Particle Swarm Optimization Based Approach for Estimation of Costs and Duration of Construction Projects

Tarq Zaed Khalaf, Hakan Çağlar, Arzu Çağlar, Ammar Nasiri Hanoon


Cost and duration estimation is essential for the success of construction projects. The importance of decision making in cost and duration estimation for building design processes points to a need for an estimation tool for both designers and project managers. Particle swarm optimization (PSO), as the tools of soft computing techniques, offer significant potential in this field. This study presents the proposal of an approach to the estimation of construction costs and duration of construction projects, which is based on PSO approach. The general applicability of PSO in the formulated problem with cost and duration estimation is examined. A series of 60 projects collected from constructed government projects were utilized to build the proposed models. Eight input parameters, such as volume of bricks, the volume of concrete, footing type, elevators number, total floors area, area of the ground floor, floors number, and security status are used in building the proposed model. The results displayed that the PSO models can be an alternative approach to evaluate the cost and-or duration of construction projects. The developed model provides high prediction accuracy, with a low mean (0.97 and 0.99) and CoV (10.87% and 4.94%) values. A comparison of the models’ results indicated that predicting with PSO was importantly more precise.


Cost; Duration; Construction Project; Particle Swarm Optimization; Managing Projects; Decision Making.


Foussier, Pierre Marie Maurice. From Product Description to Cost: A Practical Approach: Volume 1: The Parametric Approach. Springer Science & Business Media, (2006).

Layer, Alexander, Erik Ten Brinke, Fred Van Houten, Hubert Kals, and Siegmar Haasis. “Recent and Future Trends in Cost Estimation.” International Journal of Computer Integrated Manufacturing 15, no. 6 (January 2002): 499–510. doi:10.1080/09511920210143372.

Rardin, Ronald L., and Ronald L. Rardin. Optimization in operations research. Vol. 166. Upper Saddle River, NJ: Prentice Hall, (1998).

Chinneck, John W. "Practical optimization: a gentle introduction." Systems and Computer Engineering), Carleton University, Ottawa, (2006). Available online:

Van Den Bergh, Frans. "An analysis of particle swarm optimizers." PhD diss., University of Pretoria, (2007).

Bromilow, F. J. "Measurement and scheduling of construction time and cost performance in the building industry." The Chartered Builder 10, no. 9 (1974): 57-65.

Carr, Robert I. "Simulation of construction project duration." Journal of the Construction Division 105, no. 2 (1979): 117-128.

Wang, Yu-Ren, Chung-Ying Yu, and Hsun-Hsi Chan. “Predicting Construction Cost and Schedule Success Using Artificial Neural Networks Ensemble and Support Vector Machines Classification Models.” International Journal of Project Management 30, no. 4 (May 2012): 470–478. doi:10.1016/j.ijproman.2011.09.002.

Hong, Yuan, Haibo Liao, and Yazhi Jiang. “Construction Engineering Cost Evaluation Model and Application Based on RS-IPSO-BP Neural Network.” Journal of Computers 9, no. 4 (April 1, 2014). doi:10.4304/jcp.9.4.1020-1025.

Zima, Krzysztof. “The Case-Based Reasoning Model of Cost Estimation at the Preliminary Stage of a Construction Project.” Procedia Engineering 122 (2015): 57–64. doi:10.1016/j.proeng.2015.10.007.

Lee, Dongoun, Seungho Kim, and Sangyong Kim. “Development of Hybrid Model for Estimating Construction Waste for Multifamily Residential Buildings Using Artificial Neural Networks and Ant Colony Optimization.” Sustainability 8, no. 9 (September 1, 2016): 870. doi:10.3390/su8090870.

Juszczyk, Michał, Agnieszka Leśniak, and Krzysztof Zima. “ANN Based Approach for Estimation of Construction Costs of Sports Fields.” Complexity 2018 (2018): 1–11. doi:10.1155/2018/7952434.

Hegazy, T_, P. Fazio, and O. Moselhi. "Developing practical neural network applications using back‐propagation." Computer‐Aided Civil and Infrastructure Engineering 9, no. 2 (1994): 145-159. doi:10.1111/j.1467-8667.1994.tb00369.x.

Hanoon, Ammar N., M. S. Jaafar, Farzad Hejazi, and Farah N.A. Abdul Aziz. “Energy Absorption Evaluation of Reinforced Concrete Beams Under Various Loading Rates Based on Particle Swarm Optimization Technique.” Engineering Optimization 49, no. 9 (December 2016): 1483–1501. doi:10.1080/0305215x.2016.1256729.

