Abstract
Employers in the construction industry mostly deviate from standard contract forms such as FIDIC and NEC, by introducing alterations to the contract conditions that shift a great portion of risks from the client to the contractor. Originally, these risks were distributed more equitably between all contracting parties in the standards forms. The imbalances in the contractual conditions create fertile ground for conflicts, and if not resolved, will escalate to disputes during the project execution phase, leading to significant cost overruns and time delays. The contractors, therefore, attempt to restore the original balance through making amendments to the communicated contract, through changing the high-risk contractual clauses and negotiating the proposed amendments with the employer. As such, effective contract management at early project stage is crucial for project’s success, therefore, this work developed a hybrid NLP and semantic analysis comparative framework, through integrating pre-trained machine learning models with pattern-matching techniques to support the contractors during tendering stage. The developed framework is designed to compare the two versions of clauses, the employer’s original and the contractor’s amended version, to quantify the extent of risk reallocation back to the employer through the clause’s revisions. The model was developed and implemented on dataset including a total of 704 clauses pairs, originally collected from a contracting company projects executed from the period of 2020-2025, and the findings revealed that, for the analyzed data, the contractor succeeded in reducing his exposure to risk by approximately 43% through the amendments made. This developed framework offers a practical decision-support tool for the contractor’s decision makers during early negotiation phase, prior to contract signature to promote a more balanced contractual outcomes and eventually reduce the potential of future disputes.
School
School of Sciences and Engineering
Department
Construction Engineering Department
Degree Name
MS in Construction Engineering
Graduation Date
Fall 2-15-2026
Submission Date
1-26-2026
First Advisor
May Haggag
Committee Member 1
Samer Ezeldin
Committee Member 2
Hesham Osman
Extent
73 p.
Document Type
Master's Thesis
Institutional Review Board (IRB) Approval
Not necessary for this item
Disclosure of AI Use
Thesis editing and/or reviewing; Code/algorithm generation and/or validation; Data/results generation and/or analysis
Recommended Citation
APA Citation
elshamy, H. M.
(2026).NLP-Based Comparative Assessment of Clauses Quantifying Risk Transfer in Construction Contracts [Master's Thesis, the American University in Cairo]. AUC Knowledge Fountain.
https://fount.aucegypt.edu/etds/2691
MLA Citation
elshamy, Hoda M.. NLP-Based Comparative Assessment of Clauses Quantifying Risk Transfer in Construction Contracts. 2026. American University in Cairo, Master's Thesis. AUC Knowledge Fountain.
https://fount.aucegypt.edu/etds/2691