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A New Hybrid Model Based on Least Squares Support Vector Machine for Project Selection Problem in Construction Industry

  • Research Article - Systems Engineering
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Abstract

Effective project selection necessitates considering numerous conflicting factors for the decision making in construction industry. Multiple factors, such as resource requirements, budget control, technological implications and governmental regulations, influence the decision to select an appropriate project selection in construction industry. Among the recent methods and models, an artificial intelligence can be recommended to achieve higher performance than traditional methods in the field. This paper introduces an effective artificial intelligence (AI) model based on modern neural networks to improve the decision making for the projects owners. A hybrid AI model based on least squares support vector machine and cross validation technique is proposed to predict the overall performance of construction projects. The presented model can be successfully utilized for long-term estimation of the performance data in construction industry. Finally, the proposed model is implemented in a real case study for construction projects. To illustrate the capabilities of the proposed model, two well-known AI models, known as back propagation neural network and radial basis function neural network, are taken into consideration. The comparisons demonstrate the superiority of the presented model in terms of its performance and accuracy through the real-world prediction problem.

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References

  1. Ebrahimnejad LS., Mousavi S.M., Tavakkoli-Moghaddam R., Hashemi H., Vahdani B.: A novel two-phase group decision-making approach for construction project selection in a fuzzy environment. Applied Mathematical Modelling, 36(9), 4197–4217 (2012)

    Article  MATH  MathSciNet  Google Scholar 

  2. Burke, R.: Project Management: Planning and Control. John Wiley and Sons, Inc. (1999)

  3. Powers, G.; Ruwanpura, J.Y.; Dolhan, G.; Chu, M.: Simulation based project selection decision analysis tooL. Proceedings of the Winter Simulation Conference, pp. 1778–1785 (2002)

  4. Mavrotas, G.; Diakoulaki, D.; Caloghirou, Y.: Project prioritization under policy restrictions. A combination of MCDA with 0–1 programming. European Journal of Operational Research 171, 296–308 (2006)

    Google Scholar 

  5. Chua D.K.H., Li D.: Key factors in bidding reasoning model. Journal of Construction Engineering and Management-ASCE 126(5), 349–357 (2000)

    Article  Google Scholar 

  6. Skitmore, M.: Identifying non-competitive bids in construction contract auctions. Omega 30, 443–449 (2002)

    Google Scholar 

  7. Lin C.T., Chen Y.T.: Bid/no-bid decision making – a fuzzy linguistic approach. International Journal of Project Management 22(7), 585–593 (2004)

    Article  Google Scholar 

  8. Cheng E.W.L., Li H.: Analytic network process applied to project selection. Journal of Construction Engineering and Management-ASCE 131, 459–466 (2005)

    Article  Google Scholar 

  9. Dikmen I., Birgonul M.T., Ozorhon B.: Project appraisal and selection using the analytic network process. Canadian Journal of Civil Engineering 34(7), 786–792 (2007)

    Article  Google Scholar 

  10. Tan, Y.T.; Shen, L.Y.; Langston, C.; Liu, Y.: Construction project selection using fuzzy TOPSIS approach. Journal of Modelling in Management 5(3), 302–315 (2010)

    Google Scholar 

  11. Puthamont, G.C.S.; Charoenngam, C.: Strategic project selection in public sector: Construction projects of the Ministry of Defence in Thailand. International Journal of Project Management 25, 178–188 (2007)

    Google Scholar 

  12. Ravanshadnia, M.; Rajaie, H.; Abbasian, H.R.: Hybrid fuzzy MADM project-selection model for diversified construction companies. Canadian Journal of Civil Engineering 37, 1082–1093 (2010)

    Google Scholar 

  13. Mousavi, S.M.; Tavakkoli-Moghaddam, R.; Vahdani, B.; Hashemi, H.; Sanjari, M.J.: A new support vector model-based imperialist competitive algorithm for time estimation in new product development projects. Robotics and Computer-Integrated Manufacturing 29, 157–168 (2013)

    Google Scholar 

  14. Vahdani B., Iranmanesh H., Mousavi S.M., Abdollahzade M.: A locally linear neuro-fuzzy model for supplier selection in cosmetics industry. Applied Mathematical Modelling 36(10), 4714–4727 (2012)

    Article  MATH  MathSciNet  Google Scholar 

  15. Tavakkoli-Moghaddam, R.; Mousavi, S.M.; Hashemi, H.; Ghodratnama, A.: Predicting the conceptual cost of construction projects: a locally linear neuro-fuzzy model. The First International Conference on Data Engineering and Internet Technology (DEIT), pp. 398–401 (2011)

  16. Mousavi, S.M.; Iranmanesh, S.H.: Least squares support vector machines with genetic algorithm for estimating costs in NPD projects. The IEEE International Conference on Industrial and Intelligent Information (ICIII 2011), Indonesia, pp. 127–131 (2011)

