Exploitation of Artificial Intelligence and Machine Learning for Quantity Surveying Practice: Using AI as an opportunity to improve Cost Certainty


   School of the Built Environment

   Applications accepted all year round  Self-Funded PhD Students Only

About the Project

Artificial Intelligence (AI) and Machine Learning (ML) often tends to go hand in hand for its propensity to mimic human cognitive functions such as problem solving, pattern recognition and respond to certain actions accordingly. The element of Machine Learning in AI uses statistical data to provide the machine with the ability to ‘learn’ from the data without having to be programmed. Thus, there is a growing focus on solutions that incorporate AI to enhance both the quality and efficiency of Quantity Surveyors in their tasks. AI can help Quantity Surveyors to improve and accelerate their tasks, with a greater efficiency, certainty and productivity by reducing waste. It can also provide better insights and analysis, and ultimately drive down cost and improve project outcomes. AI algorithms can be used to analyse large volumes of project data to uncover trends, patterns, correlations and provide insights to optimise time, cost and quality and even predict project outcomes, including cost and schedule overruns. The more data that is inputted into the computer the more chance the machine is able to predict and provide meaningful insights. 

Although the future of Quantity Surveying is promising, as the demand for construction projects continues to grow and despite the wide range of benefits received from AI, Quantity Surveyor’s profession has been susceptible to disruption from AI. AI has been able to automate many tasks undertaken by the Quantity Surveyors. For instance, Bill Of Quantities could likely be completed by an AI eventually which will feed into the client's and the contractor's requirements.

However, AI would struggle to compete the more human-focused parts of a QS’s profession. Commercial Management exercises like value management and value engineering that aid extracting value out of a project in the planning phase requires substantial amount of professional and judgemental analysis, communication and coordination between the QS and several of the other project members. As such, the wide-ranging role of a QS couldn’t be undertaken by a machine in its entirety but it can be part of their toolkit. In fact, it could be argued that digital technology including AI will allow the QS profession to progress and grow at a faster rate. AI and ML, having the potential to disrupt the role of the Quantity Surveyor’s profession in one hand, also possess the power to bring the quantity surveyors role into a whole new level. However does it, in fact, represent an opportunity rather than a threat?

The aim of this doctoral study is to investigate how AI is being used to support and assist Quantity Surveyors in their role especially in cost certainty, by understanding the application of AI. For example, how can AI be adopted to prevent cost overruns and improve cost certainty in pre-construction stages? The project should then emphasise why there is still a need for ‘humans to stay in the loop’ to provide their expertise and professional judgement. The successful applicant will investigate not only the current application of AI into QS practice but also make recommendations for future adoptions. Research in this topic can derive into multiple possible PhD avenues, using either qualitative or quantitative research, at organisational (e.g. level of assistance received from AI for cost related decisions, associated risks, etc) or operational level (e.g. trialling software that uses AI to help spot the level of effectiveness).

For informal enquiries please contact: Dr Upeksha Madanayake ()

This PhD study is available to both Home and Overseas students.

To be eligible, applicants should have a first-class honours degree in a relevant subject or a 2.1 honours degree plus Masters (or equivalent experience).


Architecture, Building & Planning (3)

Register your interest for this project



Where will I study?