Improving project cost estimates using Artificial Intelligence and Machine Learning

An exciting opportunity to apply for eight fully funded PhD positions in the College of Arts, Technology and Environment, UWE Bristol.

Ref: 2223-APR-CATE15

The expected start date of these studentships is 1 April 2023.

The closing date for applications is 8 January 2023.

Please note: out of the eight projects being advertised for the CATE Studentships 2022. The projects for funding will be selected based on the merit of applicants following the process outlined below.

Construction projects notoriously suffer from poor cost prediction and cost-overrun. Poor cost performance remains a challenge for the construction industry worldwide. Despite recent methodological and technological advances in the field, project-cost overrun remains a significant challenge for the industry.  Government has emphasised the need for the industry to transform, from a management-focused change agenda to one that is more technocentric that views digital technologies and other Industry 4.0-enabling technologies as initiatives to enhance the industry. 

This doctoral study will propose practical digital solutions that can overcome the challenges and promote the opportunities to improve cost performance of construction projects using Artificial Intelligence (AI) and Machine Learning (ML). Use of AI has helped to achieve significant enhancement of service processes and industry productivity in recent years, alongside enhance automation, and provides competitive advantage as compared to conventional approaches. This research aims to develop a novel approach which will adopt AI in the interpretation and analysis of cost data that would generate more accurate and reliable order of cost estimates (OCE) and related cost risk assessment for construction projects. It will use AI and Machine Learning techniques to mine historic data of disparate quality and formats to enable its analysis and, to extend the techniques to produce more accurate cost predictions. This approach also has the potential to deliver improved cost management by shining light on the drivers of high costs and providing the evidence needed for improved cost prediction, analysis and reduction.

This project will lie at the interface between built environment and data science/AI, and motivated applicants from disciplines related to either field are welcomed 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.

The successful candidate will be a member of the Centre for Architecture and Built Environment Research Centre for Architecture and Built Environment Research (CABER). CABER seeks to develop innovative approaches, procedures, technologies and techniques that support the design, management, reconfiguration, maintenance and operation of buildings, their fabric and the environments they create. In the recent Research Assessment Exercise 88% of research in ABE was judged to be Internationally Excellent or World Leading.

For an informal discussion about the studentship, please email Dr Ndibarafinia Young Tobin at n.tobin@uwe.ac.uk.

You can also contact Professor Jessica Lamond at Jessica.Lamond@uwe.ac.uk or Dr Stephen Hall at Stephen3.Hall@uwe.ac.uk about the studentship programme.

The studentship is available from 01 April 2023 for a period of three and half years, subject to satisfactory progress and includes a tax exempt stipend, which is currently £17,668 per annum. 

In addition, full-time tuition fees will be covered for up to three years. 

Applicants must have a Bachelor’s degree in quantity surveying/ construction project management/ construction management/ or related construction degree.

Ideally the candidate will have some of the following:

• a Master’s degree or equivalent research experience
• experience in /familiarity with the UK’s construction industry
• a background in workplace design/management
• knowledge of qualitative research methods, research integrity and ethics
• Ability to learn new fields of application of data science.

Relevant experience:

• AI/Machine Learning tools and techniques
• Experience in AI and cost estimating. 

A recognised English language qualification is required.

Please submit your application online. When prompted use the reference number 2223-APR-CATE15.

Supporting documentation: You will need to upload your research proposal, all your degree certificates and transcripts and your proof of English language proficiency as attachments to your application, so please have these available when you complete the application form.

References: You will need to provide details of two referees as part of your application. At least one referee must be an academic referee from the institution that conferred your highest degree. Your referee will be asked for a reference at the time you submit your application, so please ensure that your nominated referees are willing and able to provide references within 14 days of your application being submitted.

The closing date for applications is 08 January 2023.

It is expected that interviews will take place on weeks commencing 20 February 2023. If you have not heard from us by February, we thank you for your application but on this occasion you have not been successful