Estimation of the building costs using intelligence data driven methods and determining the factors influencing on cost. (Case study: East of Tehran province)

Document Type : Original Article

Authors

1 msc student in construction engineering and management, higher education institute of pardisan

2 Assistant professor, Pardisan University of Fereydonkenar, Fereydonkenar, Iran

3 Civil Engineering Department, Tabari University, Babol, Iran

4 Assistant Professor, Department of Civil Engineering, Shahrood University of Technology

Abstract

\Today, the importance of construction costs in construction projects is increasing day by day and it is necessary to study the estimation of construction costs and the factors affecting construction costs.This research has been done around the subject of estimate the construction costs by intelligence data driven methods named Artificial Neural Network (ANN), Support Vector Machine (SVM) and Gene Expression Programming (GEP) determine the effective factors on cost and cost optimization in east of Tehran. To estimate the costs of residential buildings, in this research it's used from 46 residential buildings which were implemented in west of Tehran and it's used from the artificial intelligence method to estimate the construction costs that is used from ANN, artificial neural network method, GEP, SVM and it was used from questionnaire and Friedman test method to diagnosis the effective factors on costs to prioritize factors that can optimize the costs by possessing these two factors, the effective factors on costs and construction costs estimation . After reviews and studies we resulted that costs estimation had been done by ANN, GEP, SVM methods and by comparing these methods, GEP cause of its less errors and more predictability, was the best method than others. The effective factors on costs were determined by Friedman test method that after reviews these factors, construction costs had been optimized.

Keywords

References

  1. Toh, T. C., Ting, C., Ali, K. N., Aliagha, G. U., & Munir, O. (2012). Critical cost factors of building construction projects in Malaysia. Procedia-Social and Behavioral Sciences, Vol. 57, PP. 360-367.
  2. Cheng, Y. M. (2014). An exploration into cost-influencing factors on construction projects. International Journal of Project Management, Vol. 32, 5, PP. 850-860.
  3. Juszczyk, M. (2017). The challenges of nonparametric cost estimation of construction works with the use of artificial intelligence tools. Procedia engineering, Vol. 196, PP. 415-422.
  4. Guerrero, M. A., Villacampa, Y., & Montoyo, A. (2014). Modeling construction time in Spanish building projects. International Journal of Project Management, Vol. 32, No. 5, PP. 861-873.
  5. Kim, H. J., Seo, Y. C., & Hyun, C. T. (2012). A hybrid conceptual cost estimating model for large building projects. Automation in construction, Vol. 25, PP. 72-81.
  6. Barg, S., Flager, F., & Fischer, M. (2018). An analytical method to estimate the total installed cost of structural steel building frames during early design. Journal of Building Engineering, Vol. 15, No. 41-50.
  7. Cheng, M. Y., Tsai, H. C., & Sudjono, E. (2010). Conceptual cost estimates using evolutionary fuzzy hybrid neural network for projects in construction industry. Expert Systems with Applications, Vol. 37, No. 6, PP. 4224-4231.
  8. Bateni, S. M., Borghei, S. M., & Jeng, D. S. (2007). Neural network and neuro-fuzzy assessments for scour depth around bridge piers. Engineering Applications of Artificial Intelligence, Vol. 20, No. 3, PP. 401-414.
  9. Ince, (2004), "Prediction of fracture parameters of concrete by artificial neural networks," Engineering Fracture Mechanic, Vol. 71, No. 15, PP. 2143–59.
  10. Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine learning, Vol. 20, No. 3, PP. 273-297.
  11. Uysal, M., & Tanyildizi, H. (2012). Estimation of compressive strength of self compacting concrete containing polypropylene fiber and mineral additives exposed to high temperature using artificial neural network. Construction and Building Materials, Vol. 27, No. 1, PP. 404-414.
  12. Ashrafian, A., Amiri, M. J. T., Rezaie-Balf, M., Ozbakkaloglu, T., & Lotfi-Omran, O. (2018). Prediction of compressive strength and ultrasonic pulse velocity of fiber reinforced concrete incorporating nano silica using heuristic regression methods. Construction and Building Materials, Vol. 190, PP. 479-494.
  13. LeCun, Y., Jackel, L. D., Bottou, L., Brunot, A., Cortes, C., Denker, J. S., ... & Simard, P. (1995, October). Comparison of learning algorithms for handwritten digit recognition. In International conference on artificial neural networks, Vol. 60, PP. 53-60.
  14. Ferreira, C. (2001). Gene expression programming: a new adaptive algorithm for solving problems. arXiv preprint cs/0102027.
  15. Ashrafian, A., Gandomi, A. H., Rezaie-Balf, M., & Emadi, M. (2020). "An evolutionary approach to formulate the compressive strength of roller compacted concrete pavement", Measurement, Vol. 152, 107309.
  16. Rezaie-Balf, M., Maleki, N., Kim, S., Ashrafian, A., Babaie-Miri, F., Kim, N. W., ... & Alaghmand, S. (2019). Forecasting daily solar radiation using CEEMDAN decomposition-based MARS model trained by crow search algorithm. Energies, Vol. 12, No. 8, 1416.
  17. GeneXproTools version 5.0 [Computer software]. Gepsoft Limited, Bristol, U.K
  18. Tropsha, P. Gramatica, V.K. Gombar, (2003), "The importance of being earnest: validation is the absolute essential for successful application and interpretation of QSPR models", Mol. Inf.Vol. 22, No. 1, 69–77.
  19. Asteris, P. G., Ashrafian, A., & Rezaie-Balf, M. (2019). "Prediction of the compressive strength of self-compacting concrete using surrogate models", Computer & Concrete, Vol. 24, PP. 137-150.
  20. Ashrafian, A., Shokri, F., Amiri, M. J. T., Yaseen, Z. M., & Rezaie-Balf, M. (2020). "Compressive strength of Foamed Cellular Lightweight Concrete simulation: New development of hybrid artificial intelligence model", Construction and Building Materials, Vol. 230, 1170418.

Files

History

  • Receive Date: 01 September 2019
  • Revise Date: 25 February 2020
  • Accept Date: 07 March 2020

Share

How to cite

Statistics