ASSESSING COLLUSION RISKS IN MANAGING CONSTRUCTION PROJECTS USING ARTIFICIAL NEURAL NETWORK

Being an insidious risk to construction projects, collusion has attracted extensive attention from numerous researchers around the world. However, little effort has ever been made to assess collusion, which is important and necessary for curbing collusion in construction projects. Specific to the context of China, this paper developed an artificial neural network model to assess collusion risk in construction projects. Based on a comprehensive literature review, a total of 22 specific collusive practices were identified first, and then refined by a two-round Delphi interview with 15 experienced experts. Subsequently, using the consolidated framework of collusive practices, a questionnaire was further developed and disseminated, which received 97 valid replies. The questionnaire data were then utilized to develop and validate the collusion risk assessment model with the facilitation of artificial neural network approach. The developed model was finally applied in a real-life metro project in which its reliability and applicability were both verified. Although the model was developed under the context of Chinese construction projects, its developing strategy can be applied in other countries, especially for those emerging economies that have a significant concern of collusion in their construction sectors, and thus contributing to the global body of knowledge of collusion.

[1]  Patrick X.W. Zou,et al.  Strategies for minimizing corruption in the construction industry in China , 2006 .

[2]  Ahmad Hadavi,et al.  Effects of high prequalification requirements , 1999 .

[3]  Howard B. Demuth,et al.  Neutral network toolbox for use with Matlab , 1995 .

[4]  Bernard Widrow,et al.  The basic ideas in neural networks , 1994, CACM.

[5]  Bon-Gang Hwang,et al.  Risk identification and allocation in underground rail construction joint ventures: contractors’ perspective , 2015 .

[6]  David Arditi,et al.  Detection of Collusive Behavior , 2012 .

[7]  Tarek Hegazy,et al.  Neural networks as tools in construction , 1991 .

[8]  Bon-Gang Hwang,et al.  Project network management: risks and contributors from the viewpoint of contractors and sub-contractors , 2015 .

[9]  Martin Skitmore,et al.  Decisions with moral content: collusion , 2000 .

[10]  Hugo Priemus,et al.  Dutch contracting fraud and governance issues , 2004 .

[11]  Min-Yuan Cheng,et al.  Evaluating subcontractor performance using evolutionary fuzzy hybrid neural network , 2011 .

[12]  O. E. Alutu,et al.  Unethical Practices in Nigerian Engineering Industries: Complications for Project Management , 2009 .

[13]  Bon-Gang Hwang,et al.  Addressing Risks in Green Retrofit Projects: The Case of Singapore , 2015 .

[14]  William P. Henry,et al.  Eliminating Corruption in Our Engineering/Construction Industry , 2009 .

[15]  G. Wu,et al.  Risk Assessment for Construction Joint Ventures in China , 2001 .

[16]  Bo Xia,et al.  Causes of Business-to-Government Corruption in the Tendering Process in China , 2017 .

[17]  Albert P.C. Chan,et al.  From Construction Megaproject Management to Complex Project Management: Bibliographic Analysis , 2015 .

[18]  Albert P.C. Chan,et al.  Measuring complexity for building projects: a Delphi study , 2012 .

[19]  A. T. C. Goh,et al.  Back-propagation neural networks for modeling complex systems , 1995, Artif. Intell. Eng..

[20]  Michael J. A. Berry,et al.  Data mining techniques - for marketing, sales, and customer support , 1997, Wiley computer publishing.

[21]  Sue Cavill,et al.  Accountability to Prevent Corruption in Construction Projects , 2008 .

[22]  Min-Yuan Cheng,et al.  Evolutionary fuzzy hybrid neural network for dynamic project success assessment in construction industry , 2012 .

[23]  Bon-Gang Hwang,et al.  Value Management in Singaporean Building Projects: Implementation Status, Critical Success Factors, and Risk Factors , 2015 .

[24]  Guomin Zhang,et al.  Modelling optimal risk allocation in PPP projects using artificial neural networks , 2011 .

[25]  Keith R. Molenaar,et al.  Programmatic Cost Risk Analysis for Highway Megaprojects , 2005 .

[26]  Hj Norussis,et al.  SPSS for Windows , 1993 .

[27]  Min-Yuan Cheng,et al.  Dynamic Prediction of Project Success Using Artificial Intelligence , 2007 .

[28]  Yu-Ren Wang,et al.  A study of preproject planning and project success using ANNs and regression models , 2008 .

[29]  Min-Yuan Cheng,et al.  Project success prediction using an evolutionary support vector machine inference model , 2010 .

[30]  Yang Miang Goh,et al.  Cognitive Factors Influencing Safety Behavior at Height: A Multimethod Exploratory Study , 2015 .

[31]  David Arditi,et al.  Predicting the risk of contractor default in Saudi Arabia utilizing artificial neural network (ANN) and genetic algorithm (GA) techniques , 2005 .

[32]  Andries G. Doree,et al.  Collusion in the Dutch construction industry: An industrial organization perspective , 2004 .

[33]  A. Chan,et al.  Understanding the risks in China's PPP projects: ranking of their probability and consequence , 2011 .

[34]  Albert P.C. Chan,et al.  Application of Delphi method in construction engineering and management research: A quantitative perspective , 2016 .

[35]  David Arditi,et al.  Analysis of collusive bidding behaviour , 2012 .

[36]  Lavagnon A. Ika.  Project Management for Development in Africa: Why Projects are Failing and What Can be Done about It , 2012 .

[37]  Ming Shan,et al.  Investigating the Effectiveness of Response Strategies for Vulnerabilities to Corruption in the Chinese Public Construction Sector , 2014, Science and Engineering Ethics.