  17. Deng, S.; Yeh, T.-H.: Applying least squares support vector machines to airframe wing-box structural design cost estimation. Expert Systems with Applications 37, 8417–8423 (2010)

    Google Scholar 

  18. Wu Q.: A hybrid-forecasting model based on Gaussian support vector machine and chaotic particle swarm optimization. Expert Systems with Applications 37, 2388–2394 (2010)

    Article  Google Scholar 

  19. Huang, S.-C.; Chuang, P.-J.; Wub, C.-F.; Lai, H.-J.: Chaos-based support vector regressions for exchange rate forecasting. Expert Systems with Applications Expert Systems with Applications 37, 8590–8598 (2010)

    Google Scholar 

  20. Ling, F.Y.Y.; Liu, M.: Using neural network to predict performance of design-build projects in Singapore. Building and Environment 39, 1263–1274 (2004)

    Google Scholar 

  21. Albino, V.; Garavelli, A.C.: A neural network application to subcontractor rating in construction firms. International Journal of Project Management 16(1), 9–14 (1998)

    Google Scholar 

  22. Hong, W.-C.; Dong, Y.; Chen, L.-Y.; Wei, S.-Y.: SVR with hybrid chaotic genetic algorithms for tourism demand forecasting. Applied Soft Computing 11, 1881–1890 (2011)

    Google Scholar 

  23. Mashford J., Rahilly M., Davis P., Burn S.: A morphological approach to pipe image interpretation based on segmentation by support vector machine. Automation in Construction 19(7), 875–883 (2010)

    Article  Google Scholar 

  24. Chen P.-H., Shen H.-K., Lei C.-Y., Chang L.-M.: Support-vector-machine-based method for automated steel bridge rust assessment. Automation in Construction 23, 9–19 (2012)

    Article  Google Scholar 

  25. Burges, C.J.C.: A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery 2(2), 121–167 (1998)

    Google Scholar 

  26. Peng, K.L.; Wu, C.H.; Goo, Y.J.: The development of a new statistical technique for relating financial information to stock market returns. International Journal of Management 21(4), 492–505 (2004)

    Google Scholar 

  27. Vapnik, V.N.: The Nature of Statistical Learning Theory. Springer, London (1999)

  28. Chang, C.-C.; Lin, C.-J.: LIBSVM: A library for support vector machines (2008)

  29. Huang C.-F.: A hybrid stock selection model using genetic algorithms and support vector regression. Applied Soft Computing 12, 807–818 (2012)

    Article  Google Scholar 

  30. Chen, K.-Y.; Wang, C.-H.: Support vector regression with genetic algorithms in forecasting tourism demand. Tourism Management 28, 215–226 (2007)

    Google Scholar 

  31. Salgado D.R., Alonso F.J.: An approach based on current and sound signals for in-process tool wear monitoring. International Journal of Machine Tools and Manufacture 47, 2140–2152 (2007)

    Article  Google Scholar 

  32. Efron B.: Estimating the error rate of a prediction rule: Improvement on cross-validation. Journal of the American Statistical Association 78, 316–331 (1983)

    Article  MATH  MathSciNet  Google Scholar 

  33. Efron, B.; Tibshirani, R.J.: An Introduction to the Bootstrap. Chapman & Hall, London (1993)

  34. Duan, K.; Keerthi, S.; Poo, A.: Evaluation of simple performance measures for tuning SVM hyper parameters. Technical report, Singapore: National University of Singapore, Department of Mechanical Engineering (2001)

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Author information

Authors and Affiliations

  1. Faculty of Industrial and Mechanical Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran

    Behnam Vahdani

  2. Young Researchers and Elite Club, South Tehran Branch, Islamic Azad University, Tehran, Iran

    S. Meysam Mousavi & H. Hashemi

  3. Department of Business Management, Faculty of Management and Economics, Science and Research Branch, Islamic Azad University, Tehran, Iran

    M. Mousakhani

  4. Department of Industrial Engineering, Karaj Branch, Islamic Azad University, Karaj, Iran

    S. Ebrahimnejad

Authors
  1. Behnam Vahdani
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  2. S. Meysam Mousavi
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  3. H. Hashemi
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  4. M. Mousakhani
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  5. S. Ebrahimnejad
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Corresponding author

Correspondence to Behnam Vahdani.

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Vahdani, B., Mousavi, S.M., Hashemi, H. et al. A New Hybrid Model Based on Least Squares Support Vector Machine for Project Selection Problem in Construction Industry. Arab J Sci Eng 39, 4301–4314 (2014). https://doi.org/10.1007/s13369-014-1032-8

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  • DOI: https://doi.org/10.1007/s13369-014-1032-8

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