Advertisement

Cluster Computing

, Volume 22, Supplement 4, pp 9357–9370 | Cite as

Distinguishing investment changes in metro construction project based on a factor space algorithm

  • Dong Wang
  • Jingkuang LiuEmail author
  • Yujing Chen
Article

Abstract

Metro construction projects are characterized by long construction periods and complex construction procedures. Therefore, contract changes involve significant information. To control metro construction investment, a method to distinguish between contract changes and extract the key influencing factors is required. In this paper, contract change information is processed and analyzed based on space factors and factor base theory. Evaluation of the proposed method reveals the following. (1) The demonstrated approach is feasible for deductive to semantic reasoning to determine and describe the concepts related to contract changes. The contract changes for various metro construction projects can be distinguished and the key factors of these contract changes can be determined. (2) Under the conditions of object and factor collection, different concept grids can be obtained by sorting the diversity factors. It is found that the same factors are included in the concept grid of each object, but different factors are contained in the concept grid of the corresponding object. (3) The main factors causing investment changes for metro construction contracts include differences in geological conditions or force majeure and conflicts between professional interfaces. When additional attention is paid to change factors, contract changes can be controlled. The results of this study provide specific guidance for governments to control metro construction investment effectively.

Keywords

Factor spaces Metro construction project Contract change Conceptual analysis sheet 

Notes

Acknowledgements

This paper is supported by grants from the National Natural Science Foundation of China (71501052), the Natural Science Foundation of Guangdong Province (2015A030310506), the Philosophy and Social Science Planning Program of Guangdong Province (GD16XGL38), and the Philosophy and Social Science Planning Program of Guangzhou (2016GZQN32). The authors would like to express their sincerely gratitude to Guangzhou Metro Corporation for providing the relevant contract data and financial support for the research (HT160103). The author would like to acknowledge the valuable suggestions of the editor and three anonymous reviewers.

