• International Journal of Technology (IJTech)
  • Vol 4, No 1 (2013)

Preliminary Cost Estimation Using Regression Analysis Incorporated With Adaptive Neuro Fuzzy Inference System

Yusuf Latief, Wisnu Isvara, Andreas Wibowo

Corresponding email: wisnu.isvara@gmail.com

Published at : 17 Jan 2014
Volume : IJtech Vol 4, No 1 (2013)
DOI : https://doi.org/10.14716/ijtech.v4i1.102

Cite this article as:

Latief, Y., Isvara, W., Wibowo, A., 2018. Preliminary Cost Estimation Using Regression Analysis Incorporated With Adaptive Neuro Fuzzy Inference System. International Journal of Technology. Volume 4(1), pp. 63-72

Yusuf Latief Department of Civil Engineering, Universitas Indonesia
Wisnu Isvara Department of Civil Engineering, Universitas Indonesia
Andreas Wibowo Agency for Research and Development, Ministry of Public Works
Email to Corresponding Author


Preliminary cost estimates play an important role in project decisions at the beginning of design phase of construction project under a limited definition of scope and constraints in available information and time. This study proposes a new approach of preliminary cost estimation model using regression analysis incorporated with adaptive neuro fuzzy inference system (ANFIS). Regression analysis (RA) is used for determination of the significant parameters as input variables in ANFIS model. Datasets of 55 low-cost apartment projects in Indonesia were compiled to demonstrate the advantage of the proposed method. The mean absolute percent error (MAPE) of testing data of the proposed model is 3.98% whereas the MAPE of RA and neural network (NN) models are, respectively, 6.92% and 10.12%, thus indicating better accuracy performance of the proposed model over the latter ones.

Preliminary Cost Estimates, Regression Analysis, Neuro Fuzzy


AACE, Association for the Advancement of Cost Engineering, International Recommended Practice No. 18R-97. 2005. Cost Estimate Classification System – As Applied In Engineering, Procurement, And Construction For The Process Industries, TCM Framework: 7.3 – Cost Estimating and Budgeting

Cheng, M.Y., et al. 2010. Conceptual Cost Estimates Using Evolutionary Fuzzy Hybrid Neural Network. Journal Expert Systems With Applications, 37, pp. 4224-4231.

Ciraci, M., & Polat, D.A. 2009. Accuracy Levels of Early Cost Estimate, in Light of the Estimate Aims. Journal of Cost Engineering, Volume 51, Number 1, pp. 16-24.

Dell’Isola, M.D. 2002. Architect’s Essentials of Cost Management, AIA. The American Institute of Architects, John Wiley and Sons, New York.

Haykin, S. 1999. Neural Networks - A Comprehensive Foundation, Prentice Hall, Pearson Education.Inc, 2nd Edition.

Holm, L., Schaufelberger J.E., Griffin, D., Cole, T. 2005. Construction Cost Estimating Process and Practices, Pearson Education.Inc, Upper Saddle River, New Jersey, USA.

Jang, J.S.R. 1993. ANFIS : Adaptive-Network-Based Fuzzy Inference System. IEEE Transaction On System, Man, and Cybernetics, Volume 23, Number 3, pp. 665-685.

Ji, S.H., et al. 2010. Data Preprocessing-Based Parametric Cost Model for Building Projects: Case Studies of Korean Construction Projects. Journal of Construction Engineering and Management, ASCE, pp. 844-853

Kim, K.J., Kim, K., (2010), Preliminary Cost Estimation Model Using Case-based Reasoning and Genetic Algorithms. Journal of Computing in Civil Engineering, ASCE, pp. 499-505.

Lowe, D.J., et al. 2006. Predicting Construction Cost Using Multiple Regression Techniques. Journal of Construction Engineering and Management, ASCE, pp. 750-758.

Pratt, D. 2011. Fundamentals of Construction Estimating, Delmar, Cengage Learning, Third Edition, Clifton Park, New York, USA.

Sonmez, R., Ontepeli, B. 2009. Predesign Cost Estimation of Urban Railway Projects With Parametric Modeling. Journal of Civil Engineering and Management, Volume 15, Number 4, pp. 405-409.

Stoy, C., et al. 2008. Drivers for Cost Estimating in Early Design: Case Study of Residential Construction. Journal of Construction Engineering and Management, ASCE, pp. 32-39.

Wibowo, A., and Wuryanti, W. 2008. Capacity Factor Based Cost Models for Buildings of Various Functions. Civil Engineering Dimension, Volume 9, Number 2, pp. 70-76.