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Cluster Computing

, Volume 22, Supplement 3, pp 6499–6516 | Cite as

Integration and formal representation in civil engineering supervision based on data-driven

  • Shifeng Wu
  • Huazhu SongEmail author
  • Ting Li
  • Xian ZhongEmail author
Article
  • 129 Downloads

Abstract

In the civil engineering supervision, the management processes about personnel, materials, quality, safety, schedule and etc. are complex, which include large amount of data, involve more participants, and timely coordination and feedback is difficult. In this paper, we propose the data integration idea based on data-driven, which could guide the whole life cycle of civil engineering supervision from the top-level data organization, supervision of business processes, the interactive units and the users, the four-elements method TDTM and so on. Then, the data integration algorithms of civil engineering supervision are put forward to integrate the civil engineering supervision data from outside to inside and from coarse-grained to fine-grained, eliminate contradictions and redundancy and ensure data consistency. Finally, the civil engineering supervision data entities are determined, and we further analyze and discuss its query and report, data maintenance and conversion, and compare the functions in the different supervision platform. The civil engineering supervision unified data platform proposed could maintain the independence of the data, have the good scalability and support the more functions.

Keywords

Civil engineering supervision Data-driven Data integration Formal representation 

Notes

Acknowledgements

This work was supported in part by the National Natural Science Fund of China under grant 61003130, in part by the Sub research topic of a National Science and Technology Support Plan under Grant 2012BAH33F03, and in part by The Natural Science Foundation of Hubei Province, China under Grant 2015CFB525.

References

  1. 1.
    Li, X.: Research and application of quality management in engineering supervision. Industry C 04, 00044–00045 (2016)Google Scholar
  2. 2.
    Li, X., Xu, J., Zhang, Q.: Research on construction schedule management based on BIM technology. Procedia Eng. 174, 657–667 (2017)CrossRefGoogle Scholar
  3. 3.
    Wang, K.C., Wang, W.C., Wang, H.H., et al.: Applying building information modeling to integrate schedule and cost for establishing construction progress curves. Autom. Constr. 72, 397–410 (2016)CrossRefGoogle Scholar
  4. 4.
    Faghihi, V., Reinschmidt, K.F., Kang, J.H.: Construction scheduling using genetic algorithm based on building information model. Expert Syst. Appl. 41(16), 7565–7578 (2014)CrossRefGoogle Scholar
  5. 5.
    Hardison, D., Behm, M., Hallowell, M.R., et al.: Identifying construction supervisor competencies for effective site safety. Saf. Sci. 65, 45–53 (2014)CrossRefGoogle Scholar
  6. 6.
    Zhang, S., Boukamp, F., Teizer, J.: Ontology-based semantic modeling of construction safety knowledge: towards automated safety directioning for job hazard analysis (JHA). Autom. Constr. 52, 29–41 (2015)CrossRefGoogle Scholar
  7. 7.
    Gurcanli, G.E., Bilir, S., Sevim, M.: Project based risk assessment and safety cost estimation for residential building construction projects. Saf. Sci. 80, 1–12 (2015)CrossRefGoogle Scholar
  8. 8.
    Ganah, A., John, G.A.: Integrating building information modeling and health and safety for onsite construction. Saf. Health Work 6(1), 39–45 (2015)CrossRefGoogle Scholar
  9. 9.
    Jiao, Y., Wang, Y., Zhang, S., et al.: A cloud approach to unified lifecycle data management in architecture, engineering, construction and facilities management: integrating BIMs and SNS. Adv. Eng. Inf. 27, 173–188 (2013)CrossRefGoogle Scholar
  10. 10.
    Alreshidi, E., Mourshed, M., Rezgui, Y.: Factors for effective BIM governance. J. Build. Eng. 10, 89–101 (2017)CrossRefGoogle Scholar
  11. 11.
    Chien, S., Chuang, T., Yu, H.S., et al.: Implementation of cloud BIM-based platform towards high-performance building services. Procedia Environ. Sci. 38, 436–444 (2017)CrossRefGoogle Scholar
  12. 12.
    Fröbel, T., Firmenich, B., Koch, C.: Quality assessment of coupled civil engineering applications. Adv. Eng. Inf. 25(4), 625–639 (2011)CrossRefGoogle Scholar
  13. 13.
    Lou, J., Xu, J., Wang, K.: Study on construction quality control of urban complex project based on BIM. Procedia Eng. 174, 668–676 (2017)CrossRefGoogle Scholar
  14. 14.
    Chen, L.J., Luo, H.: A BIM-based construction quality management model and its applications. Autom. Constr. 46, 64–73 (2014)CrossRefGoogle Scholar
  15. 15.
    Niknam, M., Karshenas, S.: A shared ontology approach to semantic representation of BIM data. Autom. Constr. 80, 22–36 (2017)CrossRefGoogle Scholar
  16. 16.
    Opitz, F., Windisch, R., Scherer, R.J.: Integration of document-and model-based building information for project management support. Procedia Eng. 85, 403–411 (2014)CrossRefGoogle Scholar
  17. 17.
    Lee, Y.C., Eastman, C.M., Solihin, W.: An ontology-based approach for developing data exchange requirements and model views of building information modeling. Adv. Eng. Inf. 30(3), 354–367 (2016)CrossRefGoogle Scholar
  18. 18.
    Li, H., Wang, Y.: BIM-based project management and management system. China Civ. Eng. Manag. J. 33(6), 78–82 (2016)Google Scholar
  19. 19.
    Shaker, H.R., Lazarova-Molnar, S.: A new data-driven controllability measure with application in intelligent buildings. Energy Build. 138, 526–529 (2017)CrossRefGoogle Scholar
  20. 20.
    Wang, H., Zhang, T., Wang, J., et al.: Research on data-driven method for TOD period identification of regional traffic. J. Wuhan Univ. Technol. (Transp. Sci. Eng.) 38(1), 40–45 (2014)Google Scholar
  21. 21.
    Wen, C., Lu, F., Bao, Z., et al.: A summary of data-driven micro fault diagnosis methods. Acta Autom. Sin. 42(9), 1285–1299 (2016)Google Scholar
  22. 22.
    Erlingsson, U.: Data-driven software security: models and methods. In: Proceedings of 2016 IEEE 29th Computer Security Foundations Symposium (CSF), pp. 9–15 (2016)Google Scholar
  23. 23.
    Jiang, J., Sekar, V., Stoica I., et al.: Unleashing the potential of data-driven networking. In: Proceedings of 9th International Conference on COMmunication Systems & NETworkS (COMSNET) (2017)Google Scholar
  24. 24.
    Pinelli, F., Nair, R., Calabrese, F., et al.: Data-driven transit network design from mobile phone trajectories. IEEE Trans. Intell. Transp. Syst. 17(6), 1724–1733 (2016)CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Computer Science and TechnologyWuhan University of TechnologyWuhanChina
  2. 2.Hubei Key Laboratory of Transportation Internet of ThingsWuhanChina

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