Hanoon, Ammar N., M.S. Jaafar, Farzad Hejazi, and Farah N.A. Abdul Aziz. “Strut-and-Tie Model for Externally Bonded CFRP-Strengthened Reinforced Concrete Deep Beams Based on Particle Swarm Optimization Algorithm: CFRP Debonding and Rupture.” Construction and Building Materials 147 (August 2017): 428–447. doi:10.1016/j.conbuildmat.2017.04.094.

Mir, Mahdi, Majid Kamyab, Milad Janghorban Lariche, Amin Bemani, and Alireza Baghban. “Applying ANFIS-PSO Algorithm as a Novel Accurate Approach for Prediction of Gas Density.” Petroleum Science and Technology 36, no. 12 (March 22, 2018): 820–826. doi:10.1080/10916466.2018.1446176.

Venkataiah, V., Ramakanta Mohanty, J. S. Pahariya, and M Nagaratna. “Application of Ant Colony Optimization Techniques to Predict Software Cost Estimation.” Computer Communication, Networking and Internet Security (2017): 315–325. doi:10.1007/978-981-10-3226-4_32.

Eberhart, Russell C., Yuhui Shi, and James Kennedy. Swarm intelligence. Elsevier, (2001).

Shi, Yuhui, and Russell Eberhart. "A modified particle swarm optimizer." In 1998 IEEE international conference on evolutionary computation proceedings. IEEE world congress on computational intelligence (Cat. No. 98TH8360), IEEE, (1998): 69-73. doi:10.1109/ICEC.1998.699146.

Al-Sulttani, Ali O., Amimul Ahsan, Ammar N. Hanoon, A. Rahman, N.N.N. Daud, and S. Idrus. “Hourly Yield Prediction of a Double-Slope Solar Still Hybrid with Rubber Scrapers in Low-Latitude Areas Based on the Particle Swarm Optimization Technique.” Applied Energy 203 (October 2017): 280–303. doi:10.1016/j.apenergy.2017.06.011.

Salimi, Shide, Mohammed Mawlana, and Amin Hammad. “Performance Analysis of Simulation-Based Optimization of Construction Projects Using High Performance Computing.” Automation in Construction 87 (March 2018): 158–172. doi:10.1016/j.autcon.2017.12.003.

Banyhussan, Qais S., Ammar N. Hanoon, Ali Al-Dahawi, Gürkan Yıldırım, and Ali A. Abdulhameed. “Development of Gravitational Search Algorithm Model for Predicting Packing Density of Cementitious Pastes.” Journal of Building Engineering 27 (January 2020): 100946. doi:10.1016/j.jobe.2019.100946.

Lavanya, Dama, and Siba K. Udgata. “Swarm Intelligence Based Localization in Wireless Sensor Networks.” Multi-Disciplinary Trends in Artificial Intelligence (2011): 317–328. doi:10.1007/978-3-642-25725-4_28.

Frank, Ildiko E., and Roberto Todeschini. The data analysis handbook. Vol. 14. Elsevier, (1994).‏

Bland, J. Martin, and Douglas G. Altman. “Agreement Between Methods of Measurement with Multiple Observations Per Individual.” Journal of Biopharmaceutical Statistics 17, no. 4 (July 2, 2007): 571–582. doi:10.1080/10543400701329422.

Smith, Geoffrey Nesbitt. "Probability and statistics in civil engineering." Collins professional and technical books 244 (1986).

Pimentel-Gomes, F. "Course of experimental statistics." Piracicaba: FEALQ 15 (2000).

Golbraikh, Alexander, and Alexander Tropsha. “Beware of Q2!” Journal of Molecular Graphics and Modelling 20, no. 4 (January 2002): 269–276. doi:10.1016/s1093-3263(01)00123-1.

Roy, Partha Pratim, and Kunal Roy. “On Some Aspects of Variable Selection for Partial Least Squares Regression Models.” QSAR & Combinatorial Science 27, no. 3 (March 2008): 302–313. doi:10.1002/qsar.200710043.

Full Text: PDF

DOI: 10.28991/cej-2020-03091478


  • There are currently no refbacks.

Copyright (c) 2020 Tarq Zaed khalaf, Hakan Çağlar, Arzu Çağlar, Ammar Nasiri Hanoon

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.