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AI-Based Decision Support Model for Sustainable Contractor Selection
Al Armouti, Sara Nezar
Al Armouti, Sara Nezar
Date
2025-11
Author
Advisor
Type
Thesis
Degree
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35.232-2025.54a Sara Nezar Al Armouti.pdf
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- Embargoed until 2027-02-02
Description
A Master of Science thesis in Construction Management by Sara Nezar Al Armouti entitled, “AI-Based Decision Support Model for Sustainable Contractor Selection”, submitted in November 2025. Thesis advisor is Dr. Sameh El-Sayegh. Soft copy is available (Thesis, Completion Certificate, Approval Signatures, and AUS Archives Consent Form).
Abstract
In the era of sustainable development, the construction industry plays a major role by highlighting contractors that achieve sustainable objectives. Sustainability is exceedingly required nowadays to ensure the world evolves environmentally, economically, and socially. It is required to ensure an equitable, prosperous, and resilient future for humanity. Contractors are selected for specific criteria that would add to the project’s success and reach the desired outcomes. Artificial intelligence has become a crucial method that is used in different fields of engineering; it can be an important source for selecting a contractor. The preliminary literature review determined the importance of sustainable construction and various sustainability indicators. Further, some of the criteria that contractors are selected for were mentioned, along with the different models that were previously used for selection. The main objective of this research is to identify contractors’ characteristics and sustainability objectives. It also aims to develop an AI decision support model to predict the probability of achieving sustainability objectives based on contractors’ characteristics. Twenty characteristics were determined using literature review that were used as inputs for the AI model. A survey was then conducted to collect data for the model to be used for training. The results showed that the AI model is accurate and precise due to having an MSE close to zero and an overall R value above 0.8. The knowledge concerned with selecting a contractor that highlights sustainability objectives has revealed a huge gap in the integration of AI. Thus, this thesis contributes to enhancing the sustainable construction field using a decision support model aligning with global efforts towards reaching better goals.