References

  1. 1.
    Thirteen-Five Development Instruction in City Transportation of China. National Development and Reform Commission. http://www.ndrc.gov.cn/fzgggz/fzgh/ghwb/gjjgh/201707/W020170720477719367685.pdf
  2. 2.
    Wei, H.M.: A study on the Development Strategy Optimization of Guangzhou Subway Co., Ltd. Lanzhou: Lanzhou University (2014)Google Scholar
  3. 3.
    Li, H.Q.: The Potential Safety Hazard of Urban Rail Transit Checking and Controlling Method Research and Practice–Case Study of Guangzhou Metro. South China University of Technology, Guangzhou (2016)Google Scholar
  4. 4.
    Li, H.X.: Factor spaces and mathematical frame of knowledge representation(VII)—mulitifactorial decision making with multiple objectives. Fuzzy Syst. Math. 9(12), 15–24 (1995)Google Scholar
  5. 5.
    Li, H.X.: Factor spaces and mathematical frame of knowledge representation(VI)—generation of ASMm-func and general models of mulitifactorial decision making. J. Syst. Eng. 14(1), 1–8 (1999)MathSciNetGoogle Scholar
  6. 6.
    Hu, K.: Factor spaces and mathematical frame of knowledge representation—properties and composition of catastrophic standard multifactorial function. Comput. Eng. Appl. 46(8), 25–28 (2010)Google Scholar
  7. 7.
    Zhong, X.W., Fu, H.Y.: Comprehensive evaluation method and its application based on interval estimation. Math. Pract. Theory. 1, 74–149 (2012)Google Scholar
  8. 8.
    Yu, F.S., Luo, C.Z.: A mathematical model of diagnostic problem based on factors space theory and it’s application. Fuzzy Syst. Math. 13(1), 47–53 (1999)Google Scholar
  9. 9.
    Mi, H.G., Yan, G.X., Yu, X.K.: The mathematical model of multi-layer diagnosis-type recognition problem. J. Hebei Univ. Technol. 32(2), 77–80 (2003)Google Scholar
  10. 10.
    Zhong, Y.B.: The FHSE model of software system for synthetic evaluating enterprising. J. Guangzhou Univ. (Natl. Sci. Edn). 4(4), 316–320 (2005)Google Scholar
  11. 11.
    Zhong, Y.B., Li, Z.J.: The structures and relationships on M-hypergroup. J. Guangzhou Univ. (Natl. Sci. Edn). 4, 11–17 (2004)Google Scholar
  12. 12.
    Cheng, Q.F., Wang, T.T., Guo, S.C., Zhang, D.Y., Jing, K., Feng, L., Zhao, Z.F., Wang, P.Z.: The logistic regression from the viewpoint of the factor space theory. Int. J. Comput. Commun. Control. 4(12), 492–506 (2017)CrossRefGoogle Scholar
  13. 13.
    Wang, P.Z.: Factor spaces and factor data-bases. J. Liaoning Tech. Univ. (Natl. Sci.). 32(10), 1297–1304 (2013).  https://doi.org/10.3969/j.issn.1008-0562.2013.10.001
  14. 14.
    Wang, P.Z., Guo, S.C, Bao, Y.K.: Causality analysis in factor spaces. J. Liaoning Tech. Univ. (Natl. Sci.). 33(7), 865–870 (2014)Google Scholar
  15. 15.
    Wang, H.D., Wang, P.Z., Guo, S.C.: Improved factor analysis on factor spaces. J. Liaoning Tech. Univ. (Natl. Sci.). 34(4), 539–544 (2015)Google Scholar
  16. 16.
    Cui, T.J., Ma, Y.D.: Research on the classification method about coal mine safety situation based on the factor space. Syst. Eng. Theory Pract. 35(11), 2891–2897 (2015)Google Scholar
  17. 17.
    Yang, J.W., He, F., Cui, T.J.: Safety analysis of coal mine disaster based on factor analysis. J. Safety Sci. Technol. 4(4), 84–89 (2015)Google Scholar
  18. 18.
    Liu, H.T., Guo, S.C.: Reasoning model of causality analysis. J. Liaoning Tech. Univ. (Natl. Sci.). 34(1), 124–128 (2015)Google Scholar
  19. 19.
    Cui, T.J., Ma, Y.D.: Definition of attribute circle in factor space and its application in object classification. Comput. Eng. Sci. 37(11), 2169–2174 (2015)Google Scholar
  20. 20.
    Xu, X., Zhang, Y.R.: Control and management to contract price change in construction stage. Constr. Econ. 8, 38–40 (2000).  https://doi.org/10.14181/j.cnki.1002-851x.2000.08.013
  21. 21.
    Jiang, J.B., Yin, Y.L.: Engineering alternation management in urban rail transit projects under BT. Urban Rapid Rail. Transit. 24(2), 55–59 (2011).  https://doi.org/10.3969/j.issn.1672-6073.2011.02.013 CrossRefGoogle Scholar
  22. 22.
    Arain, F.M.: IT-based approach for effective management of project changes: a change management system. Adv. Eng. Inf. 22(4), 457–472 (2008).  https://doi.org/10.1016/j.aei.2008.05.003
  23. 23.
    Fang, J., Ren, H.: Variation controlling of construction contract performing. J. Chongqing Univ. 27(6), 137–139 (2004)Google Scholar
  24. 24.
    Yin, Y.L., Qiu, Y.: Defining similar work for the variance cost of construction contracts under the conditions of 99 FIDIC. J. Eng. Manag. 28(1), 82–87 (2014)Google Scholar
  25. 25.
    Enshassi, A., Arain, F., Al-Raee, S. : Causes of variation orders in construction projects in the Gaza Strip. J. Civil Eng. Manag. 16(4), 540–551 (2010). http://dx.doi.org/10.3846/jcem.2010.60
  26. 26.
    Serag, E., Oloufa, A., Malone, L.: Reconciliation of owner and contractor views in heavy construction projects. J. Prof. Issues Eng. Edu. Pract. 134(1), 128–137 (2008).  https://doi.org/10.1061/(ASCE)1052-3928 CrossRefGoogle Scholar
  27. 27.
    Serag, E., Oloufa, A., Malone, L., Radwan, E.: Model for quantifying the impact of change orders on project cost for U.S. roadwork construction. J. Constr. Eng. Manag. 136(9), 1015–1027 (2010).  https://doi.org/10.1061/(ASCE)CO.1943-7862.0000206 CrossRefGoogle Scholar
  28. 28.
    Li, X., Li, N.W., Yang, Z.: Coal mine safety evaluation model based on quantum genetic algorithm. Comput. Syst. Appl. 7(21), 102–106 (2012)Google Scholar