[38]  Patrick X.W. Zou,et al.  Comparative Study on the Perception of Construction Safety Risks in China and Australia , 2009 .

[39]  Paul Bowen,et al.  Corruption in the South African construction industry: a thematic analysis of verbatim comments from survey participants , 2012 .

[40]  Albert P.C. Chan,et al.  Investigating the Causal Relationships between Causes of and Vulnerabilities to Corruption in the Chinese Public Construction Sector , 2014 .

[41]  Anna Zarkada-Fraser,et al.  A Classification of Factors Influencing Participating in Collusive Tendering Agreements , 1998 .

[42]  Kumar Neeraj Jha,et al.  Analyses and evaluation of irregularities in public procurement in India , 2011 .

[43]  Lei Zhang,et al.  Prediction of Engineering Performance: A Neurofuzzy Approach , 2005 .

[44]  Bon-Gang Hwang,et al.  Critical success factors for enterprise risk management in Chinese construction companies , 2013 .

[45]  Bon-Gang Hwang,et al.  Identifying the critical risks in underground rail international construction joint ventures: Case study of Singapore , 2013 .

[46]  Semiha Kiziltas,et al.  Prediction of organizational effectiveness in construction companies , 2005 .

[47]  Albert P.C. Chan,et al.  Critical Review of Labor Productivity Research in Construction Journals , 2014 .

[48]  M. Hussain,et al.  Hybrid neural network for prediction of CO2 solubility in monoethanolamine and diethanolamine solutions , 2010 .

[49]  Kumar Neeraj Jha,et al.  Neural Network Approach for Safety Climate Prediction , 2015 .

[50]  Ahmad Kamal Hayati Yahya,et al.  Analysis of project success factors in construction industry , 2015 .

[51]  A. Halim Boussabaine,et al.  The use of artificial neural networks in construction management: a review , 1996 .

[52]  William F. Maloney,et al.  Basic Research Needs in Construction Engineering , 1983 .

[53]  S. Jamieson.  Likert scales: how to (ab)use them , 2004, Medical education.

[54]  Albert P.C. Chan,et al.  Overview of corruption research in construction , 2014 .

[55]  Sandhya Samarasinghe,et al.  Neural Networks for Applied Sciences and Engineering , 2006 .

[56]  Bon-Gang Hwang,et al.  Influence of Trade-Level Coordination Problems on Project Productivity , 2014 .

[57]  B. Hwang,et al.  Developing Fuzzy Enterprise Risk Management Maturity Model for Construction Firms , 2013 .

[58]  Bon-Gang Hwang,et al.  Identifying critical leadership styles of project managers for green building projects , 2016 .

[59]  Bo Xia,et al.  Understanding Collusive Practices in Chinese Construction Projects , 2017 .

[60]  Michael Y. Hu,et al.  Forecasting with artificial neural networks: The state of the art , 1997 .

[61]  Eugenio Pellicer,et al.  Detecting abnormal and collusive bids in capped tendering , 2013 .

[62]  Koleola Tunwase Odusami,et al.  Professionals' Ambivalence toward Ethics in the Nigerian Construction Industry , 2010 .

[63]  Kumar Neeraj Jha,et al.  Prediction of schedule performance of Indian construction projects using an artificial neural network , 2011 .

[64]  O. E. Alutu,et al.  Unethical Practices in Nigerian Construction Industry: Prospective Engineers’ Viewpoint , 2007 .

[65]  单明,et al.  Research Trend of Collusion in Top Construction Journals , 2012 .

[66]  Martin Skitmore,et al.  Professional ethics in the construction industry , 2003 .

[67]  Akintola Akintoye,et al.  Ethical behaviour in the South African construction industry , 2007 .

[68]  Martin Loosemore,et al.  Behavioural factors influencing corrupt action in the Australian construction industry , 2015 .

[69]  Akintola Akintoye,et al.  Professional ethics in the South African construction industry , 2007 .

[70]  John A. Gambatese,et al.  Qualitative Research: Application of the Delphi Method to CEM Research , 2010 .

[71]  Martin Skitmore,et al.  Ethics in Tendering: A Survey of Australian Opinion and Practice , 1999 .

[72]  Murat Gunduz,et al.  Corruption and Internal Fraud in the Turkish Construction Industry , 2012, Science and Engineering Ethics.

[73]  Kumar Neeraj Jha,et al.  Neural Network Model for the Prediction of Safe Work Behavior in Construction Projects , 2015 .

[74]  Albert P.C. Chan,et al.  Identification of selection criteria for operational variations of the design-build system : a delphi study in China , 2012 .

[75]  Sai On Cheung,et al.  Project Dispute Resolution Satisfaction Classification through Neural Network , 2000 .

[76]  Mundia Muya,et al.  The need for technical auditing in the Zambian construction industry , 2009 .

[77]  Albert P.C. Chan,et al.  Empirical study to investigate the difficulties of implementing safety practices in the repair and maintenance sector in Hong Kong , 2012 .

[78]  Bo Xia,et al.  Measuring corruption in public construction projects in China , 2015 .

[79]  Sai On Cheung,et al.  Predicting project performance through neural networks , 2006 .

[80]  Min-Yuan Cheng,et al.  Evaluating sub-contractors performance using EFNIM , 2007 .

[81]  Yang Miang Goh,et al.  Neural network analysis of construction safety management systems: a case study in Singapore , 2013 .

[82]  Rolfe Hartley,et al.  Fighting Corruption in the Australian Construction Industry: The National Code of Practice , 2009 .

[83]  Jiayuan Wang,et al.  Identification of key liability risks of supervision engineers in China , 2009 .