The global investment in infrastructure megaprojects is at an all-time high. However, despite the availability of various cost and schedule estimating techniques, transportation megaprojects, in particular, are renowned for having significant delays and cost overruns. The most dramatic increases, however, appear when comparing the initial cost estimate proposed by its promoters with the final decision to finance the project. Research has shown that systematic cost underestimation, psychological biases, and lack of understanding of the complexity and scope of the project are among the factors that mislead the decisions on initiating megaprojects which lead to cost increases at various phases of a project from inception to completion. Front-end decision-making with realistic cost estimates and schedules, therefore, is vital for delivering successful megaprojects.
Utilising large datasets of past megaprojects is one possible solution for successful forecasting of the costs and schedules. However, identifying what data is required to make better project decisions, is still a grey area in project management practice and falls in the territory of ‘unknown unknowns’. Researchers have suggested that the arrival of new digital technologies such as Big Data and Machine Learning could offer a wealth of opportunities to improve the performance of construction projects. However, the type of data required and the full potential of using such technologies in front-end decision-making of megaprojects is yet to be discovered. Previous research has focused mainly on identifying challenges and potential architectures of Big Data and Machine Learning. Only a limited number of studies address the use of Big Data and Machine Learning to deal with large amounts of diverse data for forecasting costs. None has been applied in the context of transportation megaprojects.
Therefore, this research seeks to develop a project data analytic model using Big Data and Machine Learning for front-end decision-making in transportation megaprojects. The deliverables of this research may include:
· A global cost database of past transportation megaprojects
· Identifying what input data is required to develop predictive analytic models from the dataset
· A predictive analytic model to support front-end decision-making with realistic project cost forecasts on future megaprojects
Eligibility
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). Additional criteria may apply so please check the specific project details before applying. Scholarships will be awarded by competitive merit, taking into account the academic ability of the applicant.
We recognise that not every talented researcher will have had the same opportunities to advance their careers. We therefore will account for any particular circumstances that applicants disclose (e.g. parental leave, caring duties, part-time jobs to support studies, disabilities etc.) to ensure an inclusive and fair recruitment process.
How to Apply
Please complete our online application form. Please select PhD Construction programme and include the project reference, title and supervisor names on your application. If these details are not included your application may not be considered. Please note that applicants may only submit ONE proposal.
Please also provide a supporting statement outlining how you would approach the research and upload this to the research proposal section of the online application. You will also be required to upload a CV, a copy of your degree certificate and relevant transcripts and one academic reference. Until your nominated referee has uploaded their statement, your application will not be marked as complete and will not be considered by the review panel. You must also provide proof of your ability in the English language (if English is not your mother tongue or if you have not already studied for a degree that was taught in English). We require an IELTS certificate showing an overall score of at least 6.5 with no component scoring less than 6.0 or a TOEFL certificate with a minimum score of 90 points.
Applications will be reviewed throughout March and applicants will be notified of the outcome of their application by the end of April 2021. Applicants MUST be available to start the course of study on a full-time basis in September 2021.
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