Journal of Structural and Construction Engineering

Journal of Structural and Construction Engineering

Using artificial neural network (ANN) to estimate the cost of residential building project in the feasibility phase

Document Type : Original Article

Authors
Behnam Soltanian 1
ٍEhsan Eshtehardian 2
Mojtaba Azizi 3
1 Master of Science, Faculty of Art & Architecture, Tarbiat Modares University, Tehran, Iran.
2 Associate professor, Faculty of Art & Architecture, Tarbiat Modares University, Tehran, Iran.
3 Assistant Professor, Faculty of Art & Architecture, Tarbiat Modares University, Tehran, Iran.
10.22065/jsce.2022.340013.2803
Abstract
Cost is considered as one of the main challenges for each project manager in the construction industry and one of the criteria that measure the success of the project is the comparison between the cost of the project with the planned budget at the beginning of the project. For this purpose, an accurate forecast in the early stages of the project can greatly assist the decisions that are taken in the feasibility phase. This accurate cost estimate will help us choose the best implementation systems in different parts of the building according to the budget we plan, and avoid the options that may not lead to our main objectives in the project. in this study, we intend to design a model to predict the cost of construction in a feasibility stage using artificial neural network (ANN) as one of the tools of artificial intelligence field, whose error is less than traditional methods and calculation time is much faster than methods such as bottom-top cost estimation. for this purpose, we have identified the most important factors that affect construction cost by experts in housing investment group and then we have collected data from the past projects. In the next stage we have used MATLAB program to build the construction cost estimation model using ANN based on data gathered from past projects on housing investment group. In the end, the model based on the obtained results has a precision of 89.56 %. Finally, a graphic user interface is designed as an EXE. file, which facilitates the use of the model for users to estimate the cost of new projects and eliminates the need for a MATLAB application to use the model.
Keywords
Cost Estimation
Artificial Neural Network
Construction Project
Feasibility Phase
Project Management
Subjects
  • Mahamid, I. (2015). Factors affecting cost estimate accuracy: Evidence from Palestinian construction projects. International Journal of Management Science and Engineering Management, 10(2), 117-125.
  • Magdum, S. K., & Adamuthe, A. C. (2017). CONSTRUCTION COST PREDICTION USING NEURAL NETWORKS. ICTACT Journal on soft computing, 8(1).
  • El-Sawalhi, N. I., & Shehatto, O. (2014). A neural network model for building construction projects cost estimating. Journal of Construction Engineering and Project Management, 4(4), 9-16.
  • Arafa, M., & Alqedra, M. (2011). Early stage cost estimation of buildings construction projects using artificial neural networks. Journal of Artificial Intelligence, 4(1).
  • Chandanshive, V., & Kambekar, A. R. (2019). Estimation of building construction cost using artificial neural networks. Journal of Soft Computing in Civil Engineering, 3(1), 91-107.
  • Barros, L. B., Marcy, M., & Carvalho, M. T. (2018). Construction cost estimation of Brazilian highways using artificial neural networks. International Journal of Structural and Civil Engineering Research, 7(3), 283-289.
  • Kulkarni, P., Londhe, S., & Deo, M. (2017). Artificial neural networks for construction management: a review. Journal of Soft Computing in Civil Engineering, 1(2), 70-88.
  • Waziri, B. S., Bala, K., & Bustani, S. A. (2017). Artificial neural networks in construction engineering and management. International Journal of Architecture, Engineering and Construction, 6(1), 50-60.
  • Ambrule, V. R., & Bhirud, A. N. (2017). Use of artificial neural network for pre design cost estimation of building projects. Interational Journal on Recent and Innovation Trends in Computing and Communication, 5(2), 173-176.
