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The Assessment and Allocation of Public Private Partnership Risks in the UAE

Shaban, Mazhd
A Master of Science thesis in Construction Management by Mazhd Shaban entitled, “The Assessment and Allocation of Public Private Partnership Risks in the UAE”, submitted in April 2022. Thesis advisor is Dr. Irtishad Ahmad and thesis co-advisor is Dr. Sameh El-Sayegh. Soft copy is available (Thesis, Completion Certificate, Approval Signatures, and AUS Archives Consent Form).
Public Private Partnerships (PPP) is a project delivery method used primarily for large civil infrastructure projects. It is an effective way to mitigate financial burdens on public sector entities. PPP arrangements have been extensively used by many developed and developing countries over the last few decades. Even developed countries are adopting this method to mitigate exorbitant financial demands of infrastructure projects. Yet, some countries such as the United Arab Emirates (UAE) have not embraced PPPs extensively. The UAE government is currently promoting the use of PPPs with the aim of attaining economic diversification and attracting foreign investment. However, risk management, which is an essential process in the development of PPP projects, is not properly understood and practiced in UAE. This lack of understanding diminishes chances of achieving success in a PPP project. Therefore, in order to satisfy the increasing need for PPP projects this thesis aims at identifying, assessing, and allocating the critical PPP risks in the UAE. Initially 55 PPP risks were identified and categorized through an extensive literature survey. These identified risks were then assessed based on the opinions of professionals experienced in PPP projects in the UAE. A survey was distributed, and the opinions of 53 respondents were obtained. The survey results were then used to assess and rank the identified risks using the Weighted Average (WA) and Monte Carlo Simulation (MCS) techniques. The outcome of the WA approach found no risks to be critical, while the more effective MCS approach found 23 critical risks. Lastly, the 23 critical risks were allocated using a machine learning technique, the Artificial Neural Networks (ANN) algorithm. At first, 25 risk allocation input parameters were identified through the literature review. Then a survey, to identify projects, was distributed globally. A sample size of 74 projects was collected. The survey responses were used to build and train a ‘classification ANN model’ for each risk. Most of the ANN models showed testing accuracies within the 65% -100% range. The models were then tested on two PPP projects in the UAE. Most of the models were capable of successfully predicting the risk allocation among the stakeholders.
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