  29. 29.
    Zhao, B.F.: On the AHP-fuzzy comprehensive evaluation of threatening degree caused by mining water floods. J. Saf. Environ. 3(13), 231–234 (2013)Google Scholar
  30. 30.
    Yu, H.Y., Yang, L.: Evaluation method of mine ventilation system based on grdy relational and support vector machine model. Coal Mine Saf. 1, 181–184 (2013)Google Scholar
  31. 31.
    Charkhakan, M.H., Heravi,G.: Identification of changes formation scenarios in construction projects based on changes occurrence paths analysis. In: Proceedings of the Construction Research Congress, pp. 427–436, May, 2012  https://doi.org/10.1061/9780784412329.043
  32. 32.
    Post, N.M.: 3D modeling spurs architect to reorganize divisions of labor. ENR (Eng. News-Record). 262(14), 30–31 (2009)Google Scholar
  33. 33.
    Sun, M., Oza, T.: User survey: The benefits of an online collaborative contract change management system. Electr. J. Inf. Technol. Constr. 15, 258–268 (2010)Google Scholar
  34. 34.
    Sun, M., Oza, T.: A benefit measurement framework for an online contract change management system. Tsinghua Sci. Technol. 13(S1), 205–210 (2008).  https://doi.org/10.1016/S1007-0214(08)70150-0 CrossRefGoogle Scholar
  35. 35.
    Koufteros, X.A., Rawski, G.E., Rupak, R.: Organizational integration for product development: the effects on glitches, on-time execution of engineering change orders, and market success. Decis. Sci. 41(1), 49–80 (2010).  https://doi.org/10.1111/j.1540-5915.2009.00259.x CrossRefGoogle Scholar
  36. 36.
    Xu, W., Huang, Z., Shen, J.: The limitation to the modification of construction contracts. J. Southeast Univ. (Philos. Soc. Sci.). 14(3), 85–92 (2012).  https://doi.org/10.3969/j.issn.1671-511X.2012.03.017
  37. 37.
    Shojafar, M., Cordeschi, N., Baccarelli, E.: Energy-efficient adaptive resource management for real-time vehicular cloud services. IEEE Trans. Cloud Comput. 99, 1–1 (2016).  https://doi.org/10.1109/tcc.2016.2551747
  38. 38.
    Wang, P.Z., Sugeno, M.: The factors field and background structure for fuzzy subsets. Fuzzy Math. 2, 45–54 (1982)MathSciNetGoogle Scholar
  39. 39.
    Wang, P.Z.: Factor space and description of concepts. J. Softw. 1, 30–40 (1992).  https://doi.org/10.13328/j.cnki.jos.1992.01.005
  40. 40.
    Wang, P.Z., Li, H.X.: A Mathematical Theory on Knowledge Representation. Tianjin Scientific and Technical Press, Tianjin (1994)Google Scholar
  41. 41.
    Chai, J., Jiang, Q.Y., Cao, Z.K., Zhou, H.: Industrial fault diagnosis based on the variable weight theory of factor space. J. Xiamen Univ. (Natl. Sci.). 41(4), 448–452 (2002).  https://doi.org/10.3321/j.issn:0438-0479.2002.04.012
  42. 42.
    Wang, Y.H.: The Study of Subject Identification Based on Feature Space. Southwest Jiaotong University, Chengdu (2004)Google Scholar
  43. 43.
    Chen, T.Q.: Internet Resource Modeling and Self-Assembly Methods Based on Factor Spaces Theory. National University of Defense Technology, Changsha (2006)Google Scholar
  44. 44.
    Zhang, M., Song, Y.B., Jiang, Z.Y.: Simulation of on-off control based on the factor space and variable weight theory. J. Xiamen Univ. (Natl. Sci.). 51(4), 671–675 (2012).  https://doi.org/10.3321/j.issn:0438-0479.2006.03.010
  45. 45.
    Peng, X.T., Kandel, A., Wang, Z.: Concepts, rules, and fuzzy reasoning: a factor space approach. IEEE Trans. Syst. Man Cybernet. 21(1), 194–205 (1991)MathSciNetCrossRefGoogle Scholar
  46. 46.
    William Ibbs, C.: Quantitative impacts of project change: size issues. J. Constr. Eng. Manag. 123(3), 308–311 (1997).  https://doi.org/10.1061/(ASCE)0733-9364(1997)123:3(308) CrossRefGoogle Scholar
  47. 47.
    Giegerich, D.B.: Early warning signs of troubled projects. Ace Int. Trans. 1, 2–10 (2002)Google Scholar
  48. 48.
    Werderitsch, A.J., Krebs, J.E.: Claims avoidance–a project management primer. AACE Int. Trans. pCDR01 (2000)Google Scholar
  49. 49.
    Li, S,N., Qin, Z., Song, H.B.: A temporal-spatial method for group detection, locating and tracking. IEEE Special Sect. Cooperat. Intell. Sens. 4, 4484–4494 (2016).  https://doi.org/10.1109/ACCESS.2016.2600623
  50. 50.
    Lin, C., Wang, P.Y., Song, H.B., Zhou, Y.H., Liu, Q.: A differential privacy protection scheme for sensitive big data in body sensor networks. Ann. Telecommun. 71(9–10), 465–475 (2016).  https://doi.org/10.1007/s12243-016-0498-7 CrossRefGoogle Scholar
  51. 51.
    Fang, Y.F.: Research on Visual Methods of Coal Enterprise’s’ Safety Management. China University of Mining and Technology, Beijing (2015)Google Scholar
  52. 52.
    Tjell, J., Bosch-Sijtsema, P.M.: Visual management in mid-sized construction design projects. Proced. Econ. Financ. 21, 193–200 (2015)CrossRefGoogle Scholar
  53. 53.
    Jaca, C., Viles, E., Jurburg, D., Tanco, M.: Do companies with greater deployment of participation systems use Visual Management more extensively? An exploratory study. Int. J. Prod. Res. 52(6), 1755–1770 (2014).  https://doi.org/10.1080/00207543.2013.848482

Copyright information

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

Authors and Affiliations

  1. 1.School of ManagementGuangzhou UniversityGuangzhouPeople’s Republic of China
  2. 2.School of InsuranceGuangdong University of FinanceGuangzhouPeople’s Republic of China
  3. 3.School of ManagementGuangdong University of TechnologyGuangzhouPeople’s Republic of China

Personalised recommendations