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Advances in Civil Engineering
Volume 2014, Article ID 107926, 11 pages
http://dx.doi.org/10.1155/2014/107926
Review Article

Using Intelligent Techniques in Construction Project Cost Estimation: 10-Year Survey

1University of Tabuk, Tabuk 50060, Saudi Arabia
2Binladen Research Chair on Quality and Productivity Improvement in the Construction Industry, College of Engineering, University of Hail, Saudi Arabia

Received 7 August 2014; Accepted 6 November 2014; Published 2 December 2014

Academic Editor: Samer Madanat

Copyright © 2014 Abdelrahman Osman Elfaki et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. S. R. Thomas, S.-H. Lee, J. D. Spencer, R. L. Tucker, and R. E. Chapman, “Impacts of design/information technology on project outcomes,” Journal of Construction Engineering and Management, vol. 130, no. 4, pp. 586–597, 2004. View at Publisher · View at Google Scholar · View at Scopus
  2. H. G. Melhem, “Technical council for computing and information technology,” Journal of Computing in Civil Engineering, vol. 22, no. 6, pp. 335–337, 2008. View at Google Scholar
  3. I. C. Parmee, “Computational intelligence and civil engineering-perceived problems and possible solutions,” in Towards a Vision for Information Technology in Civil Engineering, I. Flood, Ed., ASCE, Nashville, Tenn, USA, 2003. View at Google Scholar
  4. L. Holm, J. E. Schaufelberger, D. Griffin, and T. Cole, Construction Cost Estimating: Process and Practices, Pearson Education, Upper Saddle River, NJ, USA, 2005.
  5. S. Staub-French, M. Fischer, J. Kunz, and B. Paulson, “A generic feature-driven activity-based cost estimation process,” Advanced Engineering Informatics, vol. 17, no. 1, pp. 23–39, 2003. View at Publisher · View at Google Scholar · View at Scopus
  6. J. S. Shane, K. R. Molenaar, S. Anderson, and C. Schexnayder, “Construction project cost escalation factors,” Journal of Management in Engineering, vol. 25, no. 4, pp. 221–229, 2009. View at Publisher · View at Google Scholar · View at Scopus
  7. A. Albogamy, D. Scott, N. Dawood, and G. Bekr, “Addressing crucial risk factors in the middle east construction industries: a comparative study of Saudi Arabia and Jordan,” Sustainable Building Conference Coventry University, West Midlands, UK, 2013.
  8. T. Bulbul, C. J. Anumba, and J. Messner, “A system of systems approach to intelligent construction systems,” in Proceedings of the ASCE International Workshop on Computing in Civil Engineering, pp. 22–32, Austin, Tex, USA, June 2009. View at Publisher · View at Google Scholar · View at Scopus
  9. G. D. Oberlender and S. M. Trost, “Predicting accuracy of early cost estimates based on estimate quality,” Journal of Construction Engineering and Management, vol. 127, no. 3, pp. 173–182, 2001. View at Publisher · View at Google Scholar · View at Scopus
  10. D. D. Ahiaga-Dagbui and S. D. Smith, “Neural networks for modelling the final target cost of water projects,” in Proceedings of the 28th Annual ARCOM Conference, S. D. Smith, Ed., pp. 307–316, Association of Researchers in Construction Management, Edinburgh, UK, September 2012.
  11. B. Akinci and M. Fischer, “Factors affecting contractors' risk of cost overburden,” Journal of Management in Engineering, vol. 14, no. 1, pp. 67–76, 1998. View at Publisher · View at Google Scholar · View at Scopus
  12. A.-D. D. Dominic and S. D. Smith, “Rethinking construction cost overruns: cognition, learning and estimation,” Journal of Financial Management of Property and Construction, vol. 19, no. 1, pp. 38–54, 2014. View at Publisher · View at Google Scholar · View at Scopus
  13. N. Sinclair, P. Artin, and S. Mulford, “Construction cost data workbook,” in Proceedings of the Conference on the International Comparison Program, World Bank, Washington, DC, USA, 2002.
  14. A. Doyle and W. Hughes, “The influence of project complexity on estimating accuracy,” in Proceedings of the 16th Annual ARCOM Conference, pp. 623–634, Glasgow Caledonian University, 2000.
  15. I. Mahamid, “Early cost estimating for road construction projects using multiple regression techniques,” Australasian Journal of Construction Economics and Building, vol. 11, no. 4, pp. 87–101, 2011. View at Publisher · View at Google Scholar · View at Scopus
  16. D. Drew, M. Skitmore, and H. P. Lo, “The effect of client and type and size of construction work on a contractor's bidding strategy,” Building and Environment, vol. 36, no. 3, pp. 393–406, 2001. View at Publisher · View at Google Scholar · View at Scopus