  • Dandan, T. H., Sweis, G., Sukkari, L. S., & Sweis, R. J. (2019). Factors affecting the accuracy of cost estimate during various design stages. Journal of Engineering, Design and Technology, 18(4), 787-819.
  • Agyekum-Mensah, G. (2019). The degree of accuracy and factors that influence the uncertainty of SME cost estimates. International Journal of Construction Management, 19(5), 413-426.
  • Yadav, R., Vyas, M., Vyas, V., & Agrawal, S. (2016). Cost estimation model (CEM) for residential building using artificial neural network. International Journal of Engineering Research & Technology (IJERT), 5(1), 430-432.
  • Al-Zwainy, F. M., & Aidan, I. A. A. (2017). Forecasting the cost of structure of infrastructure projects utilizing artificial neural network model (highway projects as case study). Indian J. Sci. Technol, 10(20), 1-12.
  • Emsley, M. W., Lowe, D. J., Duff, A. R., Harding, A., & Hickson, A. (2002). Data modelling and the application of a neural network approach to the prediction of total construction costs. Construction Management & Economics, 20(6), 465-472.
  • Günaydın, H. M., & Doğan, S. Z. (2004). A neural network approach for early cost estimation of structural systems of buildings. International journal of project management, 22(7), 595-602.
  • Kim, G. H., An, S. H., & Kang, K. I. (2004). Comparison of construction cost estimating models based on regression analysis, neural networks, and case-based reasoning. Building and environment, 39(10), 1235-1242.
  • Aibinu, A. A., Dassanayake, D. H. A. R. M. A., & Thien, V. C. (2011, June). Use of artificial intelligence to predict the accuracy of pre-tender building cost estimate. In Proceeding of the Conference of Management and Innovation for a Sustainable Built Environment, Amsterdam, the Netherlands.
  • Petroutsatou, K., Georgopoulos, E., Lambropoulos, S., & Pantouvakis, J. P. (2012). Early cost estimating of road tunnel construction using neural networks. Journal of construction engineering and management, 138(6), 679.
  • Kim, G. H., Shin, J. M., Kim, S., & Shin, Y. (2013). Comparison of school building construction costs estimation methods using regression analysis, neural network, and support vector machine.
  • Bala, K., Bustani, S. A., & Waziri, B. S. (2014). A computer-based cost prediction model for institutional building projects in Nigeria: an artificial neural network approach. Journal of Engineering, Design and Technology.
  • Hyari, K. H., Al-Daraiseh, A., & El-Mashaleh, M. (2016). Conceptual cost estimation model for engineering services in public construction projects. Journal of Management in Engineering, 32(1), 04015021.
  • Alqahtani, A., & Whyte, A. (2016). Estimation of life-cycle costs of buildings: regression vs artificial neural network. Built Environment Project and Asset Management.
  • Leśniak, A., & Juszczyk, M. (2018). Prediction of site overhead costs with the use of artificial neural network based model. Archives of Civil and Mechanical Engineering, 18(3), 973-982.
  • Elmousalami, H. H. (2019). Intelligent methodology for project conceptual cost prediction. Heliyon, 5(5), e01625.
  • Hashemi, S. T., Ebadati E, O. M., & Kaur, H. (2019). A hybrid conceptual cost estimating model using ANN and GA for power plant projects. Neural Computing and Applications, 31(7), 2143-2154.
  • Tijanić, K., Car-Pušić, D., & Šperac, M. (2020). Cost estimation in road construction using artificial neural network. Neural Computing and Applications, 32(13), 9343-9355.
  • Hakami, W., & Hassan, A. (2019). Preliminary Construction Cost Estimate in Yemen by Artificial Neural Network. Baltic Journal of Real Estate Economics and Construction Management, 7(1), 110-122.
  • Cheng, M. Y., Cao, M. T., & Herianto, J. G. (2020). Symbiotic organisms search-optimized deep learning technique for mapping construction cash flow considering complexity of project. Chaos, Solitons & Fractals, 138, 109869.
Journal of Structural and Construction Engineering
Volume 10, Issue 6 - Serial Number 71
September 2023
Pages 20-33

  • Receive Date 24 June 2022
  • Revise Date 31 August 2022
  • Accept Date 30 November 2022