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Bayesian project diagnosis for the construction design process

Published online by Cambridge University Press:  02 November 2012

P.C. Matthews  and
A.D.M. Philip
P.C. Matthews*
Affiliation:
School of Engineering and Computing Sciences, Durham University, Durham, UK
A.D.M. Philip
Affiliation:
School of Engineering and Computing Sciences, Durham University, Durham, UK
*
Reprint requests to: P. C. Matthews, School of Engineering and Computing Sciences, Durham University, Durham DH1 3LE, UK. E-mail: [email protected]
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Abstract

This study demonstrates how subtle signals taken from the early stages within a construction process can be used to diagnose potential problems within that process. For this study, the construction process is modeled as a quasi-Markov chain. A set of six different scenarios representing various common problems (e.g., small budget, complex project) is created and simulated by suitably defining the transition probabilities between nodes in the Markov chain. A Monte Carlo approach is used to parameterize a Bayesian estimator. By observing the time taken to pass the review gateway (as measured by number of hops between activity nodes), the system is able to determine with good accuracy the problem scenario that the construction process is suffering from.

Keywords

Design ProcessMarkov ChainsMonte Carlo SimulationProject ManagementUncertainty
Type
Special Issue Articles
Information
AI EDAM , Volume 26 , Issue 4: Intelligent Decision Support and Modeling , November 2012 , pp. 375 - 391
Copyright
Copyright © Cambridge University Press 2012

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References

REFERENCES

Anderson, G.R., Mukherjee, A., & Onder, N. (2009). Traversing and querying constraint driven temporal networks to estimate construction contingencies. Automation in Construction 18, 798–813.CrossRefGoogle Scholar
Chan, A., Chan, D., Chiang, Y., Tang, B., Chan, E., & Ho, K. (2004). Exploring critical success factors for partnering in construction projects. ASCE Journal of Construction Engineering and Management 130, 188–198.CrossRefGoogle Scholar
Chapman, C.B. (1990). A risk engineering approach to project risk management. Risk Management 8, 5–16.Google Scholar
Chester, M., & Hendrickson, C. (2005). Cost impacts, scheduling impacts, and the claims process during construction. ASCE Journal of Construction Engineering and Management 131, 102– 107.CrossRefGoogle Scholar
Cho, S.H., & Eppinger, S.D. (2005). A simulation-based process model for managing complex design projects. IEEE Transactions on Engineering Management 52, 316–328.CrossRefGoogle Scholar
Clough, R.H., Sears, G.A., & Sears, S.K. (2000). Construction Project Management. New York: Wiley.Google Scholar
Cross, N. (2000). Engineering Design Methods: Strategies for Product Design. Chichester: Wiley.Google Scholar
Dissanayake, M., & Robinson Fayek, A. (2008). Soft computing approach to construction performance prediction and diagnosis. Canadian Journal of Civil Engineering 35, 764–776.CrossRefGoogle Scholar
Eckert, C., Clarkson, P.J., & Zanker, W. (2004). Change and customisation in complex engineering domains. Research in Engineering Design 15, 1–21.CrossRefGoogle Scholar
Emmitt, S., & Gorse, C.A. (2003). Construction Communication. Oxford: Blackwell.Google Scholar
Fan, C.F., & Yu, Y.C. (2004). BBN-based software project risk management. Journal of Systems and Software 73, 193–203.CrossRefGoogle Scholar
Fenton, N., Krause, P., & Neil, M. (2002). Software measurement: uncertainty and causal modeling. IEEE Software 19, 116–122.CrossRefGoogle Scholar
Flanagan, T., Eckert, C.M., & Clarkson, P.J. (2007). Extemalizing tacit overview knowledge: A model-based approach to supporting design teams. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 21, 227–242.CrossRefGoogle Scholar
Kelley, J.E., & Walker, M.R. (1959). Critical-path planning and scheduling. Proc. Eastern Joint Computer Conf.Google Scholar
Khodakarami, V., Fenton, N., & Neil, M. (2007). Project scheduling: improved approach to incorporate uncertainty using Bayesian networks. Project Management Journal 38, 116–122.CrossRefGoogle Scholar
Kim, B.C., & Reinschmidt, K.F. (2009). Probabilistic forecasting of project duration using Bayesian inference and the beta distribution. Journal of Construction Engineering and Management 135, 178–186.CrossRefGoogle Scholar
Kreyszig, E. (1999). Advanced engineering mathematics (8th ed.). New York: Wiley.Google Scholar
Lee, E., Park, Y., & Shin, J.G. (2009). Large engineering project risk management using a Bayesian belief network. Expert Systems with Applications 36, 5880–5887.CrossRefGoogle Scholar
McCabe, B., AbouRizk, S.M., & Goebel, R. (1998). Belief networks for construction performance diagnostics. Journal of Computing in Civil Engineering 12, 93–100.CrossRefGoogle Scholar
Mitropoulos, P., & Howell, G. A. (2002). Renovation projects: Design process problems and improvement mechanisms. Journal of Management in Engineering 18, 179–185.CrossRefGoogle Scholar
Nasir, D., McCabe, B., & Hartono, L. (2003). Evaluating risk in construction-schedule model (ERICS) construction schedule risk model. Journal of Construction Engineering and Management 129, 518–527.CrossRefGoogle Scholar
Neil, M., Fenton, N., & Tailor, M. (2005). Using Bayesian networks to model expected and unexpected operational losses. Risk Analysis 25, 963–972.CrossRefGoogle ScholarPubMed
Pahl, G., & Beitz, W. (1996). Engineering Design: A Systematic Approach (2nd ed.). London: Springer.CrossRefGoogle Scholar
Pandelis, D. G. (2010). Markov decision processes with multidimensional action spaces. European Journal of Operational Research 200, 625–628.CrossRefGoogle Scholar
Pearl, J. (2000). Causality: Models, Reasoning, and Inference. New York: Cambridge University Press.Google Scholar
Ritz, G.J. (1994). Total Construction Project Management. New York: McGraw–Hill.Google Scholar
Royal Institute of British Architects. (2007). RIBA Plan of Work. London: Royal Institute of British Architects.Google Scholar
Siegel, S., & Castellan, N.J.J. (1988). Nonparametric Statistics for the Behavioral Sciences (2nd ed.). New York: McGraw–Hill.Google Scholar
Soibelman, L., Liu, L.Y., Kirby, J.G., East, E.W., Caldas, C.H., & Lin, K.Y. (2003). Design review checking system with corporate lessons learned. ASCE Journal of Construction Engineering and Management 129, 475–484.CrossRefGoogle Scholar
Taha, H. (2007). Operations Research: An Introduction. New York: Pearson/Prentice Hall.Google Scholar
Von Stamm, B. (2008). Managing Innovation, Design and Creativity. Chichester: Wiley.Google Scholar
Weidl, G., Madsen, A.L., & Israelson, S. (2005). Applications of object-oriented Bayesian networks for condition monitoring, root cause analysis and decision support on operation of complex continuous processes. Computers and Chemical Engineering 29, 1996–2009.CrossRefGoogle Scholar
Williams, T. (1995). A classified bibliography of recent research relating to project risk management. European Journal of Operational Research 85, 18–38.CrossRefGoogle Scholar
Wu, H.H., & Shieh, J.I. (2006). Using a Markov chain model in quality function deployment to analyse customer requirements. International Journal of Advanced Manufacturing Technology 30, 141–146.CrossRefGoogle Scholar