  17. S. Donyavi and R. Flanagan, “The impact of effective material management on construction site performance for small and medium sized construction enterprises,” in Proceedings of the 25th Annual Conference of the Association of Researchers in Construction Management (ARCOM '09), A. R. J. Dainty, Ed., pp. 11–20, Association of Researchers in Construction Management, Nottingham, UK, September 2009. View at Scopus
  18. C. Cho and G. Edward, “Building project scope definition using project definition rating index,” Journal of Architectural Engineering, vol. 7, no. 4, pp. 115–125, 2001. View at Publisher · View at Google Scholar · View at Scopus
  19. A. P. Kaka and A. D. F. Price, “Relationship between value and duration of construction projects,” Construction Management and Economics, vol. 9, no. 4, pp. 383–400, 1991. View at Google Scholar
  20. F. J. Bromilow, M. F. Hinds, and N. F. Moody, The Time and Cost Performance of Building Contracts 1976–1986, Australian Institute of Quantity Surveyors, Sydney, Australia, 1988.
  21. F. Edum-Fotwe, “Developing benchmarks for project schedule risk estimation,” in System-Based Vision for Strategic and Creative Design, F. Bontempi, Ed., Swets & Zeitlinger, Lisse, The Netherlands, 2003. View at Google Scholar
  22. I. Flood and N. Kartam, “Neural networks in civil engineering. I: principles and understanding,” Journal of Computing in Civil Engineering, vol. 8, no. 2, pp. 131–148, 1994. View at Publisher · View at Google Scholar · View at Scopus
  23. K. Petroutsatou, E. Georgopoulos, S. Lambropoulos, and J. P. Pantouvakis, “Early cost estimating of road tunnel construction using neural networks,” Journal of Construction Engineering and Management, vol. 138, no. 6, pp. 679–687, 2012. View at Publisher · View at Google Scholar · View at Scopus
  24. C. G. Wilmot and B. Mei, “Neural network modeling of highway construction costs,” Journal of Construction Engineering and Management, vol. 131, no. 7, pp. 765–771, 2005. View at Publisher · View at Google Scholar · View at Scopus
  25. R. Jafarzadeh, J. M. Ingham, S. Wilkinson, V. González, and A. A. Aghakouchak, “Application of artificial neural network methodology for predicting seismic retrofit construction costs,” Journal of Construction Engineering and Management, vol. 140, no. 2, Article ID 04013044, 2014. View at Publisher · View at Google Scholar · View at Scopus
  26. S.-H. An, U.-Y. Park, K.-I. Kang, M.-Y. Cho, and H.-H. Cho, “Application of support vector machines in assessing conceptual cost estimates,” Journal of Computing in Civil Engineering, vol. 21, no. 4, pp. 259–264, 2007. View at Publisher · View at Google Scholar · View at Scopus
  27. B. Hola and K. Schabowicz, “Estimation of earthworks execution time cost by means of artificial neural networks,” Automation in Construction, vol. 19, no. 5, pp. 570–579, 2010. View at Publisher · View at Google Scholar · View at Scopus
  28. H. Son, C. Kim, and C. Kim, “Hybrid principal component analysis and support vector machine model for predicting the cost performance of commercial building projects using pre-project planning variables,” Automation in Construction, vol. 27, pp. 60–66, 2012. View at Publisher · View at Google Scholar · View at Scopus
  29. M.-Y. Cheng and N.-D. Hoang, “Interval estimation of construction cost at completion using least squares support vector machine,” Journal of Civil Engineering and Management, vol. 20, no. 2, pp. 223–236, 2014. View at Publisher · View at Google Scholar · View at Scopus
  30. S.-H. Ji, M. Park, and H.-S. Lee, “Case adaptation method of case-based reasoning for construction cost estimation in Korea,” Journal of Construction Engineering and Management, vol. 138, no. 1, pp. 43–52, 2012. View at Publisher · View at Google Scholar · View at Scopus
  31. S. Choi, D. Y. Kim, S. H. Han, and Y. H. Kwak, “Conceptual cost-prediction model for public road planning via rough set theory and case-based reasoning,” Journal of Construction Engineering and Management, vol. 140, no. 1, Article ID 04013026, 2014. View at Publisher · View at Google Scholar · View at Scopus
  32. K. J. Kim and K. Kim, “Preliminary cost estimation model using case-based reasoning and genetic algorithms,” Journal of Computing in Civil Engineering, vol. 24, no. 6, pp. 499–505, 2010. View at Publisher · View at Google Scholar · View at Scopus
  33. A. E. Yildiz, I. Dikmen, M. T. Birgonul, K. Ercoskun, and S. Alten, “A knowledge-based risk mapping tool for cost estimation of international construction projects,” Automation in Construction, vol. 43, pp. 144–155, 2014. View at Publisher · View at Google Scholar · View at Scopus
