Journal of Structural and Construction Engineering

Journal of Structural and Construction Engineering

Prediction of Residential Building Construction Costs in Mashhad Using Artificial Neural Networks

Document Type : Original Article

Authors
Alireza Raoufi 1
Seyyed Nasser Bashi Azghadi 2
1 Master's student in Construction Management, Khavaran Institute of Higher Education, Mashhad, Iran
2 Assistant Professor, Department of Civil Engineering and Environment, Khavaran Institute of Higher Education, Mashhad, Iran
10.22065/jsce.2025.512961.3711
Abstract
Numerous Iranian construction projects are left unfinished for various reasons, one of the most significant being incorrect cost estimation, particularly during the initial design phase. Cost prediction is among the most critical stages of civil project planning and a necessary objective for project managers, as deviation from initial estimates leads to numerous issues and legal disputes. Furthermore, controlling costs while maintaining the specified quality is the single most critical factor for continuing civil project activities, considering the heightening competition among contractors and diminishing profit margins. Thus, cost prediction is a crucial tool for controlling cost deviations for all project stakeholders, ranging from the owners to the contractors. This study aims to extract main criteria influencing cost prediction using the Delphi method, and predict construction cost using artificial neural network for residential building of Mashhad city based on 70 projects data. The findings indicate that the best model trained using neural networks has a correlation coefficient of 0.87, demonstrating the satisfactory performance of the model. Additionally, principal component analysis demonstrates that parameters such as usable area per floor, ground floor area, built-up area, the total number of units, concrete volume used in the building, construction duration, exterior wall area, and land area are first-ranked factors, while the number of floors above ground level, total floors from the foundation, and building height from the foundation are second-ranked factors. Sensitivity analysis based on obtained results revealed that exterior wall area and the number of above-ground level floors were the most significant factors in this paper. Using this method can provide employers and contractors of construction projects with a quick and appropriate estimation for the initial cost of constructing residential projects.
Keywords
Machine Learning
Artificial Neural Network
Cost Prediction
Residential Building
project

Subjects

[1] Elhag, T. M. S., & Boussabaine, A. H. (1998, September). An artificial neural system for cost estimation of construction projects. In 14th Annual ARCOM Conference (Vol. 1, pp. 219-226). University of Reading: Association of Researchers in Construction Management.‏
 [2] Sanni-Anibire, M. O., Zin, R. M., & Olatunji, S. O. (2022). Developing a preliminary cost estimation model for tall buildings based on machine learning. In Big Data and Information Theory (pp. 94-102). Routledge.‏
 [3] Roxas, C. L. C., & Ongpeng, J. M. C. (2014, March). An artificial neural network approach to structural cost estimation of building projects in the Philippines. In DLSU Research Congress (Vol. 2, No. 2, pp. 1-8). De La Salle University–Manila.‏
 [4] Khandardi, M., & Shakeri, Eqbal. (2011).  Presentation of a neural network model for estimating the cost of residential building projects. 7th International Conference on Project Management.Tehran.
[5] Ji, S. H., Ahn, J., Lee, H. S., & Han, K. (2019). Cost estimation model using modified parameters for construction projects. Advances in Civil Engineering, 2019(1), 8290935.‏
 [6] 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.‏
 [7] Naghash Toosi, H., Sabt. M. H., & Zouichi, A.(2008). Analytical study of the continuous repetition of deviations and weak performance of projects in achieving planned goals. Proceedings of the Fourth International Project Management Conference. Tehran.
 [8] Khalaf, T. Z., Çağlar, H., Çağlar, A., & Hanoon, A. N. (2020). Particle swarm optimization based approach for estimation of costs and duration of construction projects. Civil Engineering Journal, 6(2), 384-401.‏
 [9] Jiang, Q. (2020). Estimation of construction project building cost by back-propagation neural network. Journal of Engineering, Design and Technology, 18(3), 601-609.‏
 [10] 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.‏
 [11] Alshemosi, A. M. B., & Alsaad, H. S. H. (2017). Cost estimation process for construction residential projects by using multifactor linear regression technique. Criterion, 6(6), 7.‏
[12] Peyman, F., & Fathi, A. (2016). Forecasting the cost of completing construction projects using artificial neural networks and the resulting value management method. Dam and Hydropower Plant, 3(10), 11-23.
[13] Baladi, S.M., & Sajedi, S. F. (2017).Providing a sustainable forecasting model for estimating the construction cost of reinforced concrete buildings.The Second National Conference on Applied Research in Structural Engineering and Construction Management. Tehran: Sharif University of Technology.    
 [14] Soltanian, B., Eshtehardian, E.A., & Azizi, M. (2023). Using Artificial Neural Network to Estimate Construction Costs of Residential Projects in the Feasibility Phase. Structural and Construction Engineering, 10(6), 20-33.
[15] Alshamrani, O. S. (2017). Construction cost prediction model for conventional and sustainable college buildings in North America. Journal of Taibah University for Science, 11(2), 315-323.‏
[16] Al-Tawal, D. R., Arafah, M., & Sweis, G. J. (2021). A model utilizing the artificial neural network in cost estimation of construction projects in Jordan. Engineering, Construction and Architectural Management, 28(9), 2466-2488.‏
[17] Ujong J.A., Mbadike E.M., Alaneme G.U. (2022). Prediction of cost and duration of building construction using artificial neural network. Asian Journal of Civil Engineering, 23(7), 1117-1139.
[18] Wang, Y., Zuo, J., Pan, M., Tu, B., Chang, R. D., Liu, S., ... & Dong, N. (2024). Cost prediction of building projects using the novel hybrid RA-ANN model. Engineering, Construction and Architectural Management, 31(6), 2563-2582.‏
[19] Lathong, K., & Wisaeng, K. (2023). The Prediction of Low-Rise Building Construction Cost Estimation Using Extreme Learning Machine (Doctoral dissertation, Mahasarakham University).‏
[20] Matel, E., Vahdatikhaki, F., Hosseinyalamdary, S., Evers, T., & Voordijk, H. (2022). An artificial neural network approach for cost estimation of engineering services. International journal of construction management, 22(7), 1274-1287.‏
[21] 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.‏
 [22] Kanat, E. (2020). Determination of Optimum Artificial Neural Network in the Stock Market.‏ Studies Economics, Finance, Politics. Turkish: PP 239-252
 [23] Jael, A., Rashki Qale-No, M., & Zolghadr, M. (2021). Determining the Capability of Artificial Intelligence Techniques in Estimating Energy Depreciation of Stepped Spillways with Procedural Flow Regime. Amirkabir Civil Engineering Journal. 53(9), 3897-3912.
[24] Lowe D.J. ,Emsley M.W., Harding A. (2006). Predicting construction cost using multiple regression techniques. Journal of Construction Engineering and Management, 132(7), 750-758.
[25] Jafarzadeh R., Ingham J.M., Wilkinson S., González V., Aghakouchak A.A. (2014). Application of artificial neural network methodology for predicting seismic retrofit construction costs. Journal of Construction Engineering and Management, 140(2), 04013044.

  • Receive Date 28 April 2025
  • Revise Date 17 July 2025
  • Accept Date 20 August 2025