Quality Supervision Evaluation of Agent Construction Projects in Construction Stage of GIP

Abstract:

The Government investment project (GIP) plays an important role in national economy in China. This paper introduces the Agent Construction System (ACS), focuses on the supervision problems of construction agent quality management in GIP. It proposes a self-control evaluation model for construction agent based on Rough Set Theory to confirm the key supervising points, and provides a agent supervision method for government. By questionnaire survey, forms an information system of the evaluation. A heuristic attribute reduction algorithm is applied in the reduction of index attribute, and then it uses the theory of attribute importance in discernibility matrix to determine the index weight. Implement reduction in quality self-control points by using MATLAB.

• Periodical: Applied Mechanics and Materials (Volumes 295-298)
• Pages: 2515-2522
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.295-298.2515
• Online since: February 2013
• Authors: Yun Na Wu, He Ping Wang, Shuo Zhang, Ze Zhong Li
• Keywords: Construction Agent, Discernibility Matrix, Government Investment Project (GIP), Quality Supervision, Rough Set (RS)

References

[1] Ling Yan, Linggang Yang, Hua Zhao, Research frame for incentive mechanism of the construction agent in government investment project: from the perspective of project governance, 2009 IEEE International Conference on Industrial Engineering and Engineering Management (2009).

DOI: 10.1109/ieem.2009.5373083

[2] Xinai Liu, Application of construction agency system in our engineering project implementation, Journal of Sinopec management institute (2009) 79-80.

[3] Min Yan, Ling Yan, Yilin Yin, Research on Pricing Mechanism of Agent Construction for Government Investment Projects based on Risk Allocation, International Conference on Logistics Systems and Intelligent Management (2010) 1708-1712.

DOI: 10.1109/iclsim.2010.5461277

[4] Dingjun Lan, Shirong Li, Wanzhong He, A Study on owner's project delivery system and innovation of agent-construction institution, China Civil Engineering Journal Vol. 41 (7) (2008) 82-86.

[5] Yunna Wu, Dongxiao Niu. The public investment project construction agent system theory and practice, Beijing, 2007, pp.24-37.

[6] Pawlak z, Vagueness and Uncertainty：A Rough Set Prospective, International Journal of computer intelligence Vol. 2 (11) (1995) 227~232.

[7] Shaorong Feng, Dongzhan Zhang, A highly efficient incremental attribute reduction algorithm, Control and decision 4 (2011) 495-500.

[8] Wong S K M, Ziarko W, On optimal decision rules in decision tables, Bulletin of Polish Academy of Sciences Vol. 33(11/12) (1985) 663-676.

[9] Hu X H, Nick Cercone, Mining knowledge rules from databases：A rough set approach, IEEE ICDE96, New Orleans, (1996) 96-105.

[10] Hu X H, Nick Cercone, Learning in relational databases：A rough set approach, Int J of Computational Intelligence Vol. 11 (2) (1995) 323-338.

DOI: 10.1111/j.1467-8640.1995.tb00035.x

[11] Duoqian Miao, Guirong Hu, A heuristic method of knowledge reduction, Computer research and development Vol. 36 (6) (1999) 681 ~ 684.

[12] Yuanyuan Li, Jun Yun, Indicator system optimization and evaluation method based on rough set, Journal of Zhejiang university (Science Edition) Vol. 37(4) (2010) 411-415.

[13] Guoyin Wang, Rough sets theory and knowledge acquisition, Xian, (2001).