Comparative Comparison of Contractors' Evaluation Criteria, Sub-Criteria and Indicators in Water Industry Tender Using Questionnaire and Text Mining

Document Type : Research Article

Authors

1 PhD student, Architecture Department, the University of Tehran, Tehran, Iran

2 project management and construction department, architecture faculty, university of tehran, tehran, iran

Abstract

The efficiency of contractors selection in infrastructure projects is always one of the main concerns of employers. The purpose of this study is to propose a new method for the specification of evaluation criteria, according to the type of project under assignment and prioritization, and to determine the proposed score of the evaluation criteria of the contractors using two different methods. The first involves statistical analysis of the questionnaires, and the second involves the text mining of the interviews. For this purpose, after library studies, fieldwork, and questionnaire design, questionnaires were given to experts in the industry, and their statistical analysis identified important criteria and sub-criteria and then interviewed another group of experts. Using the text mining and clustering of interview texts, the criteria and indices of contractors' evaluation were identified, and the results of these two methods were compared with the bidding law implementing regulations in Iran. SPSS software was used to analyze the interviews, and the K-means algorithm was used for interviews text mining. The findings of the study indicate that the results of the text mining and questionnaire are in agreement, and It can be concluded that the identified criteria have acceptable accuracy and generalization capability, and organizations can use these criteria and evaluation indicators to select the most suitable contractor in the tenders. Identified Criteria, in addition to covering all the criteria introduced in the Bidding Law Implementing Regulations, also include two new criteria, namely Claim Management and Safety Management & Quality Control.

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Main Subjects

References

[1] M. Sönmez, J.B. Yang, G.D. Holt, Addressing the contractor selection problem using an evidential reasoning approach, Engineering, Construction and Architectural Management, 8 (2001) 198-210.
[2] K.C. Lam, S. Thomas Ng, T. Hu, M. Skitmore, S.O. Cheung, Decision support system for contractor pre-qualification—artificial neural network model, Engineering, Construction and Architectural Management, 7 (2000) 251-266.
[3] D.J. Watt, B. Kayis, K. Willey, Identifying key factors in the evaluation of tenders for projects and services, International Journal of Project Management, 27 (2009) 250-260.
[4] G.D. Holt, Which contractor selection methodology?, International Journal of project management, 16 (1998) 153-164.
[5] J.B. Yang, W.C. Wang, Contractor selection by the most advantageous tendering approach in Taiwan, Journal of the Chinese Institute of Engineers, 26 (2003) 381-387.
[6] Z. Hatush, M. Skitmore, Assessment and evaluation of contractor data against client goals using PERT approach, Construction Management & Economics, 15 (1997) 327-340.
[7] S.T. Ng, R.M. Skitmore, Client and consultant perspectives of prequalification criteria, Building and environment, 34 (1999) 607-621.
[8] E. Palaneeswaran, M. Kumaraswamy, Recent advances and proposed improvements in contractor prequalification methodologies, Building and Environment, 36 (2001) 73-87.
[9] Y.I. Topcu, A decision model proposal for construction contractor selection in Turkey, Building and environment, 39 (2004) 469-481.
[10] M.K. J. Han, J. Pei, Data Mining concepts and techniques,  (2012) 1-2.
[11] D. Pickell, Structured vs Unstructured Data – What's the Difference?,  (2018).
[12] M. de Miranda Santo, G.M. Coelho, D.M. dos Santos, L. Fellows Filho, Text mining as a valuable tool in foresight exercises: A study on nanotechnology, Technological Forecasting and Social Change, 73 (2006) 1013-1027.
[13] N. Singh, C. Hu, W.S. Roehl, Text mining a decade of progress in hospitality human resource management research: Identifying emerging thematic development, International Journal of Hospitality Management, 26 (2007) 131-147.
[14] N.T. Ratrout, Subtractive clustering-based k-means technique for determining optimum time-of-day breakpoints, Journal of Computing in Civil Engineering, 25 (2010) 380-387.
[15] M. Al Qady, A. Kandil, Automatic clustering of construction project documents based on textual similarity, Automation in construction, 42 (2014) 36-49.
[16] R. Yan, Z. Ma, G. Kokogiannakis, Y. Zhao, A sensor fault detection strategy for air handling units using cluster analysis, Automation in Construction, 70 (2016) 77-88.
[17] H. Naganathan, W.O. Chong, X. Chen, Building energy modeling (BEM) using clustering algorithms and semi-supervised machine learning approaches, Automation in Construction, 72 (2016) 187-194.
[18] A.G. Kashani, A.J. Graettinger, Cluster-based roof covering damage detection in ground-based lidar data, Automation in Construction, 58 (2015) 19-27.
[19] E. Rodrigues, D. Sousa-Rodrigues, M.T. de Sampayo, A.R. Gaspar, Á. Gomes, C.H. Antunes, Clustering of architectural floor plans: A comparison of shape representations, Automation in Construction, 80 (2017) 48-65.
[20] S. Saitta, P. Kripakaran, B. Raphael, I.F. Smith, Improving system identification using clustering, Journal of Computing in Civil Engineering, 22 (2008) 292-302.
[21] I. Brilakis, L. Soibelman, Y. Shinagawa, Material-based construction site image retrieval, Journal of computing in civil engineering, 19 (2005) 341-355.
[22] S. Yarmohammadi, R. Pourabolghasem, D. Castro-Lacouture, Mining implicit 3D modeling patterns from unstructured temporal BIM log text data, Automation in Construction, 81 (2017) 17-24.
[23] Z. Ding, Z. Li, C. Fan, Building energy savings: Analysis of research trends based on text mining, Automation in Construction, 96 (2018) 398-410.
Amirkabir Journal of Civil Engineering
Volume 53, Issue 6
September 2021
Pages 2335-2352

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