Future Directions of Cost and Productivity Estimating


using Artificial Intelligence (AI)



By Danilo Arba

Milan, Italy
Lima, Peru


With an ever-increasing competitive world of engineering services, and with always thinner profit margins and decreasing market shares, the cost of a project is one of the significant criteria in decision making at the early stages of the construction industry.

To remain competitive in the market, companies must have an accurate estimate of their projects. With the rise of computing power, there is now a tendency to use Machine Learning (ML)-based methods, such as Artificial Neural Networks (ANNs), [1]for more accurate cost estimation that can remain reliable in the face of insufficient details during the tendering phase.

This technical paper will review an artificial neural network approach to the cost estimate of engineering services and construction activities. While developing the mentioned model, firstly, the influential factors that affect the costs of construction and engineering services are identified, after this, a model developed using data of multiple projects from the author experience.

Keywords:  Artificial Intelligence, Artificial Neural Network, Construction Industry, Construction Management, Construction projects, Construction Estimating, Project cost modelling, GAPPS, CIFTER


While there are no written records from the Pyramids, credible research done by Damian Zimmerman[2] back in 1997 on the Great Wall of China explained:

“The fact is, that the cost of the wall’s construction bankrupted Dynasty after Dynasty, and we should also remember that Herodotus[3], while not giving specific cost information, stated that it took 100,000 men around 20 to 23 years to construct the Great Pyramid of Khufu[4] .

Khufu or also known by his Greek name, Cheops, was the Egyptian pharaoh in the Fourth Dynasty, who was described by Herodotus as a cruel and strange figure, that prostituted his daughter as he runs out of money although the Westcar Papyrus[5] describes Khufu as a traditional oriental monarch: good-natured, amiable to his inferiors and interested in human existence and magic. Despite not being remembered as fondly as his father, the funerary cult of Khufu was still followed in the 26th Dynasty, and he became increasingly popular during the Roman period.

Figure 01: The Westcar Papyrus and the Miracle Stories of the Old Kingdom [6]

With the construction industry embarks on the much-touted journey to embrace leading-edge technologies like blockchain, digital twins, 3D printing, drones and laser scanning, it cannot lose sight of the fundamental responsibilities of meeting the critical project metrics of time and cost.

For almost seven decades the Project Management Triangle—also known as the Triple Constraint or Iron Triangle[7]—has been the base of a rubric for measuring project management success. Over the years, project teams have increased other constraints such as risk, safety, sustainability, to this list.


To read entire paper, click here

How to cite this paper: Arba, D. (2020). Future Directions of Cost and Productivity Estimating using Artificial Intelligence (AI), PM World Journal, Vol. X, Issue VIII, August. Available online at https://pmworldlibrary.net/wp-content/uploads/2021/08/pmwj108-Aug2021-Arba-future-cost-and-productivity-estimating-uesing-artificial-intelligence.pdf

About the Author

Danilo Arba

Milan, Italy
Lima, Peru

Danilo Arba is a project controls & management enthusiast, with 20 years of experience. Certified Cost Engineer and Executive MBA from Politecnico di Milano. Thorough understanding of EPC (Engineering, Procurement, and Construction) industry, with verifiable track record of planning multimillion/billion-dollar worldwide construction projects. He lived & worked all his life around the world from South America, Africa, South East Asia to Europe. Adept at building and leading cross-functional teams from project conception to completion, optimising performance, contractual, and financial deliverables. Currently furthering his education by way of a distance learning mentoring course, under the tutorage of Dr Paul D. Giammalvo, CDT, CCE, MScPM, MRICS, GPM-m Senior Technical Advisor, PT Mitrata Citragraha, to attain Guild of Project Controls certification.

Danilo lives in Milan, Italy and Lima, Peru and can be contacted at danilo.arba@mip.polimi.it


[1] What are artificial neural networks? (n.d.). Bernard Marr. https://bernardmarr.com/default.asp?contentID=2126
[2] Great Wall of China. (n.d.). Mandala Projects. https://mandalaprojects.com/ice/ice-cases/wall.htm
[3] Herodotus. (n.d.). Encyclopedia Britannica. https://www.britannica.com/biography/Herodotus-Greek-historian
[4] Egyptian art and architecture – The pyramid of Khufu. (n.d.). Encyclopedia Britannica. https://www.britannica.com/art/Egyptian-art/Pyramid-of-Khufu
[5] The Westcar papyrus. (2017, April 5). Ancient History Encyclopedia. https://www.ancient.eu/The_Westcar_Papyrus/
[6] The Westcar papyrus and the miracle stories of the old kingdom. (2016, October 28). Ancient Origins | Reconstructing the story of humanity’s past. https://www.ancient-origins.net/artifacts-ancient-writings/westcar-papyrus-and-miracle-stories-old-kingdom-006895
[7] Project management triangle | Overview of triple constraints. (n.d.). StarAgile Consulting | Certification Training Courses. https://staragile.com/blog/project-management-triangle