2019 Vol.2 Dec. No.6

• Title: Real-time detection method of ground subsidence data for public building construction under cloud computing environment
• Name: Seraphin Grimson
• Company: Swinburne University of Technology
• Abstract:

  Traditional public building construction ground subsidence data detection lacks real-time performance. In order to effectively improve the ability of real-time detection of public building construction ground subsidence data, it is necessary to collect and extract public building construction ground subsidence data under cloud computing environment. Then, the real-time characteristics of cloud computing data are used to effectively analyze the ground subsidence data. Finally, the real-time detection of ground subsidence data under realistic cloud computing environment. After simulation experiment demonstration and analysis, the experimental results show that the ground settlement data of public building construction under cloud computing environment is accurate and effective, and the data is real-time.

• Keyword: Cloud Computing Environment; Public Buildings; Land Subsidence; Subsidence Data; Real-Time Detection;
• DOI: 10.12250/jpciams2019060638
• Citation form: Seraphin Grimson.Real-time detection method of ground subsidence data for public building construction under cloud computing environment[J]. Computer Informatization and Mechanical System, 2019, vol. 2, pp. 33-39.

[1] Zhangzhidong. Accurate detection simulation of user interest data in cloud computing environment[J]. Computer simulation, 2017, 34(10): 410-413

[2] Party Red, Zhaoerping, Sunhaixia.. Real time mining method simulation of non-prominent feature data under cloud computing[J]. Computer Simulation, 2017, 34(7): 203-206.

[3] Licheng, Zhangjiaxin, Shaoyixuan. A brief discussion on data security analysis under the large data cloud computing environment[ J]. Computer fan, 2017(3): 37-38.

[4] Yuki. Research on data distributed secure access in cloud computing environment[J]. Science and Technology Bulletin, 2017, 33(8): 189-192.

[5] Mengweitao, Mayanmin , Pochunhui. Research on Dynamic Data Gathering Algorithm in Cloud Computing Environment[J]. Computer Measurement and Control, 2018.

[6] Gaoyantao, Wangjunjian, Xuhongwei, et al.. Data Processing and Accuracy Analysis of Ground Settlement Monitoring Based on GNSS[ J]. Resource Guide, 2017(20): 37-39.

[7] Songxiaoguang, Aimingyao, Zhangyue, et al.. Analysis and evaluation of urban land subsidence prediction model based on multiphase discrete observation data[J]. Engineering Survey, 2017, 45(2): 68-73.

[8] Shixiangfeng, Wanglifen, Shenyang, et al.. Study on prediction time series of ground subsidence in foundation pit construction based on GA-SVM[J]. Construction Technology, 2017, 46(8): 16-19.

[9] Wangjunhui, Taolianjin, Hanxuan, et al.. Real-time prediction of land subsidence induced by construction precipitation in multi-layer aquifers in Beijing[J]. Journal of Beijing University of Technology, 2017, 43(2): 251-260.

[10] Liuyin, Jiangmingming.. Evaluation of Ground Settlement Risk of Soft Soil Top Pipe Construction Based on Fuzzy Theory[J]. People's Yangtze river, 2017(b06): 199-201.

[11] Mengchao, Wutonglin, Zhangqi.. Research on Audit Work Mode in the Background of Large Data Cloud Computing[J]. Times Economic and Trade, 2017(16): 55-57.

[12] Party Red, Zhaoerping, Sunhaixia.. Real time mining method simulation of non-prominent feature data under cloud computing[J]. Computer Simulation, 2017, 34(7): 203-206.