  34. S.-K. Lee, K.-R. Kim, and J.-H. Yu, “BIM and ontology-based approach for building cost estimation,” Automation in Construction, vol. 41, pp. 96–105, 2014. View at Publisher · View at Google Scholar · View at Scopus
  35. P. Ghoddousi, E. Eshtehardian, S. Jooybanpour, and A. Javanmardi, “Multi-mode resource-constrained discrete time-cost-resource optimization in project scheduling using non-dominated sorting genetic algorithm,” Automation in Construction, vol. 30, pp. 216–227, 2013. View at Publisher · View at Google Scholar · View at Scopus
  36. S. Kim, “Hybrid forecasting system based on case-based reasoning and analytic hierarchy process for cost estimation,” Journal of Civil Engineering and Management, vol. 19, no. 1, pp. 86–96, 2013. View at Publisher · View at Google Scholar · View at Scopus
  37. M. Rogalska, W. Bozejko, and Z. Hejducki, “Time/cost optimization using hybrid evolutionary algorithm in construction project scheduling,” Automation in Construction, vol. 18, no. 1, pp. 24–31, 2008. View at Publisher · View at Google Scholar · View at Scopus
  38. A. T. de Albuquerque, M. K. El Debs, and A. M. C. Melo, “A cost optimization-based design of precast concrete floors using genetic algorithms,” Automation in Construction, vol. 22, pp. 348–356, 2012. View at Publisher · View at Google Scholar · View at Scopus
  39. A. Afshar, A. K. Ziaraty, A. Kaveh, and F. Sharifi, “Nondominated archiving multicolony ant algorithm in time—cost trade-off optimization,” Journal of Construction Engineering and Management, vol. 135, no. 7, pp. 668–674, 2009. View at Publisher · View at Google Scholar · View at Scopus
  40. Y. Zhang and S. T. Ng, “An ant colony system based decision support system for construction time-cost optimization,” Journal of Civil Engineering and Management, vol. 18, no. 4, pp. 580–589, 2012. View at Publisher · View at Google Scholar · View at Scopus
  41. K. Karakas, I. Dikmen, and M. T. Birgonul, “Multiagent system to simulate risk-allocation and cost-sharing processes in construction projects,” Journal of Computing in Civil Engineering, vol. 27, no. 3, pp. 307–319, 2013. View at Publisher · View at Google Scholar · View at Scopus
  42. E. M. Rojas and A. Mukherjee, “Multi-agent framework for general-purpose situational simulations in the construction management domain,” Journal of Computing in Civil Engineering, vol. 20, no. 3, pp. 165–176, 2006. View at Publisher · View at Google Scholar · View at Scopus
  43. H.-J. Kim, Y.-C. Seo, and C.-T. Hyun, “A hybrid conceptual cost estimating model for large building projects,” Automation in Construction, vol. 25, pp. 72–81, 2012. View at Publisher · View at Google Scholar · View at Scopus
  44. M.-Y. Cheng, N.-D. Hoang, and Y.-W. Wu, “Hybrid intelligence approach based on LS-SVM and Differential Evolution for construction cost index estimation: a Taiwan case study,” Automation in Construction, vol. 35, pp. 306–313, 2013. View at Publisher · View at Google Scholar · View at Scopus
  45. G. H. Kim, D. S. Seo, and K. I. Kang, “Hybrid models of neural networks and genetic algorithms for predicting preliminary cost estimates,” Journal of Computing in Civil Engineering, vol. 19, no. 2, pp. 208–211, 2005. View at Publisher · View at Google Scholar · View at Scopus
  46. W.-D. Yu and M. J. Skibniewski, “Integrating neurofuzzy system with conceptual cost estimation to discover cost-related knowledge from residential construction projects,” Journal of Computing in Civil Engineering, vol. 24, no. 1, pp. 35–44, 2010. View at Publisher · View at Google Scholar · View at Scopus
  47. T. P. Williams and J. Gong, “Predicting construction cost overruns using text mining, numerical data and ensemble classifiers,” Automation in Construction, vol. 43, pp. 23–29, 2014. View at Publisher · View at Google Scholar · View at Scopus
  48. M.-Y. Cheng, H.-C. Tsai, and W.-S. Hsieh, “Web-based conceptual cost estimates for construction projects using Evolutionary Fuzzy Neural Inference Model,” Automation in Construction, vol. 18, no. 2, pp. 164–172, 2009. View at Publisher · View at Google Scholar · View at Scopus
  49. H. Zhang and F. Xing, “Fuzzy-multi-objective particle swarm optimization for time-cost-quality tradeoff in construction,” Automation in Construction, vol. 19, no. 8, pp. 1067–1075, 2010. View at Publisher · View at Google Scholar · View at Scopus
  50. S. Nunnally, Construction Methods and Management, Prentice Hall, Englewood Cliffs, NJ, USA, 2007.