Abstract
Construction engineering bid evaluation is a multi-attribute decision-making evaluation. In order to prevent the negative influence of individual poor indicators from being neutralized by other indicators, strengthen the synergistic effect of indicators, and improve the rationality of decision-making, fuzzy clustering algorithm is adopted. On the basis of λ synergy degree, the standardized processing of the decision matrix is completed, and the scheme correction factor model is established, and the scheme is selected on this basis. The effectiveness and feasibility of the fuzzy clustering algorithm are illustrated by a calculation example.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Liu, B., Huo, T., Liao, P.C., Yuan, J., Sun, J., Hu, X.: Special partial least squares (PLS) path decision modeling for bid evaluation of large construction projects. KSCE J. Civ. Eng. 21(3), 1–14 (2017). https://doi.org/10.1007/s12205-016-0702-3
Xiao, L., Chen, Z.S., Zhang, X., Chang, J.P., Chin, K.S.: Bid evaluation for major construction projects under large-scale group decision-making environment and characterized expertise levels. Int. J. Comput. Intell. Syst. 32(14), 267–273 (2020)
Gajzler, M., Zima, K.: Evaluation of planned construction projects using fuzzy logic. Int. J. Civ. Eng. 15(4), 1–12 (2017). https://doi.org/10.1007/s40999-017-0177-8
Seresht, N.G., Lourenzutti, R., Fayek, A.R.: A fuzzy clustering algorithm for developing predictive models in construction applications. Appl. Soft Comput. 29(3), 341–359 (2020)
Tran, D.H., Cheng, M.Y., Pham, A.D.: Using fuzzy clustering chaotic-based differential evolution to solve multiple resources leveling in the multiple projects scheduling problem. Alex. Eng. J. 55(2), 1541–1552 (2016)
Papsdorf, K., Sima, S., Richter, G., Richter, K.: Construction and evaluation of yeast expression networks by database-guided predictions. Microbial Cell 3(6), 236–247 (2016)
Shi, Z., Wu, D., Guo, C., Zhao, C., Cui, Y., Wang, F.Y.: FCM-RDPA: TSK fuzzy regression model construction using fuzzy C-means clustering, regularization, droprule, and powerball adabelief. 12(1), 244–251 (2020)
Karunambigai, M.G., Akram, M., Sivasankar, S., Palanivel, K.: Clustering algorithm for intuitionistic fuzzy graphs. Int. J. Uncertain. Fuzz. Knowl.-Based Syst. 25(3), 367–383 (2017)
Zhang, Y.: Research on decision-making method of bid evaluation for engineering projects based on fuzzy DEA and grey relation. Open Cybern. Syst. J. 9(1), 711–718 (2015)
Zheng, L., Ouyang, W.: A model of teacher evaluation system based improved fuzzy clustering algorithm. Revista de la Facultad de Ingenieria 32(7), 461–466 (2017)
Acknowledgements
The study was supported by “Research on application of self-compacting concrete mixed with industrial waste residue in structural engineering, China (Grant No.KJQN202005502)” and “Research and Application of Damping and Noise Reducing Road Concrete, China (Grant No. KJQN201805501)”.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Deng, S., Zhang, L. (2022). Analysis and Design of Construction Engineering Bid Evaluation Considering Fuzzy Clustering Algorithm. In: J. Jansen, B., Liang, H., Ye, J. (eds) International Conference on Cognitive based Information Processing and Applications (CIPA 2021). Lecture Notes on Data Engineering and Communications Technologies, vol 84. Springer, Singapore. https://doi.org/10.1007/978-981-16-5857-0_14
Download citation
DOI: https://doi.org/10.1007/978-981-16-5857-0_14
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-5856-3
Online ISBN: 978-981-16-5857-0
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)