Improving Post Contract Cost-Control Estimates using Artificial Intelligence (AI) and Machine Learning (ML)

Construction projects infamously suffer from significant financial losses and possible ultimate project failure as a result of poor financial management due to poor budgeting, cost prediction, monitoring, and not well-informed commercial management decisions. To date, various cost management techniques and models have been utilised to ensure cost performance, however, building projects are rarely finished within the budgeted cost and time frame.

For contracting organisations, successful construction projects are mainly the ones that meet or exceed the expected return on investment (ROI). Effective financial management occurs when line items whose actual costs exceed the budgeted costs are identified, total cost at completion is estimated and profit margin is maintained or maximised. Current studies show profit margins are typically slender i.e., between 2%-7% from large annual turnovers. Globally, despite the improvements in construction financial management, cost overruns remain a major issue with project performance.

Studies speculate that successful controls happen through the application of mature and practical commercial management techniques. However, the government has placed a strong emphasis on the need for the construction industry to transition from a change agenda that was management-focused to one that is more technocentric and sees measures to improve the sector through the use of digital technology and other Industry 4.0-enabling technologies.

Currently, artificial intelligence (AI) is transforming sectors like manufacturing, retail, and telecommunications, with the construction industry being one of the least digitised industries. AI has the capability to significantly improve construction industry efficiency and service processes, along with enhancing automation. Also, it is able to give businesses a more competitive edge over traditional methods. Using artificial intelligence (AI) and machine learning (ML), the doctoral research will propose workable digital solutions that can address obstacles and promote opportunities to enhance cost control performance for building projects.

This doctoral research aims to develop a novel approach that utilises AI and ML capabilities to: generate more reliable and accurate budgets, accurately monitor performance, accurately identify possible causes of variance; and suggest effective corrective actions for ongoing and future projects occur. AI and ML methods will be employed in mining historical data in a variety of formats and quality levels to facilitate analysis and expand the methods' ability to generate more precise estimates.

The approach also has the potential to deliver improved post-contract financial management by performing cost value reconciliations (CVR), cashflow calculations, budgeting, identifying possible causes of variance, as well as suggesting corrective actions. Ultimately, the project will help curb risk to orgnaisations profit margin, keep project costs within budgets, and forecast the consequences of decisions and out-turn costs.

This project will lie at with the interface between the Built environment and data science/AI. Motivated applicants from both disciplines are welcome but must be able to work well as a part of a multi-disciplinary team. The scope of the project can be tailored as required, and the successful applicant will be supported by a multi-disciplinary team of supervisors.

Academic qualifications

A first-class honours degree, or a distinction at master level, or equivalent achievements in Built Environment or Computer Science. 

English language requirement

If your first language is not English, comply with the University requirements for research degree programmes in terms of English language.

Application process

Prospective applicants are encouraged to contact the supervisor, Dr. Nimi Dan-Jumbo ([Email Address Removed]) to discuss the content of the project and the fit with their qualifications and skills before preparing an application. 

Contact details

Should you need more information, please email [Email Address Removed].

The application must include: 

Research project outline of 2 pages (list of references excluded). The outline may provide details about

• Background and motivation, explaining the importance of the project, should be supported also by relevant literature. You can also discuss the applications you expect for the project results.
• Research questions or
• Methodology: types of data to be used, approach to data collection, and data analysis methods.
• List of references

The outline must be created solely by the applicant. Supervisors can only offer general discussions about the project idea without providing any additional support.

• Statement no longer than 1 page describing your motivations and fit with the project.
• Recent and complete curriculum vitae. The curriculum must include a declaration regarding the English language qualifications of the candidate.
• Supporting documents will have to be submitted by successful candidates.
• Two academic references (but if you have been out of education for more than three years, you may submit one academic and one professional reference), on the form can be downloaded here.

Applications can be submitted here.

Download a copy of the project details here.