ◽  
Construction Projects ◽  
Monitoring Network ◽  
Safety Monitoring ◽  
Monitoring And Evaluation ◽  
Construction Safety ◽  
Monitoring Data ◽  
Safety Monitoring System

For most construction projects, the complex engineering environment, the backward data collection technology, and the unreasonable monitoring network have resulted in many problems in monitoring data such as lots of noise and missing data items, therefore, it is of great significance to study the safety monitoring system of construction projects based on wireless sensor network (WSN). For this reason, this paper proposed a construction safety monitoring and evaluation (CSME) model based on multi-sensor fusion. First, the system structure and data flow model of the construction safety monitoring system were constructed; then, combining with a multi-sensor deep fusion system which was built on physical and information systems, this paper designed a spectrum sensing algorithm for sensor signals within the construction area. After that, tempo-spatial correlation analysis was conducted on the monitoring data, and a multi-sensor monitoring network joint sparse (MSMN-JS) model was constructed, which realized reconstruction of missing data items. At last, this paper used experimental results to prove the application value of the algorithm model to the safety monitoring and evaluation of construction projects.

scholarly journals Practical Classification and Evaluation of Optically Recorded Food Data by Using Various Big-Data Analysis Technologies

Machines ◽  
2020 ◽  
Vol 8 (2) ◽  
pp. 34
Author(s):  
Tim Jarschel ◽  
Christoph Laroque ◽  
Ronny Maschke ◽  
Peter Hartmann
Keyword(s):  
Big Data ◽  
Big Data Analytics ◽  
Monitoring And Evaluation ◽  
Life Cycles ◽  
Automated System ◽  
Time Data ◽  
Production Technologies ◽  
Set Up ◽  
Advanced Production ◽  
Data Visualizations

An increasing shortening of product life cycles, as well as the trend towards highly individualized food products, force manufacturers to digitize their own production chains. Especially the collection, monitoring, and evaluation of food data will have a major impact in the future on how the manufacturers will satisfy constantly growing customer demands. For this purpose, an automated system for collecting and analyzing food data was set up to promote advanced production technologies in the food industry. Based on the technique of laser triangulation, various types of food were measured three-dimensionally and examined for their chromatic composition. The raw data can be divided into individual data groups using clustering technologies. Subsequent indexing of the data in a big data architecture set the ground for setting up real-time data visualizations. The cluster-based back-end system for data processing can also be used as an organization-wide communication network for more efficient monitoring of companies’ production data flows. The results not only describe the procedure for digitization of food data, they also provide deep insights into the practical application of big data analytics while helping especially small- and medium-sized enterprises to find a good introduction to this field of research.

scholarly journals A Safety Warning Algorithm Based on Axis Aligned Bounding Box Method to Prevent Onsite Accidents of Mobile Construction Machineries

Sensors ◽  
2021 ◽  
Vol 21 (21) ◽  
pp. 7075
Author(s):  
Cynthia Changxin Wang ◽  
Mudan Wang ◽  
Jun Sun ◽  
Mohammad Mojtahedi
Keyword(s):  
Safety Management ◽  
Soft Soil ◽  
Safety Monitoring ◽  
Human Beings ◽  
Risk Area ◽  
Foundation Pit ◽  
Bounding Box ◽  
Safety Warning ◽  
Risk Areas ◽  
Box Method

Mobile construction machineries are accident-prone on a dynamic construction site, as the site environment is constantly changing and continuous safety monitoring by human beings is impossible. These accidents usually happen in the form of machinery overturning or collapsing into risk areas, including the foundation pit, slopes, or soft soil area. Therefore, preventing mobile construction machineries from entering risk areas is the key. However, currently, there is a lack of practical safety management techniques to achieve this. Utilizing a wireless sensor device to collect the location information of mobile construction machineries, this research develops a safety warning algorithm to prevent the machineries moving into risk area and reduces onsite overturning or collapsing accidents. A modified axis aligned bounding box method is proposed according to the movement patterns of mobile construction machineries, and the warning algorithm is developed based on the onsite safety management regulations. The algorithm is validated in a real case simulation when machinery enters the warning zone. The simulation results showed that the overall algorithm combining the location sensing technology and the modified bounding box method could detect risk and give warnings in a timely manner. This algorithm can be implemented for the safety monitoring of mobile construction machineries in daily onsite management.

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